Mom and pop coffee shop

profilescoobie
SpatialBusiness-COMPLETE-singlespaced-4-19-2022.pdf

Copyright (c) by Esri Press

Do not distribute except to students using the book in a course.

Table of Contents

Introduction 1

Acknowledgements 6

PART I

Chapter 1. Fundamentals of Location Value 8

Chapter 2. Fundamentals of Spatial Technology 23

Chapter 3. Fundamentals of Location Analytics 43

PART II

Chapter 4. Growing Markets and Customers 63

Chapter 5. Operating the Enterprise 87

Chapter 6. Managing Business Risk and Increasing Resilience 106

Chapter 7. Enhancing Corporate Social Responsibility 120

PART III

Chapter 8. Business Management and Leadership 138

Chapter 9. Strategies and Competitiveness 156

Chapter 10. Themes and Implications for Practice 173

References 191

Introduction

Technology and Location A quarter-century ago, Frances Cairncross (1997) proclaimed the “Death of Distance”

and a future not bound by location but connected via the electronic revolution. In the ensuing decades, it has undoubtedly been the case that individual lifestyles, the economy, and the world have been transformed by ongoing digital transformations.

But, alas, 25 years later, location is not dead, but deeply intertwined with technology. We live in a global economy, but that economy varies widely by region and location. We live in a high-tech world that allows for unparalleled virtual connections, and yet these high-tech companies tend to cluster in certain regions of the world. We live in a world where shopping can be done entirely online, but these products are sourced through intricate supply chains that deliver the product to one’s doorstep.

Location intelligence is embedded in these contemporary dynamics—that is, businesses need to know where to source, where to operate, where to market, where to grow, and so forth. This book is intended to inform business professionals as well as business students about this new world of location intelligence and how to utilize this intelligence to achieve business success. It also aims to inform geographic information system (GIS) professionals and students about how location analytics can be considered and utilized within business functions and strategies. Indeed, the book unites these domains (business, GIS) into a sphere we call Spatial Business.

To support business progress in this expanding space, the geospatial industry is growing in its capacity to support location analytics, GIS, web and cloud-based processing and display, satellite and drone imagery, LIDAR scanning, and navigation and indoor positioning tools. The total size of the geospatial industry is estimated to be $439 billion (US) by 2024 and at a compound annual growth rate of 13.8% (GMC 2019).

With this level of location digitization and growth, location intelligence has become foundational to business in its marketing, operations, services, risk management, deployment of assets, and many other functions. Through location analytics and location intelligence, a firm can leverage location information to make better-informed decisions and ultimately create value to the business and often to society as well. There are numerous examples of companies that have successfully built-up location analytics capacity and have been able to use the ensuing insights to better serve consumers, operate more efficiently, and achieve competitive advantage. What has been needed is an integrated perspective on these developments and that is the aim of this book.

Spatial Business Organization This book seeks to provide a contemporary foundation for understanding the business

and locational knowledge base to solve spatial problems, support location-based decision-

1

making, and create location value. Our approach is to do so can be seen in Figure 1, which provides an overview of the book’s organization and key concepts. The opening segment (Chapters 1-4) introduces spatial business foundations. Following these foundations, the book dives deeper (Chapters 4-7) into achieving business and social value in four areas (growing markets and customers, managing the organization, managing risk, and resilience, corporate social responsibility). The book then turns (Chapters 8-9) to the management and strategy elements aimed toward spatial excellence. The book concludes (Chapter 10) with a summary of key themes and a set of implications for practice for each of the themes. What follows is a brief preview of key concepts, applications, and company cases that are examined in the book.

Figure 1. Organization of Spatial Business Book, showing how each of the ten chapters fit within three sections

(Source: Author)

Spatial Business Fundamentals Spatial business refers to concepts, techniques, and actions that enhance the use of

locational insights to achieve business and broader societal goals. Spatial Business Fundamentals (Part I) begins by considering the fundamental principles of locational value and how understanding location value chains can inform various business functions such as marketing, operations, and supply chains. Chapter 1 also outlines levels of a company’s spatial maturity as well as the process of gaining maturity. The Shopping Center Group is provided as an example of a company with high spatial maturity and strategic use of location analytics.

These business and locational concepts provide an underpinning for describing the Spatial Business Architecture, which is outlined in Chapter 2. The architecture begins with the business goals and needs, then addresses business users and stakeholders who have responsibilities for addressing these business goals and using location analytics to do so. The

2

architecture continues with a series of location analytics tools to be applied to business areas, tools that depend on various forms of location data. Supporting all of these functions are the various platforms that host spatial business processes, such as the cloud, the enterprise, or mobile services. The final component is the net consequence in terms of location intelligence that can be used to provide business insights, inform decisions, and have an impact on business performance. Companies such as Zonda, OverIt, and Walgreens are described as examples of effective architectural deployments.

Location Analytics lies at the heart of the Spatial Business Architecture. Chapter 3 provides a deeper presentation of the use for descriptive, predictive, and prescriptive analyses. Descriptive location analytics provide exploratory spatial analysis of business patterns as well as visualization of patterns. Predictive location analytics encompasses spatial statistics to detect and predict business patterns, clusters, and hotspots. Prescriptive location analytics are often the most complex and can include spatial forecasting, space-time analysis, and GeoAI (geographic artificial intelligence). Examples of business use of these analytics are provided throughout Part II of the book.

Achieving Business and Societal Value Building on these Spatial Business Fundamentals, Part II explores the use of spatial

analytics across the business goals, featuring growing markets and customers operating the enterprise, and managing risk and resilience. It also considers the role of spatial business applications to understand and track a company’s social responsibility or what has been termed the “new purpose of the business”.

The role of location intelligence is evolving rapidly as geo-marketing is leveraged by organizations to generate deep locational insights about customers and markets. Chapter 6 analyzes the role of location analytics in market and industry cluster analysis to identify business opportunities, determine consumer preferences and buying patterns with customer segmentation, scrutinize geotagged social media streams to examine patterns and relationships between consumer sentiment and actual sales, and determine best locations for new facilities. The chapter also discusses the linkage of location analytics with the “7 Ps” of marketing. Acorn, Fresh Direct, Heineken, and Oxxo are provided as examples of location analytics used for growing markets and customers.

Effective management of business operations is a highly varied, process-oriented part of the organization and its functioning is critical to achieving business goals. Chapter 7 outlines how location analytics spans to include situational awareness to facilities, ensuring business and service continuity, and achieving efficiencies in supply chains and logistics. Chapter 8 focuses on risk and resiliency. Using location analytics, companies can gain a new way to measure and initiate operational actions action ahead of time, gaining the advantage of being proactive in managing risk. With improved visibility via dashboards, there is the capacity to quickly adjust to events such as natural disasters and COVID-19 related closures. Cisco, CSX Rail, and Travelers Insurance are provided as examples of location analytics using operational and risk management.

3

Corporate Social Responsibility (CSR) calls for a company to be socially accountable in ways that go beyond making a profit. The company takes a broader view of its goals, thinking not only of its stockholders, but also of the benefits to its employees, customers, community, the environment, and society as a whole. This expansive role of the business to address social, racial, economic, health, and educational inequities has been heightened worldwide by the COVID-19 pandemic. As corporate leaders navigate their businesses through increasingly uncertain business and geopolitical environments in the post-COVID world and are pressured to achieve growth, they are also being called to shape their organizations' role in confronting and addressing these items. Chapter 7 outlines “shared value” strategies and actions by companies to use location analytics to address issues such as climate change impacts, sustainable supply chains, UN (2030) sustainable goals, and economic advancement of underserved communities. Marx and Spenser, Nespresso, Natura, AT&T, and JP Morgan are provided as examples of how location analytics can contribute to these important societal goals.

Toward Spatial Excellence A driving theme is that location analytics should not be considered an isolated GIS

undertaking, but rather an integral analytical function for creating business success. Given the importance of management and senior leadership in an enterprise’s spatial transformation, Part III details the application of management principles allied with spatial business strategies and building the location analytics workforce to accomplish this transformation. It concludes with implications for practice that serve as action items for those engaged in spatial business.

Chapter 8 outlines critical dimensions of spatial leadership needed to achieve spatial maturity, where location analytics become intertwined with business strategies and business gains. Core activities that are discussed include demonstrating the value of location analytics to key business goals, championing spatial initiatives, and developing the workforce capacity to achieve these goals. Companies such as CoServ Electric, and British Petroleum (bp) are provided as examples of effective spatial management and leadership.

Chapter 9 moves from leadership and management into strategic and competitive actions. Geospatial strategic planning is characterized as having both external and internal elements. The external element focuses on how location analytics can be used to strengthen the firm’s competitive position or modify forces affecting competition, such as customer relationships or new products. Internal planning emphasizes improving the firm’s own geospatial infrastructure and processes. The internal element focuses on the alignment with business needs, technological capacity, and human resource requirements to achieve desired location and business value. These strategic actions are demonstrated by both a large company example (Kentucky Fried Chicken) and a small company (RapidSOS) example.

The concluding chapter (10) moves to Implications for Practice from all that has been presented in the book. This discussion is centered around 10 themes that can guide spatial business actions:

1. Identify and Enhance Location Value Chain

2. Enable Spatial Maturity Pathway

4

3. Match Analytical Approach to the Business Needs

4. Build a Spatial Business Architecture

5. Use Market and Customer Intelligence to Drive Business Growth

6. Measure, Manage, and Monitor the Operation

7. Mitigate the Risk and Drive the Resiliency

8. Enhance Corporate Social Responsibility

9. Inspire Management to Capture Vision and Deliver Impacts

10. Solidify Spatial Leadership for Sustainable Advantage

A set of Implications for Practice are provided as specific steps that be taken to achieve an effective Spatial Business strategy and operation that will contribute to business success in today’s competitive and complex environment.

We hope you will find the following chapters to be informative about principles, concepts, and practices of Spatial Business and will inspire their use for business and societal gain. For leaders, it represents an important opportunity to leverage location intelligence for strategic leadership and competitive gain. For analysts, it is an exciting opportunity to deploy innovative location technologies and applications that can have a demonstrable impact. For students, it is a growing field of study and profession that complements and widens traditional business education and professions. For all, spatial business has elements that broaden the space of inquiry to consider related societal outcomes, challenges, and benefits for communities and the world.

5

Acknowledgments

The publication of Spatial Business represents a product of four years of work undertaken by the authors, who each made an equal contribution to the book. This book is a part of a Spatial Business Initiative conducted in cooperation with Esri. The partnership has been invaluable in providing a forum for investigating trends and developments in business location analytics. We are deeply grateful to Jack and Laura Dangermond for their support of this initiative. Special thanks are due to Cindy Elliott for her strong partnership as the designated lead at Esri as well as her insightful reviews of draft chapters. Nikki Paripovich Stifle and Karisa Schroeder provided expert input on several chapters, especially Chapters 2 and 10. We are also appreciative of the guidance provided by so many at Esri Press, especially Catherine Ortiz, Stacy Krieg, Dave Boyles (in the early stages of the project), Alycia Tornetta, and Jenefer Shute. The book has also benefited from our participation in Harvard Business School’s Microeconomics of Competition (MOC) network. Several key concepts in the book (e.g., location value chain, cluster mapping, and shared value) were inspired by materials, presentations, and collaborations made possible through the network.

During the book project, various forms of research and outreach were conducted to inform the concepts, methods, and cases outlined in the book. The most intensive of those efforts was the case study research for which several private sector leaders in charge of geospatial strategy and location intelligence provided keen insights about their respective organizations. We want to thank Gregg Katz, formerly of The Shopping Center Group, Ben Farster of Walgreens, Enrique Ernesto Espinosa Pérez of OXXO, Kurt Towler of Sulphur Springs Valley Electric Cooperative, Joe Holubar of Travelers Insurance, Brian Boulmay, formerly of British Petroleum, Lawrence Joseph of KFC, Martin Minnoni of RapidSOS, and Andy Reid of Zonda. Each of them agreed to be interviewed as part of our spatial business research, shared nuanced insights on how location analytics is shaping competitiveness and strategies in their respective organizations, and were generous with their geospatial industry perspectives. Esri's Cindy Elliott, Helen Thompson, and Bill Meehan were instrumental in connecting us to several of these experts, and their roles are gratefully acknowledged.

We convey our thanks to several former and present graduate students who provided research assistance at various stages of the project. Anuradha Diekmann, Lauren Salazar, Simisoluwa Ogunleye, Jahanzeb Khan, and Burt Minjares helped to record and transcribe case study interviews, and to collect relevant secondary information from various companies, businesses, academic journals, and business information databases. They distilled key findings and corroborated the findings with the research team members and knowledgeable business contacts, and also helped us procure permissions for the project's artwork. In addition to these talented students, we would also like to thank Kian Nahavandi in our school for her valuable administrative support for the book.

Finally, the University of Redlands provided a hospitable environment for the project. We are grateful to colleagues at the University for their steadfast encouragement and support for this project.

6

SPATIAL BUSINESS: COMPETING AND LEADING WITH LOCATION ANALYTICS

PART I

7

CHAPTER 1

Fundamentals of Location Value

Introduction

Creating Value If we begin with the premise that the purpose of a business is to create value, how do

we identify specific value? Focusing on the private sector, this value is typically revealed in products and services that are successful in the marketplace. Technology companies provide products that are purchased, real estate companies provide homes and office buildings that are purchased or leased, consultants provide advisory services that are procured. Every sector of industry, including government and nonprofits, has a range of specific value that it creates.

From a competitive perspective, this value is framed within the context of a company’s unique “value proposition” to its customers. Anderson, Narros, and Rossum (2006) identified three types of value proposition: all benefits, comparative advantage, and resonating focus. An all-benefits value proposition represents the comprehensive set of customer benefits a company provides, while a comparative advantage value proposition highlights its value relative to the competition. A resonating value proposition—considered the gold standard of value propositions—identifies the key points of difference that will deliver the most compelling value to the customer.

The challenge and opportunity of location analytics is to provide business insight into how location affects these value propositions, taking into account a host of geographic, economic, technological, environmental and societal actors.

Sustainable Value While many companies rightly focus on their value proposition to customers, broader

value considerations affect their business activities and decisions. In the five decades since economist Milton Friedman famously proclaimed that the sole responsibility of business is to make a profit, there has been a growing recognition that the purpose of a company entails yet transcends its profit-making capacity. On August 22, 2019, in recognition of this expanded view of the role of business in society, the prestigious US Business Roundtable announced a revised articulation of the purpose of a business. (Business Roundtable 2019) This broader perspective, backed by 181 of the top US companies, includes the following dimensions: delivering value to customers, investing in employees, dealing fairly and ethically with suppliers, supporting communities, embracing sustainable business practices, generating long-term value for shareholders, and effective engagement with shareholders. As Darren Walker, President of the Ford Foundation observed at the time of the announcement, “This is tremendous news because it is more critical than ever that businesses in the 21st century are focused on generating long- term value for all stakeholders and addressing the challenges we face, which will result in shared prosperity and sustainability for both business and society” (Business Roundtable, 2019).

8

These developments are often framed within the context of corporate social responsibility (CSR), and more recently Environment, Social, and Governance (ESG) factors. KPMG has conducted an annual survey since 1993 on global corporate CSR/ESG activities and reporting. At the time of the 1993 survey only 12% of the (N100) top companies in surveyed companies were reporting on their CSR-ESG activities. As of 2020, this reporting had grown to 85%. Moreover, the growth in the top global corporations (G250) which they started surveying in 1995) as gown to 90% (see figure 1.1) (KPMG 2020). Companies are clearly seeing the connection between their actions and the surrounding world, and the need to track and address societal and environmental factors that could inhibit their success. For example, that same 2020 survey found that top global (G250) reporting on the threat of global climate change as a financial risk had grown from had grown dramatically for both groups, with 43% of top global companies (G250) noting the risk and 53% of top national companies(N100) noting this financial risk.

Figure 1.1 Growth in ESG Reporting by Top National and Global Companies (trends for top national and global companies are displayed in dark blue and light blue lines respectively)

(Source: KPMG)

The COVID-19 pandemic has only served to intensify the interlinkages between companies and societal conditions. During the pandemic business have had to radically change employee work patterns and relationships with customers and do their part to safeguard to health and safety of all of those within their business ecosystem--all this amidst dramatic economic and employment contractions. It has become clear that the health and safety of employees is not only of great consequence when they are “at work” but depends upon the conditions of the environments and communities they live in and travel to.

9

Turning to the focus of this book, it is also the case that business location analytics has a role to play in advancing this broad purpose of business in delivering value to customers, communities, and the global environment. Such as role can be best introduced by considering the spatial decision cycle that enhances business value.

Spatial Decision Cycle Given these various dimensions of value (ranging from a product to a societal impacts), how can one start to spatially think about enhancing such value through location analytics.? It is useful to consider a cycle of four elements in a spatial decision process: value, spatial thinking, location analytics, and data (see Figure 1.2). The cycle begins with understanding the value created by a company’s products and services. It then considers the spatial dimension of the value created, followed by the appropriate location analytics suggested by this spatial thinking. The cycle then turns to the data requirement for achieving the desired location-analytics insights and concludes with the value added by these insights for business priorities.

Figure 1.2 Spatial Decision Cycle, showing linkages between its key elements: Value, Spatial Thinking, Location Analytics, and Data

(Source: Author)

10

Element 1: Value (proposition)

From a strategic perspective, spatial decision-making begins with business goals to deliver a company’s “value proposition” through market and customer growth, achieving competitive advantage in offerings, driving operational efficiencies, and managing risk and regulatory compliance. Taking into account the dimensions outlined by the Business Roundtable, these goals can also include upgrading employee skills, ensuring effective and sustainable supply chains, supporting local communities, and improving environmental conditions.

For example, the case of gourmet coffee maker Nespresso illustrates a company strategy that embraces these objectives and embraces location analytics as a means to achieve them. As the company notes in its Business Principles, their value proposition is to "promise consumers the finest coffee in the world that preserves the best of our world" (Nespresso, 2021). Similar to the Business Roundtable new statement of purpose, Nespresso notes that " If we are to be successful – not only as a business, but in delivering on this promise – we know we must earn the trust and respect of our people, our customers, our suppliers and wider society." As well be outlined in the Nespresso Case Study (in Chapter 8), a key aspect to delivering on this promise is the use of location analytics, to monitor and manage achieving a variety of business, environmental and community goals under their "Positive Cup Framework" (Nespresso, 2021). Of course, not every company operates in the same context as Nespresso, but a key value proposition can usually be discerned, with priorities that set the stage for spatial thinking.

Element 2: Spatial thinking

This second stage of the cycle focuses on utilizing spatial thinking to translate business objectives into spatial considerations. Spatial thinking is considered a form of intelligence, along with other forms of intelligence such as logical and interpersonal (Gardner, 2006). The National Research Council (2006) noted that there are three components to spatial thinking: spatial attributes, representations, and reasoning. In spatial business, spatial attributes refers to the ways to measure and assess location dynamics such as in trade areas, supply-chain transportation and so forth. Representations include various means of rendering spatial dynamics, such as customer cluster maps, business space-time trendlines, and supply network visualizations.

Perhaps the most important component is spatial reasoning. In spatial business, this calls for constructing a line of inquiry that reveals the influence of locational factors on business success. For example, a hospital can examine its supplier network to determine where in the supply chain interruptions are occurring. A retail company can examine trends in sales across different customer markets to determine where new stores should be opened because such locales have a strong presence of desired customer profiles.

A classic example of strategic spatial reasoning is the case of the investment company Edward Jones. Edward Jones started as a “small-town” investment firm in Missouri. The company viewed its comparative value proposition as providing a single investment service to more rural

11

communities, compared to Merrill Lynch, which provided full-service portfolios in large metropolitan areas (Collins & Rukstad, 2008). In the early 1980s, Edward Jones conducted a series of analyses and consultations and discovered that its resonating value proposition was that it offered a highly personalized investment service to those individual customers who wanted to delegate investment decisions. It further discovered that it could competitively offer these services in select rural and select metropolitan locales where such customer profiles were strongly represented. The company then proceeded to operationalize the new market mix. This spatial reasoning resulted in the rapid growth of Edward Jones from 400 to 1,000 locations in a seven-year period and remains its driving focus today (Edward Jones, 2020).

Element 3: Location analytics

Clear spatial thinking drives the choice of location analytics. If a business is mostly interested in a general understanding of spatial trends in customers, assets, suppliers, and so forth, then descriptive analysis can provide situational awareness through maps and infographics. If a business desires to carry the spatial analysis further, to understand how spatial insights can help achieve business value priorities, then explanatory analysis can be conducted to help explain dynamics such as why growth did or did not occur, or why certain sites or locations were or were not successful. If a business wants a predictive analysis of the likely success of a service, product, or location, it can conduct predictive spatial analysis. And if a business wants to know where to locate new locations or serve new markets, it can conduct prescriptive analysis. These spatial analysis approaches are reviewed in detail in Chapter 3.

Many industries are advancing their analytical capacities to move from descriptive to predictive and prescriptive analytics. As one example, the insurance industry is rapidly evolving to adjust to the more extreme climate conditions brought on by climate change and other socio- demographic and economic changes. Companies such as Travelers Insurance now employ a full range of location analytics to assist a range of business-critical functions (Travelers Insurance, 2021). These include predicting the location of natural disasters (for underwriting purposes), analyzing damage locations (for claim purposes), and identifying high priority locational impacts (for disaster response). These tools have been used with great success for recent hurricanes on the east coast and wildfires on the west coast (Claims Journal, 2019).

Element 4: Data

The fourth element in the spatial decision cycle is data. As the expression goes, “You are only as good as your data.” A business may have a driving need to use location analytics to enhance its success but will be hampered without the appropriate data. Typical data types include sales, profit, customer, cost, asset, and network data. In addition to this proprietary data, numerous governmental and commercial datasets can inform location analysis—for example, trade-area analysis, business transactions, supply chain network data and social, economic and environmental trends. Companies are leaning into digital transformation and, as part of that, aligning their various business intelligence enterprises, which includes enhanced interoperability of these data sources.

12

In addition to the need for “location stamped” data, three other data issues that deserve attention are the level of geographic specificity, the availability and consistency of data over time, and the policies surrounding data use. Regarding the first, the greater the level of granularity the better the analysis, although many publicly available data sets will limit the granularity for reasons of privacy and anonymity. Regarding the second, temporal data is critical for looking at spatial changes over time, such as the growth or decline of customers, sales, inventory, etc. And third, various policies can affect the use of data within a company or its ability to share the results of the data. For some industries such as healthcare, these privacy conditions are well established (e.g. HIPAA), while for other industries such as retail there are emerging privacy issues around location-based services. Element 5: Value (added) The cycle concludes with the consequence of location analysis in contributing to business success. This may include value added to business priorities such as driving growth, improving operations, managing risk, and ensuring regulatory compliance. To the extent possible, this contribution should be documented in terms of the type and amount of value contributed and the stakeholders who received this value. In determining this value, there are a number of dimensions to consider: depth, breadth, use, internal stakeholders, external stakeholders, and financial contribution, as summarized in Table 1.1. Beginning with depth, this value type refers to the value delivered to a specific business function such as marketing or operations (discussed below). Breadth refers to the value delivered across business functions, as the organization increases its spatial maturity. Then, there are different use values. This can include the value that location analytics has in informing stakeholders (e.g. situational awareness), decision-making, contributing to business goals, and, ultimately, contributing to the business mission. Like beauty, value is the eye of the beholder. There are internal stakeholders who perceive value, ranging from employees carrying out specific organizational functions to the ladder of middle, senior, and executive managers and leaders. There are external stakeholders who perceive value, including customers, partners, suppliers, distributors, and the public. Finally, there is the traditional estimation of value, often framed within the context of “return on investment.” This can be in the form of a formal/quantitative return on investment or a more qualitive summarization of the value elements noted above. The former can be particularly appropriate when the costs and benefits can be easily parsed. Most importantly, this is but one iteration of the spatial decision cycle. The cycle should be considered ongoing and integrated into decisions regarding key business priorities. A case example of this tight integration is The Shopping Center Group, which we consider next.

Case Example: The Shopping Center Group The Shopping Center Group (TSCG) is a leading national retail-only real estate service

provider in the United States. It has 20 offices in the United States, 215 team members, and 28 GIS specialists (known as “mappers”). Over the last decade the company has come to tightly

13

integrate the use of business-focused location analytics that deliver business value to its customers and its organization (The Shopping Center Group, 2021)

TSCG has four main service lines: tenant representations, project leasing, retail property sales, and property management of those retail properties. As TSG's Chief Strategy Officer Greg Katz noted, “At the core of everything is GIS research. We consider GIS research to be the heartbeat of the organization. It allows all four of those service lines to tell a story” (ArcWatch, 2017). Each of the four service lines engages in ongoing spatial decision cycles, i.e.: What is the property in question? What are the business objectives for the property? What locational analytics will inform decisions about the property? What data can be applied to this analysis? What recommendations come out of this cycle of analysis?

As such, it is clear that location analytics is providing considerable value to the TSSG. Table 1.1 provides a summary of this value along the dimensions described above. Beginning with value to key business functions, location analytics provides value to the company’s marketing and sales support. This features deep spatial insight into consumer and trade-area markets. For example, an analysis conducted on behalf of Columbus Mall in Georgia combined trade-area, drive-time, GPS, and psychographic analyses to pinpoint key market considerations to guide the selection of potential tenants for that commercial property.

Table 1.1 Value of Location Analytics for The Shopping Center Group (TSCG), with each row showing different dimensions of value and how they are manifest at TSCG

Value Domain The Shopping Center Group

Depth

Value Within Marketing and Sales Support

Deep spatial insight on relative consumer and trade-area markets for commercial properties

Breadth Value Along Key Business Priorities

Drives overall value proposition as an information-focused technology enabled commercial real estate company.

Success of brokers and growth of company

Use

Inform, Decide, Grow, Avoid Used to inform brokers, decide on commercial selections, and avoid mismatches between commercial property types and surrounding markets

Who: Internal

Internal (Analysts, Managers, Sales, Operations, C-suite)

1:4 “mapper” to broker ratio

C-suite vetting and management of commercial properties

14

Who: External

Clients and Partners

Multilayer commercial real estate maps for clients and partners

Results|ROI

Direct input to competitive advantage and growth

Key contributing component to 30% growth of company, and mission as an analytics-focused commercial real estate company

As Table 1.1 shows, location analytics is part of the overall TSSG value proposition as an

information-focused, technology-enabled commercial real estate company, and it spans a wide range of value types. It informs brokers and partners, it aids in their decision-making process, and it helps the company grow while avoiding costly market assumption errors in retail commercial transactions.

These location analytics insights and products are utilized by a wide range of stakeholders. Internally, brokers and other analysts have ready access to the 28 “mappers” who fuel the analysis. This utilization rises to the C-suite level, where every major deal is required to have a location analytics review as part of the vetting process. Given the highly integrated nature of location analytics into the TSSG mission, processes, and product lines, its ROI value is considered within the context of overall corporate success. In this case, company executives consider it to be a key contributor to TSSG’s 30 percent growth and its emergence as a commercial retail and information company.

Location Value Chain The use of location analysis across business dimensions can be considered the “location

value chain.” The concept is a variant of Porter’s seminal “value chain,” which outlines the various business processes that combine to create value in terms of products and services delivered (Porter, 1998). A business’s location value chain captures those business functions that benefit from location analytics and thus contribute to the overall value of the company.

Depending on the business value being pursued, location analytics can be deployed across a range of business functions (see figure 1.3).

15

Figure 1.3 Location Value Chain, comprised of business functions and business needs within each function that prompt the use of location analytics

(Source: Author)

Request this Figure to be re-drawn.

In term of basic business functions, this covers the following:

 Research and Development (R&D), including service and product development, new market development, acquisition due diligence, and location siting.

 Marketing, including market expansion, customer segmentation, customer retention.

 Business Development and Sales, including product roll-out, mergers and acquisitions, sales growth.

 Operations, including asset management, facilities management.

 Site Strategy, including trade-area analysis, competitive analysis, facilities layout.

 Supply Chain, including sourcing, operations, network analysis, tracking, and simulation.

 Risk Management, including risk assessment, management, recovery, and resiliency

 Corporate Social Responsibility, including employee health, social equity, community impacts, and shared value creation.

A variety of studies have documented the range of location analytics use across this value chain. In 2018, the University of Redlands conducted a survey of 200 businesses that had at least initial adoption of location technology to determine patterns of location analytics use. (University of Redlands, 2018.) The survey found that an overwhelming majority (86%) of

16

surveyed businesses report moderate to high use in more than one function (Figure 1.4). Overall, 51% of businesses use GIS in 1–3 functions, 35% use GIS in 4–6 functions, and the remaining 14% use GIS in 7–9 functions. Figure 1.4 shows levels of use 9 major business functions, with GIS usage highest for R&D (58%)., followed by operations (50%), services (48%), IT (48%), sales and business development (47%), and marketing (43%).

Figure 1.4 High and Moderate Use of Location Analytics in Different Business Functions along the Location Value Chain (High Use in Maroon and Moderate use in Blue)

(Source: Author)

In terms of the overarching business motivations for using spatial analysis, 46% of the survey respondents reported moderate to high motivation for improving the competitive posture of the business. This was followed by GIS use to optimize business performance (39%), for effective risk and disaster management (31%), and finally for regulatory compliance (28%).

Looking more closely at customer-centric activities, GIS use is highest for analysis of spatial patterns of customers (46% indicate moderate to high use), yet lowest for tracking and measuring sales activities (29%), pointing to a gap in GIS use for these purposes. In the middle are GIS use for customizing marketing strategies (38%), predicting future customer trends (36%), and optimizing sales territories (31%). With the exception of tracking and measuring sales, GIS use for customer and sales activities seems to decline as the purpose of deriving location intelligence shifts from descriptive to predictive to prescriptive in nature.

Turning to operational activities, GIS use is highest among the following activities: space and location decisions (58% indicate moderate to high use), spatial field data collection (56%), tracking and managing asset allocations (43%), predicting future operational needs (36%), and managing logistics and supply chains (20%). As in customer and sales activities, moderate GIS use surpasses high use for operational activities.

17

Pandemic Influences on Location Value Chain The COVID-19 pandemic of 2020 profoundly influenced the location value chain of many

businesses. With the disruption of operations, location analytics played an important role in assessing the challenges to business continuity, which varied by location. Business had to move quickly to close, modify or continue with operations, depending on a variety of federal, state, and local conditions. The pandemic also had major impacts on business supply chains, most visibly on the healthcare supply chain, where the crisis exposed risks associated with the global, just-in-time systems that had come to dominate the medical device and supplies industry. In addition, businesses needed to have locational information on employees to ensure their safety and to take appropriate action if they or their coworkers were exposed to infection.

As businesses are emerging into a post-pandemic era, new value propositions are emerging that will affect the location value chain of businesses. Within the retail sphere, online- on-ground hybrid models are being extended. McKinsey & Co (2020) reported that many customers have also tried new omnichannel models. For example, buy online, pick up in store (BOPIS) grew 28 percent year- over-year in February,2021 and grocery delivery was up by 57 percent. They further note that many of these new engagement models are here to stay. Consumers report high intention to continue using models such as BOPIS (56 percent) and grocery delivery (45 percent) after the pandemic. As these business models evolve, it will create new opportunities to integrate location analytics into the new-normal of business operations.

Drivers of Spatial Maturity Spatial maturity involves the deepening of location analytics use across the locational

value chain. Looking at the range of use, the University of Redlands survey estimated that roughly 1 out of 5 businesses (22%) use GIS enterprise-wide, spanning multiple departments. At the other end of the spectrum, roughly 1 out of 5 businesses (20%) report GIS usage to be very limited. In the middle, 28% of businesses report their GIS usage to be currently limited but poised to grow soon, while another 25% indicate GIS usage to be moderate and steady. Overall, it is clear that business use of GIS has the potential to grow in the near term. However, charting a pathway for spatial business transformation is essential.

In determining the factors that contributed to achieving spatial maturity, the survey found five that played an influential role. The more advanced, spatially mature companies had: 1) perceived the value of location analytics; 2) a clear and coherent business strategy; 3) C-suite sponsorship and support; 4) availability of best-in-class technology; and 5) clear articulation of Return on Investment (ROI).

Turning to inhibitors of spatial maturity, IDC/Esri Canada identified several factors as “challenges” to achieving deeper spatial maturity. Factors such as cost, culture, appropriate skill set, integration challenges, and data quality issues were identified as inhibitors that could impede the growth and use of location analytics in companies.

Various forecasting reports provide a generally bullish outlook on the future use of location analytics. This is due to at least these eight drivers of location analytics use:

18

1. Growing geospatial ecosystem

2. Deepening use across a range of industry applications and verticals

3. Increasing availability of spatial analytics tools

4. Widening range of location services

5. Integration of location analytics with business intelligence

6. Growing indoor locational analytics

7. Rise of associated advanced technologies

8. Rise in global environmental, societal, and health challenges

Driver 1: Geospatial ecosystem Location analytics is part of a larger geospatial ecosystem that includes Global Navigation Satellite Systems (GNSS), GIS/Spatial Analytics, Earth Observation, Light Detection and Ranging (lidar), Space-Time Visualization, Augmented/Virtual Reality (AR/VR), and Artificial Intelligence (AI). The global geospatial solutions market is projected to reach USD 502.6 billion by 2024 from an estimated USD $239.1 billion in 2019, at a CAGR of 13.2% during the forecast period (Markets and Markets, 2019). The cornerstone of this industry is GNSS, which provides the technological backbone for the industry and accounts for approximately 59% of the total value. This has fueled the growth of GIS/spatial analytics to be the second largest segment of the industry, with growth expected to double by 2022 (GeoBiz 2019). Driver 2: Industry use Consistent with the Redlands survey noted above, other industry outlooks have documented deepening use across a range of industries. For example, Dresner Advisory Location Analytics Survey (2020) found that location analytics was viewed as critical or very important across a range of vertical industries. Ninety-three percent (93%) of survey respondents viewed location analytics as having some importance to their organization, and over 53% noted it was critically or very important to their organizations. In terms of specific vertical markets, the survey found this to be especially true for health care, business services, financial services, consumer services, and manufacturing. Each of these sectors viewed locational analytics as critical or very important to their organizations. Driver 3: Spatial analytics tools Spatial analytics tools continue to broaden in their areas of application and deepen in their capabilities. In terms of broad solution sets, geocoding (and reverse geocoding), reporting and visualization, thematic mapping and analysis, and data integration/ETL are each expected to grow substantially between now and 2024 (Markets and Markets, 2019). The ability to

19

effectively geocode data is particularly helpful in making a range of industry data available for location analytics, such as customer, business, product, and supply chain data. Thematic mapping and analysis growth will continue with the availability of increasingly sophisticated analytics. Reporting and visualization tend to increase demand both within businesses and from their external stakeholders. As the volume and value of data continue to growth, there is a strong need for integration across different systems, including business intelligence (BI) systems. Driver 4: Business intelligence integration Further fueling this growth is the increasing appetite for the business insights provided by location analytics. Dresner Advisory (2020) reports that Executive Management and Operations are expected to experience the highest growth for BI penetration (which includes location analytics) over the next three years, and that “better decision-making” is the primary objective of BI use, followed by related key business areas such as (in descending order) growth in revenues, operational efficiencies, increased competitive advantage, enhanced customer service, and risk management. Increased integration into BI software suites and reports provides a natural path for location analytics to contribute to business growth and competitiveness. Driver 5: Location services The rise of location-based services (LBS) has provided unprecedented opportunities to customize offerings and customer experiences. The related rise of Real-Time Location Systems (RTLS) also provides unprecedented opportunities to track assets, personnel, and products. As an industry, the LBS/RTLS services is expected to grow rapidly (CAGR of 20.1%) to become a $40 billion market by 2024 (Markets and Markets, 2019). GPS-enabled mobile devices have spurred an entirely new dimension to retail marketing and customer services. Driver 6: Indoor location analytics Related to the growth of LBS and RTLS, indoor location analytics is growing rapidly as industries begin to appreciate its value, especially in operational efficiencies and risk management. For example, healthcare is considered a prime use of RTLS, and RTLS use in this sector is expected to grow by CAGR 18% to a $6.84 billion market by 2027. Looking across industries, the COVID- 19 pandemic has heightened the need to track and monitor personnel locations for health, safety, and other risk management measures. LBS/RTLS is also disrupting traditional distribution center workflows and processes, as evidenced by the innovative distribution center techniques deployed by such retail giants as Amazon, Target, and Walmart. As the penetration of the Internet of Things (IoT) and other location-based technologies deepens, new applications will emerge. At the same time, privacy and security threats will condition the extent to which such solutions are deployed, and this constraint could vary widely across regions and cultures.

20

Driver 7: Advanced technologies A range of advanced technologies will provide numerous opportunities to extend and deepen the use of location analytics in business and contribute to the ongoing digital transformation of business. The IoT has already led to a pronounced rise in indoor GIS, particularly in the retail sector. The recent COVID-19 pandemic has heightened the need for and use of georeferenced IoT devices to track supply chains, analyze human travel patterns, and monitor health conditions. Advances in AI are enabling machine and deep learning across a range of business domains, such as analyzing and predicting customer buying patterns, operational improvements, and threats to business continuity. These and other AI applications comprise what is known as “GeoAI”. Of course, IoT, AI, and related technological advances would not be feasible without continued advances in Big Data platforms and applications. Specific to locational analytics, the geospatial industry is moving to highly cloud-based and “Web-GIS” platforms with integration to big datasets and location analytics applications. Driver 8: Global environmental, societal, and health challenges The eighth driver is the changing environmental, health, and societal context under which businesses operate. In terms of the environment, the private sector is increasingly treating climate change as a contextual condition that can have a significant impact on business success. This impact is across the location value chain, affecting companies' ability to source sustainable suppliers and retain resilient supply chains through increasing volatile climate conditions. Societal issues range from racial equity, income disparities, broadband access and other factors that can affect a company’s performance and success in different communities and region. The COVID-19 pandemic has raised awareness of the massive impact that such an outbreak can have on all aspects of the economy, and the need build resiliency into supply chains and operations. At the macro level, Porter and Kramer have emphasized the concept of “creating shared value—pursuing financial success in a way that also yields societal benefits” (Porter and Kramer, 2016). They note: “Collective impact is based on the idea that social problems arise from and persist because of a complex combination of actions and omissions by players in all sectors—and therefore can be solved only by the coordinated efforts of those players, from businesses to government agencies, charitable organizations, and members of affected populations.” There are many examples of such shared value initiatives which rely on locational information. Examples include locationally targeted partnerships for economic development, training suppliers on sustainable practices relative to their local community, and public-private collaborations on relief during the COVD-19 pandemic.

Global Location Analytics Outlook These eight drivers, as well as other influences, are expected to result in considerable

growth in location analytics across the globe. Location analytics as an industry is expected to rise from $10.6 billion (2019) to $22.8 billion (2024), representing 16.6% CAGR (Markets and

21

Markets 2019). This growth is expected to be worldwide. Currently, the major regional markets are North America (34.8%), Europe (28%), and Asia Pacific (20.4%). Looking to 2024, these will continue to be major markets with the largest growth (17.1% CAGR) expected in the Asia Pacific region. The Middle East and Latin America are expected to remain smaller regional markets, though each region is expected to have noteworthy growth (Markets and Markets 2019).

Looking more closely at the types of business use that will provide this growth (Figure 1.5), the strongest growth is projected in supply chain planning and optimization (17.3%), sales and marketing optimization (17.1%), customer experience management (16.7%), remote monitoring (16.3%), and emergency response management (16.3%).

Figure 1.5 Global Outlook for Location Analytics across different business functions, with the base year (2017) in light blue and the forecasted year (2024) in dark blue

(Source: Sreedhar and Bhatnagar, 2019)

In summary, the “Location Value” foundation outlined in this chapter serves as an organizing set of concepts, principles, and examples for understanding the business location value of any given company, a value that includes organizational success within a societal context. As organizations broaden and deepen their use of location analytics to achieve business priorities and goals, location analytics can become more integral to a company’s mission. Various market forecasts suggest that such deepening use will indeed be the case around the globe and across a wide range of industries and business functions. This growth, in turn, contributes to and benefits from the need to integrate across business intelligence systems, geospatial platforms, and various new technological systems and products as they arise. These technology issues are taken up next in terms of a “Spatial Business Architecture.”

22

CHAPTER 2

Fundamentals of Spatial Technology

Introduction Achieving location intelligence depends on a process that delivers valuable insights to

business users. Though other technologies may specialize in informing “who,” “what,” and “why,” location intelligence makes information actionable by adding in the element of “where.” Organizations increasingly focus on this location intelligence to drive strategic decisions on all levels of the enterprise.

This chapter aims to define the different components of the technological backbone of location analytics by outlining the six essential elements of a “Spatial Business Architecture” -- Business Goals and Needs, Human Talent, Location Analytics, Data, Platforms, and Location Intelligence (see Figure 2.1).

23

Figure 2.1 The components of spatial business architecture are shown, including business goals, human talent, location analytics, data, platforms, and location intelligence

(Source: Author)

Request this Figure to be re-drawn.

The architecture begins with the business goals and needs, then the human talent needed to accomplish these business goals. The architecture continues with a series of location analytics tools and the data upon which the analysis is performed. Underlying all of these functions are the various platforms that host spatial business processes, such as the cloud, the enterprise, or portals. The final component is the net consequence in terms of location intelligence that provides insights, informs decisions, and impacts business performance. The following sections describes each of these Spatial Business Architecture elements and the examples are taken from case studies throughout the book.

Element 1: Business Goals and Needs

Location analytics are performed to enhance business value, in terms of business goals and needs associated with achieving this value. Business goals that location analytics can contribute to are diverse, and they can be focused anywhere across the location value chain. The architecture highlights three goals that are generally central to any business: drive sustainable growth, strengthen operational effectiveness, and enhance business resilience.

There are many factors associated with sustainable business growth: having valued products and services, attracting and retaining customers, operating as a socially and environmental responsible manner to name a few. The appropriate location analytics tools can contribute to business and broader goals. For example, John Deere is a leader in location-based precision farming that is responsive to climate changes and enables more sustainable farming practices.

A second goal is to strengthen operational effectiveness. There are several factors associated with achieving this goal, such as operational performance, supply chain management, and logistics. For example, Cisco uses a customer location dashboard to ensure timely service support.

A third goal is to enhance business resilience. Factors associated with this central goal include risk management, risk recovery, and risk reliance. This can be seen in the locational risk and response algorithms developed by Travelers Insurance, to predict hurricane directions and risk, and by Mid-South Synergies tree analysis to protect against power outages due to fallen trees.

Element 2: Human Talent

The success of spatial business depends on having the human talent to accomplish the strategic and tactical actions needed to achieve business gains. Employees and stakeholders act in varying capacities at each stage of the spatial business cycle. To identify business needs, a variety of people contribute -- business internal users of location-based systems, executives, managers, spatial analysts and technical specialists. Trained developers design and build location analytics, consulting with managers. Location-driven decision making is done by

24

managers, senior executives, and business unit managers. Staff at all levels may use the applications, such as in sales, operations management, and service data collection.

This list is not exhaustive. The point is that the successful execution of a spatial business strategy depends on people at varied levels in the organizational hierarchy, with different skill sets, and disparate levels of technical and business knowledge. Walgreens is an example of how technical and business unit managers collaborated to swiftly roll out Covid-19 pandemic testing application as well advance their overall spatial technology platform.

Element 3: Location Analytics and Applications

Location analytics may be divided into descriptive, predictive, and prescriptive tools. Descriptive analytics describes locational phenomena. For instance, a map of electric car charging locations with car capacity and charging intensity is descriptive. Predictive location analytics predicts future business phenomena based on forecasting of geo-referenced data and space-time prediction techniques. An example would be to predict in one year’s time the locations of customers placing e-commerce orders s based on trend analysis or geographically- weighted statistical regression models. Prescriptive location analytics seeks the provide optimal locational and network arrangements to achieve a business objective. An example UPS has developed a routing tool for optimized routing of deliveries that also produces considerable fuel savings.

Underlying location analytics are algorithmic techniques such as overlay, buffers, drive-time analysis and sophisticated methods such as visualization, decision trees, text analysis, data mining, spatial cluster methodologies, spatial statistics, location-allocation modeling, network analysis, artificial intelligence, and machine learning.

Spatial Visualization and Hotspots At a basic level, mapping is a form of visualization that simplifies understanding of geographic differences. For instance, the thematic map of the density of Airbnb properties in New York City (Fig 2.2) visualizes the geography of the city and the density levels of Airbnb properties indicating the highest levels in lower Manhattan and north sections of Brooklyn. This visual utilizes geography, colors, labeling, and scale to create impact and tell a story that the equivalent table of data would not do easily.

25

Figure 2.2 A thematic map exhibits the zip code areas of New York City by categories of Airbnb property densities, with the highest densities in lower Manhattan and central Brooklyn

(Source: Sarkar, Koohikamali, and Pick, 2019)

Visualization offers a way to simplify massive data and its processed outputs to highlight the important overall outcomes and bring out important details which may have been overlooked in a tabular format. Visualization can help users to identify patterns, such as density. For instance, perhaps an organization's marketing team is putting together an advertising campaign targeted at tourists visiting the Manhattan area. Rather than canvas the entire metropolitan area, the marketing team can add value to the organization by spatially thinking about the solution. In running location analytics, the team can discover density levels of rental properties within Manhattan by visualizing location-enabled data. In reviewing the map in Figure 2.2, it is not difficult to see the most effective place to enact the advertising campaign.

Cluster and hotspot analytics are most advanced form of pattern detection. For example, cluster mapping can be done on industry locations to identify geographic concentrations of specific industry clusters, such a medical device industry cluster. Hot spot analysis can be done to track geographically concentrated events, such as has been done extensively during the Covid pandemic.

Indoor Analytics Indoor Analytics is a rapidly growing form of location analytics. The segment is estimated to grow from $3.9 billion in 2019 to $8.4 billion by 2024, at a CAGR of 16.5% (Sreedhar &

26

Bhatnagar, 2019). Indeed, maximizing the value of the indoor built environment is increasingly a strategic differentiator for many businesses. Descriptive location analytics can track the movement of people, goods, and assets. Prescriptive location analytics and be applied to improve productivity and throughput of an indoor space, and to provide navigation and routing services, saving time, effort, and money.

Consider the challenges inside a large warehouse to understand the spatial location of inventory, locate the movement of workers, optimize the movement of pallets of goods, coordinate arrival and departure of trucks, and adhere to safety regulations and social distancing from covid-19. This indoor environment can be managed by a cloud-driven warehouse spatial intelligence system (WSI) (Zlatanova and Isikdag, 2017). Data is input from a combination of precise GIS 3D location of assets and people, RFID tags on inventory, and high definition image and video feeds. The data goes beyond simple RFID-tagging of inventory to identify the space-time dynamic movement of vehicles, people, and inventory (Pavate, 2021). Such a system allows for numerous location analyses. For instance, the location of warehouse inventory by product types can be studied by cluster analysis; visualization can be applied to understand complex spatial arrangements; and machine learning can aid in guiding the movement of warehouse robots.

StoryMaps Organizations looking to tell a data-driven story often look to maps to communicate

with location intelligence. Adding additional content alongside a map can help to strengthen a map’s persuasive storytelling. A StoryMap helps to communicate a story by creating an interactive experience that features maps, text, images, and videos. Functioning like a templated website, a StoryMap includes adaptable widgets that enable a user to quickly build an information tool without having to learn code.

StoryMaps have become quite popular, with over 450,000 published (Semprebon, 2021). The interactive capabilities of StoryMaps allow users to interact and engage with location information, unlocking possibilities otherwise impossible with static maps, tables, or charts. StoryMaps can be used internally for idea sharing, proof of concept, or financial reporting. Externally, StoryMaps can be branded and used in replacement of time consuming web landing pages. Users can even incorporate forms or survey links to aid in the storytelling engagement and continue to collect data into the organization.

One example is a StoryMap in the area of corporate social responsibility. Countries from around the world have committed to meeting the 17 environmental sustainability development goals (SDGs), many of which have implications for industry actions. The country of Ireland has published online a StoryMap to explain progress in 2011-2018 toward important goals of reducing poverty (SDG 1) and achieving decent work and economic growth (SDG8). (Government of Ireland, 2018). One page of the StoryMap shows unemployment in the Irish statistical regions in 2018. (see figure 2.3) The StoryMap narratives provides this and other mapping insights on the dynamics of changing unemployment over the seven years in relationship to the two UN SDGs.

27

Figure 2.3 A map image from a storymap of unemployment patterns in Ireland displays regional patterns of unemployment across the nation’s regions

(Source: Government of Ireland, 2018)

GEO-AI The rapid collection speed of big data has forced rapid adoption of artificial intelligence (AI) and advanced analytical modeling. As the data has evolved, so have the tools needed to manage it. Big, unstructured, fast-moving data from a variety of sources have necessitated business investment in advanced analytics. GeoAI, or geographic artificial intelligence, is an advanced form of location analytics designed to provide intelligence at scale. GeoAI may be streamlined from many structured and unstructured data sources. It can be used to identify real time location-specific patterns, predict likely outcomes, and provide statistical projections. Such data analysis may be used to predict fluctuations in population, places, or environment, and may be applied as a means of “knowledge work”. The integration and embedding of AI with GIS technology has accelerated the pace of making predictions and business decisions at scale.

Companies are increasingly capturing new insights on their consumers which can provide deep intelligence on the lifestyle and preferences of various audience segments. When combining location-specific customer data, including web engagement, buying history, or common movement patterns, with news, social feeds, and current events, an organization can begin to define the unique attributes of their consumers – where do they go, what do they buy, and what influences them. This insight about a customer’s mindset and behavior, can help an organization to identify other customers with similar behaviors. When modeled through GeoAI, consumer behavior can be used to identify new market opportunities. GeoAI aids in the

28

discovery of loyal customer patterns. It has related uses for financial AI services. For example, Visa’s AI algorithm is used by used to detect unusual charges based on customer purchasing and geo-patterns. Visa estimates that it has stopped/saved $25 billion in fraud charges annually as a result of the model (Nelson, 2021).

Digital Twins Digital Twins may also be used to predict space and time occurrences within a virtual replicated environment. A Digital Twin (Grieves and Vickers, 2017) is a 3D virtual replica of physical assets, processes, or systems that bridges the gaps in both space and time between the physical and digital worlds. The lessons learned, issues observed, and opportunities uncovered within the virtual environment can be applied to the physical world reducing expenses, time, minimizing disruptions and failures, and most importantly lowering harm to its users (Marr, 2017). In construction, spatial integrations technologies have elevated the electronic blueprint from 2D to 3D. Digital Twin environments take real world environments and create a digital world that can be used to plan and monitor a project over time. Digital Twins can be used as a test environment to add different elements to a structure plan. As data comes alive within the Digital Twin environment, analysts can review different scenarios such as the placement of trees to avoid heat islands, time and place of heavy traffic, or interior design test and manipulation.

In a manufacturing setting, Digital Twin approaches are being used to inform product design and integrate it with actual factory environments to optimize productions. Digital Twins enhanced by location analytics can optimize warehouse management systems by providing decision support and comprehensive outcome analytics on workflows, energy and resource utilization efficiency, and overall plant management. Interactions between different parts of a dynamic production environment are visualized and analyzed using Digital Twins and location analytics and fed back to the design process of products (Lim, Zheng, and Chen, 2019). Altogether, by fusing Digital Twins with GIS in innovative ways, businesses can create dynamic feedback loops in all stages between product and service design. Figure 2.4 provides an illustration of its use in telecommunications, as it allows for a digital twin virtualization of proposed 5G and fiber location. The upper left image is an example of an actual site, the other images are digital twin that can be analyzed to determine proper cell tower locations (Esri, 2019).

29

Figure 2.4 Digital Twins for Telecommunications, in which the upper left photo shows a digital model of a proposed cell 5G cell tower, the right photo is a digital display of the fiber line it would run through and the bottom left is a visualization of the internet service provide (ISP) center that it would enter

(Source: Esri, 2021d)

Element 4: Data for Spatial Business

A driving force in the growth of location analytics is the rise of big data. It is estimated that 181 zettabytes of data/information will be created, captured, copied, and consumed in 2025, at an annual growth rate from 2020 of 17% (Statista, 2021). A zettabyte is equal to a trillion gigabytes. Most of this data is either already geo-referenced or potentially able to be so. Businesses are increasingly making use of this big data. For spatial business, this enormous stream of data offers opportunity to obtain value and competitive strength. Location analytics can utilize data to describe, predict, and prescribe from this data using techniques that include visualization, data mining, clustering, network analysis, text analysis, machine learning, AI, and deep learning.

Location data is pervasive and growing in size and type. The Spatial Business Architecture includes all forms of customer, demographic, business, financial, social, and environmental data. Table 2.1 provide a summary of primary data types used to conduct location analytics for business. The sources of these data are next discussed.

30

Table 2.1 Summary of Spatial Business Data

Type Dimensions Locational Use

Customer

Psychographics Lifestyle

Preferences

Satisfaction

Customer data includes attributes on lifestyle, brand preferences, and spending habits, informing marketing, sales, and customer growth and retention

Points of Interest

POI

POIs

Places

Business POIs include business and supplier locations, providing locational information for business planning, and operations and supply chain management.

Movement

Human

Cargo

Networks

Movement data looks at the physical movement of people and cargo from place to place, facilitating business location services and resilient supply chains.

Community

Demographic

Community

Demographic, community data can inform business community strategies and impacts in areas such as social and racial equity.

Imagery and Remote Sensing

Aerial - Satellite, InSAR- Street Level

Remote Sensing

Provides intelligence showing emergency situations in business facilities and locations, movements of assets, movement of supply chain materials, and understanding the geography and players in markets.

Environment

Climate Change

Air Quality

Land Use

Environment data provides authorities indicators key environmental conditions and can inform. Corporate social responsibility actions.

Business Data - Enterprise Many companies already have a vast network of geographically enabled data available

internally for location analytics and discovery. A cornerstone of the business data is customer data. When customer data is analyzed by location, new opportunities are presented, indicating

31

by analysis exactly where behaviors are occurring and when. For example, it may be very common for a suburban family of four to frequent the movie theater, while a young professional in a nearby downtown neighborhood may rarely visit the theater, opting for live music venues instead. Deep understanding of customer demographics, behaviors, and lifestyle preferences presents incredible business value by opening the door to build products that resonate, launch go-to-market strategies that are embraced and extend a product’s lifecycle far beyond the average sunset length, by understanding who the customer is, what product and where a product is desired, and where demand is highest.

Technology advancements enable the deployment of location-based applications to understand indoor spatial patterns such as foot traffic and dwell time. As a customer makes her way in pathway of locations, her movement data can be tracked and assigned to her device ID. This device ID then indicates the behaviors of the customer and indicates willingness to buy a product and/or promotions. Other examples of business data include revenue and sales metrics, employee profiles, customer profiles, asset logs, and logistics. Asset management is reliant on private business data and, in times of crisis, the transparency of this data helps to overcome risk. Knowing which assets are in a particular location, how many trucks are enroute nearby a hazard, or understanding how much revenue may be lost if a route does not fulfill its route, are all spatial questions that a business can answer with spatial technology and business data.

Business data may also include product data, research data, and supply chain data. Everyday business processes and functions also produce spatial data. The business value of this data is high as it directly contribute to product development, asset tracking, customer loyalty, and revenue gains. Some business data may be automated in nature; for example automated data from customer, supplier or facility movements. Other data may come from transactions that are reported. With the rise in automated collection methods, what may have previously taken a business several weeks, months, or even years to capture and store within a company’s database, may now be immediately streamed into business applications. It is the size and fluidity of this data which enables decision makers to add business value to the organization in real time.

Business Data – Commercial To make location intelligence more powerful, many organizations seek third-party

private commercial data to enhance first-party business data. By augmenting enterprise data with third-party data, organizations create enriched portfolios of location data. For example, a company might combine their customer data, with a commercial geodemographic data to not only where their customers are located, but to also examine where potential customers who are like their customers are also located. This can help determine a market growth strategy.

The emergence of Data Marketplaces has made commercial data more readily available. These self-serving ecommerce platforms have enabled the data industry to sell commercial data at scale. Commonly used Data Marketplaces include Snowflake, Amazon Web Service (AWS), and Azure. Data as a Service (DaaS ) solutions are now easily accessed, with streamlined integration and pricing models. With DaaS, data may be streamed into organizational databases

32

and dashboards, bringing up-to-date data in, quickening the process to push location intelligence out. Beyond demographics, massive files on human behaviors are often exchanged through Data Marketplaces. This includes movement data, which matches device IDs to location. When a user ops-in a mobile device for location-sharing, the device will aid in time- stamping the pathways of the person. For example, over a 4-month period in the New York City area, location data from a single user’s smartphone was recorded over 8,600 times, once every 21 minutes (Valentino-Devries, Singer, Keller, and Krolik 2018).

Community and Environmental Data – Public/Open Like commercial data, public-open data may be used to augment an organization’s

existing database. Open data is public data often made available through a creative-commons license from government sources or open-crowdsourcing communities. Many familiar authoritative U.S. government agencies provide public data as open data in order to maintain an open government status. For example, the United States Census is a source of public people data made available as open data. The US Census Bureau provides authoritative demographic data yearly and every ten years by the decennial census. Other authoritative U.S. government agencies providing business related public data include Department of Commerce (DOC), Environmental Protection Agency (EPA), and Department of Labor (DOL). These agencies and others provide essential community and environmental data that can be used to track corporate social responsibility actions and outcomes.

Open data may also be crowdsourced. It may come from community contributed data, as part of an open-data initiative to share data openly and freely in the name of a common cause. Many organizations choose to utilize hubs to host open-data initiatives, which will be discussed later in this chapter. Crowdsourced data can be added via web or desktop applications, but the validity of the data is not always cross-checked. In some cases, open data may be the only type of data available. When this occurs, an organization must consider the risk of this data’s accuracy and consider if other methods may be used to vet or quality-check the open data contributions. Many foundational GIS maps will use a combination of authoritative and crowdsourced open data. This helps to keep maps up-to-date by allowing users to contribute changes to the map in real time, and may allow for changes that may otherwise be overlooked, like the addition of a streetlight, road, or detour.

Imagery and Remote Sensing Data Remote sensing refers to imagery that is not collected directly but rather is collected at

a distance away from the object. Devices that do remote sensing include satellites, planes, and drones. These devices tend to have digital image collectors, including radar collectors, LIDAR, multi-spectral collectors, and digital cameras. Each mode of collection is appropriate for certain business applications, and each mode has advantages and disadvantages (Sarlitto, 2020). Digital cameras are user friendly but limited to the part of the electromagnetic spectrum that covers the range of the human eye, while radar can sense large areas from high altitudes and penetrate through cloud barriers, but have limited resolution and are expensive. Local overflights by small planes or drones with digital cameras are a less expensive but widely used imagery gathering approach, and in extractive, agricultural, and environmental industries.

33

The imagery sector is enriching spatial business by providing base maps, point clouds, space-time imagery, AI-enhanced map imagery, and raster analytics and modeling (Dangermond, 2021). The rapidity of imaging means that information can be provided in a daily or under refresh, yielding timely business intelligence showing emergency situations in business facilities and locations, movements of assets, movement of supply chain materials, and understanding the geography and players in markets.

Today the earth has thousands of daily satellite overflights and image gathering by dozens of governments, international organizations, large well-known imagery companies and small firms (Sarlitto, 2020). For instance, in the US, NASA has been collecting imagery in its Earth Observing Satellite program for two and a half decades, including its planetary land surface imagery from the Landsat series and its Sentinel-6 in collaboration with the European Space Agency to measure global sea level rise. Large earth observation businesses such as Descartes Labs and Planet Labs in the US, Skyrora in the UK, and Axelspace in Japan create commercial satellites for earth observation, while smaller specialist companies such as Zonda design and manage small satellites and provide location analytics services.

Zonda is a firm that produces business intelligence, advanced imagery, and analytics solutions for the business needs of home builders, land developers and financial institutions. Zonda brings a location analytics approach to monitoring of residential investment properties and construction sites (Reid, 2021). Traditionally, most residential-real-estate construction monitoring researched the stages of building construction. Prior to the advent of the covid pandemic in spring of 2020, Zonda’s field worker crews would visit home sites quarterly to observe and record the stage of construction, using simple commercial mapping software. Covid, however, put a stop to the field visits due to concern for the health threat to the crews. As a consequence of the business need created, the company’s analytics team developed machine learning algorithms that can be trained with data to recognize which construction stage a new home is in. Subsequently, during the pandemic, Zonda rapidly grew its satellite- based monitoring in scale and capabilities. It has now set a standard that 80% of its home monitoring to be performed by automatic satellite observation and only 20% by the traditional driving to sites.

Element 5: Platforms

Location analytics are not limited to a single type of platform. Depending on the needs of an organization, spatial technology may take shape through a variety of platform solutions, ranging from cloud-based software to enterprise system deployment, to geo-enabled hubs. Organizations of all sizes, budgets, and scalability, may find success in deploying spatial solutions. As diverse as the nature of business, spatial technology is adaptable and personalizable based on desired results and intelligence goals.

Cloud The evolution of cloud computing software has accelerated major advances in spatial

technology. In this mode, GIS software can support a software-as-a-service or SaaS model. This revolution of GIS as SaaS has opened new opportunities for organizations looking to establish a

34

spatial infrastructure. Within this infrastructure, the organization can utilize a variety of spatial tools to perform day-to-day business functions. With the connections enabled from the cloud, these tools can work in tandem with one another, enabling a network which supports an interconnected workflow among processes and people within different areas of the organization.

The architecture within the cloud has the capability to manage all aspects of a geospatial system, from maps and data, to analytical tools and applications. The connection of tools within the cloud environment enables location intelligence sharing at scale. Millions of active interactions can be taking place at any given time. Organizations functioning within the cloud benefit from the fluidity and open access of data sharing and management. Open sharing within a department or across the organization, enables users of varying job titles to access and build off one another’s work. Additionally, SaaS-based GIS software is intelligently designed to provide seamless updates, removing the necessity of downloading the latest features and updates.

Though data security within the cloud may be of top concern, the evolving geospatial architecture alongside advances in cloud-secure environments has helped to alleviate this concern among stakeholders. In many cases, the benefits of the cloud outweigh the risk, especially as organizations build out infrastructures that connect workers onsite to home office workers. By automating processes and streamlining the flow of data, through other emerging technologies, such as Internet of Things (IoT) sensors, vehicle sensors, social media and web data, geographic information can be obtained in real time.

Enterprise Enterprise integration is a process of expanding location analytics and intelligence to

serve spatial needs across the entire organization. It needs to serve all departments in the organization that have need for locational systems (Woodward, 2020). Standalone spatial systems in separate departments that have their own separate databases create obstacles to the consistent and rapid sharing of the same data. These separate systems may serve a department well in the early spatial maturity stages of an organization, since the users are limited and mainly interested in data from their own department. Moving to enterprise integration of data has many advantages including eliminating redundancy in the same data and enabling users in diverse areas of the organization to be consistent in their use of location analytics and intelligence. Having the common platform also can be helpful from an information security standpoint, since security protection can be strengthened for one central data-base, rather than scattering the security protection to the separate “silos.”

Another advantage of an enterprise system is that any user of location analytics throughout the company has the potential to access all the spatial data available organization- wide. The user might not have present need for all this data, but future needs might arise to add data from a distant department and it will be easily accessible. Because of widening of spatial users, expanded data, and consistency of enterprise GIS software, having an enterprise spatial system adds to the competitiveness of the company.

35

A GIS enterprise system offers decision makers options as the enterprise platform is a robust system that connects with the full set of company GIS solutions. Leaders increasingly are choosing to base the enterprise system in the cloud or retain it on an internal infrastructure, for security and control reasons. Users looking to perform advanced spatial analysis through desktop or mobile applications can benefit greatly from an enterprise system.

Desktop While GIS started out with a strong desktop (stand-alone) component, the massive

movement to the cloud over the last two decades have substantially altered the use of this platform. Although desktop provides essential high-end GIS computing to skilled individuals or teams of professionals, among the weaknesses include challenges in scaling the number of users and upping the capacity of desktop or local servers and challenges in how to provide simplified user interfaces to the business non-technical user and vulnerability of desktop systems to physical failure.

The desktop can be enhanced at low cost through connections to WebGIS. This WebGIS platform provides GIS software, data, and processing in the web, so the user is freed up from particular devices and installations and needs only a web browser for access. This has several advantages, including (Fu, 2020; Longley et al., 2015) that is available worldwide, there is a low cost of implementation, it has minimal requirements for the desktop, and there are a large variety of related web services and libraries that can be integrated.

Portals A spatial portal is a public or private location to share applications across designated

users that can range from small groups to the entire organization (Tang and Selwood, 2005, Esri, 2021). The portal is useful to organize information and make it available to groups of users. Private and restricted information can be excluded from the portal or made accessible only by approved users. The overall philosophy behind the portal concept is to make as much information as possible open and easily accessible. A portal is designed to share the information across mobile devices, social media, WebGIS, and servers.

An example of a business portal is Esri’s Portal for ArcGIS, which can be installed, along with its applications, on a company-owned server or in the cloud (Esri, 2016). Among the provider’s applications commonly available are: cloud-based GIS (ArcGIS Online), a user-friendly mobile app builder (WebAppBuilder), a dashboard creator and operator (Operations Dashboard), two applications for collecting information with a mobile device (Collector for ArcGIS and Survey 123), software that supports users in creating imagery including point clouds (DroneMap), and an app to encourage teamwork and coordination for field workforce (Workforce for ArcGIS). These applications can be supplemented with other applications available commercially and proprietary ones developed by the company.

A spatial portal has the aim to foster collaboration among the users within a business. A company can offer private portals restricted to segments of its workforce as well as public portals for invited customers or open to the general public (Tang and Selwood, 2005; Esri, 2016). For the portal user, the portal provides a web-based set of applications, flexible and

36

accessible across devices, to address a problem, develop location analytics solutions, and support research or decisions. The web positioning of the portal does raise potential security issues but security measures can mitigate or largely eliminate the risk.

Hubs Organizations looking to center their workforce around collaborative initiatives, may

find interest in the cloud-based hub platform. Hubs are much broader in reach than a portal, providing a centralized location for the enablement of people, data, and tools to reach common goals. The platform enables a single place to communicate and collaborate on key initiatives within the organization, including planning activities, sharing projects, and goal setting. Often used for community engagement, a hub can be used for a variety of business functions, including spearheading a new product launch, strengthening company culture, or onboarding new employees. Hubs support the hosting of geo-enabled databases, allowing quick access to mapping layers, prepared visualizations, raw datasets, and StoryMaps.

Functioning much like a website, a hub grants unlimited possibilities when building up its contents, allowing components and structure to take shape around business needs. Templates are adaptable to brand personalization and unique initiative aspects. Analytical components of a hub also allow for the tracking of views and interactions within the platform. This added value can help decision makers to evaluate content engagement and stakeholder interaction.

With hubs, organizations are enabling open sharing of location intelligence across the globe, while centralizing around common themes that connect people and places on a unified platform. The Los Angeles GeoHub exemplifies a community hub with intensive use (see Figure 2.5). The hub has publicly available maps, downloadable data, and documentation on over 1,000 features for citizens, and business alike, including analyses of job acceptability, populations, and a range of demographic analyses (Marshall, 2016). The hub also has features to create customized experiences, contribute data to the hub, and participate in, collaboration and co-creation of work being done on initiatives by internal teams, and external teams or combined teams (Esri, 2021).

37

Figure 2.5 Los Angeles City’s GeoHub comprised of sets of features the public can access to explore, visualize, analyze, build, export, and share maps, with an example being a map of job accessibility by transit type

(Source: City of Los Angeles, 2021)

The application to spatial business is that a data-centric business that seeks to interact with the public can install a Hub with the intent to improve sharing and collaboration of its own teams inside the company, provide easily accessible maps and data to certain customers or the public at large, and encourage collaboration between the company’s workforce and outside existing and prospective customers. The firm as a profit-making organization could maintain a portion of its data on the inside for commercial sales. For instance, the hub might be appropriate for government data providers, statistical data providers, or environmental consulting firms, or think tanks.

A general point for platforms and applications concerns data protection of private information. Recent movements in consumer data protection have shifted the use of location services to now include permission-based options. Many applications now require opt- in services, i.e. mandating that the user have the choice not to agree to provide her personal data at the time of entering a software service. These changes have been important to protect the rights of consumers and in many ways, also have been crucial to alleviating the ethical dilemma that organizations often face in utilizing consumer data tracking. Keeping consumer

38

data private and permission-based, helps to protect personal information. Ethical data practices protect both the consumer and the organization. Companies should have a sound ethical policy and data implementation strategy can help to minimize this risk for an organization looking to implement spatial technology.

Element 6: Location Intelligence

The second level of location intelligence are decisions that are based on location insights. Location intelligence can inform decisions to increase competitiveness, foster new products and services, and provide new ways to strengthen ties with suppliers and buyers. For instance, in the Kentucky Fried Chicken its franchisees with rich and varied social media and demographic information at the micro level for decision-making on competitive locations of retail units. Decision making also benefits by group collaboration, big data, and anytime/anywhere availability of location analytics (Sharda et al., 2018). The wide dispersion of location analytics capability in companies means that dispersed team members can be informed by location analytics and collaborate on decisions.

A third level of location intelligence are the impacts that result from location analytics. These impacts include the performance of a company on number of dimensions such as market share growth, customer satisfaction, operational efficiencies, supply chain reliability, and societal benefit. As seen in this chapter’s Walgreens closing case study, impacts occur at the operational level as well. In this case, it was the quick development of application to provide testing and vaccines equitably to the public that also served to enable of broader enterprise solution.

Closing Case Study: Walgreens Walgreens is one to the two largest pharmacy firms worldwide, with 2020 revenues of

$140 billion and assets of $87 billion. It operates 9,000 drug stores throughout the US and acquired a majority interest in Alliance Boots in 2014, one of the largest pharmacy firms in Europe and parts of Asia. It acquired 1,000 Rite Aid stores in 2017. Walgreens has intensive competition and must drive sales volume to achieve its on its profit margins (Morningstar, 2021). The firm also faces regulatory constraints on its drug products and pharmacy activities.

GIS has been present in the firm for over fifteen years, and its principal use is for planning and operations of its store network throughout the US. It has a spatial enterprise system for its US stores and employs simpler cloud-based spatial solutions for certain special projects. The corporate GIS team for operations in the US consists of the director of enterprise location intelligence, several GIS power-users, and specialists who focus on external facing provision of spatial data, statistics, and maps. The team participates in designing the GIS features for mobile applications of its customers that are used to make use of the varied product channels of the firm, including visiting and purchasing from stores.

39

Figure 2.6

Regional middle managers at Walgreens discuss how to solve a spatial problem, which is being displayed on a large screen using the firm’s WalMap software

(Source, ArcWatch, Esri, 2015)

The leading GIS applications are WalMap and WalMap Pro, locally tailored software that supports middle and upper-middle management corporate-wide in group analytics and decision making. In regional offices throughout the firm, conference rooms have large screens displaying Walgreens and competitors’ store locations, demographic features, and market indicators (see Figure 2.6). The enterprise system supports WalMap on mobile devices and the web, so it is device-independent and centrally controlled. WalMap provides solutions for (1) deciding on the locations of stores, and the optimization of the geography of groups of stores, (2) giving broad demographic and marketing information to the middle ranks of management firm-wide, (3) maintaining and displaying competitive data on a monthly basis, so merchandising can if a store location is competitive or not, and (4) offering a range of other mapping, e.g. pharmacy information, market share, aerial imagery, while WalMap Pro provides advanced tools to a select group with the market planning and research group.

The systems development of these two critical applications was accomplished through the coordination of four systems development teams: the corporate GIS team, internal IT, an offshore outsourcer, and Latitude Geographics from Canada. The project success is ascribed to a talented director of enterprise location intelligence, who kept everything moving. This key manager reflected that “the team had to have patience and set reasonable expectations. They said it’s going to take a little bit longer to make sure we get it right and that’s okay, with the payoff at the end” (Walgreens, 2018). The same team also commenced a spatial enterprise project.

Walgreen’s relative slow and deliberate improvement steps were interrupted by the start of the Covid pandemic in March of 2020. A White House meeting led to immediate urgent request to Walgreen’s location intelligence director to decide on where testing sites would be

40

set up. The director created a web app within hours that could choose testing locations and shared it with other key leaders, in a rapid exchange lading to a prototyped solution. It was later refined by the spatial team and they started within weeks designating covid testing sites throughout the nation, eventually designating a national set of thousands of locations, and by the following winter doing a similar designation for stores giving vaccinations (Walgreens, 2021).

In this rapid and successful site selection, the team was helped by a robust data-base already present of store locations and attributes, demographic and other information. In the process, the team emphasized equity of distribution and set-up the socially vulnerable communities first.

An offshoot of this emergency intervention in the covid crisis is that the GIS leadership pushed forward rapidly and complete the spatial enterprise system that had been on a slow track before the pandemic (Shah, 2021; Walgreens, 2021). The firm’s location enterprise portal resides on a cloud platform from a leading vendor. The user can enter the portal through an enterprise location intelligence homepage, which provides access to all the enterprise modules, which include the latest versions Wal Map, Wal Map Pro, and the Asset Protection mapping module that provides location analytics for sustaining and protecting assets from environmental and other threats to assets (see Figure 2.7). The portal entry page reinforces the chapter point that varied personnel have different mapping needs. Here, middle managers use the simple and data-rich WalMap app; GIS analysts and company planners utilize the full- featured WalMap Pro; and emergency and security personal depend on the specialized asset protection map. All the interfaces draw on the centralized enterprise data and analytics.

An example of the Wal Map Pro capabilities is real-time display of customers, in a time slice, for three Walgreens stores in the Inland Empire of southern California, showing a tendency for clustering of customer residences around each of the three stores, with relatively little cannibalization between stores (Figure 2.8). This exemplifies the power of cloud-based, real-time situational analytics. With the cloud platform, Walgreens GIS team is better able to integrate mapping and location analytics to employees accessing mobile devices, remote workers at home, and group decision making in regional offices and headquarters, while communicating through integrated web mapping across the company.

Walgreens GIS management indicated the company has moved forward to be 80 percent of the way towards the full GIS maturity level. Although the location enterprise portal has been the centerpieces so far, GIS management sees a number of projects needed in the future to move towards maturity, including supporting the Internet of Things (IoT) and introducing AI and machine learning analytics.

In summary, Walgreens illustrates the dimensions of a Spatial Business Architecture. There are business goals that drive its use and there is a strong technical team that collaborates with management to address needs with solutions. As such, they demonstrate the development of enterprise level spatial platform that responds to business needs, attends to a

41

pressing societal (pandemic) issue, relies on human talent, and is creating strategic value for the company.

Figure 2.7 A diagram of Walgreen’s Enterprise Portal shows its major components of WalMap Pro, Enterprise Location Intelligence, Asset Protection Mapping, and WalMap Lite

(Source: Walgreens, 2021)

Fig. 2.8 Wal Map Pro: Mapping of a Time Slice of Customers’ Affinities for three neighboring Walgreens stores in San Bernardino County CA

(Source: Walgreens, 2021)

42

CHAPTER 3

Fundamentals of Location Analytics

Introduction To achieve competitive success, organizations are increasingly placing analytics at the

heart of the business. Business analytics has been defined by Thomas Davenport as the “extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions” (Davenport and Harris 2017, pg. 26). Davenport’s definition not only highlights the importance of data and analytical modeling in contemporary organizations but connects it to decision-making and actions. This connection is key. Clearly, the analytical process of transforming data into insights has a purpose: to provide evidence and support for decision-making. By using analytics to make better decisions, organizations generate value that manifests in the form of business benefits, both tangible and intangible. These include cost savings, revenue growth via increased share of wallet, uncovering business opportunities and untapped markets, increase in productivity, process improvements resulting in asset efficiency, enhanced brand recognition, increased customer satisfaction, and benefits to the environment and society. In digitally mature organizations, analytics is not just an add-on to existing processes and practices; rather, it is fully integrated into the strategies and business functions.

This chapter dives deeper into location analytics element introduced as part of the Spatial Business Architecture and explores their use throughout the location value chain. Location analytics can inform where companies should locate new stores, stock facilities, and enhance infrastructure. They can guide employee recruitment, optimize sales territories, and maximize loyalty programs. They also help companies appraise risk emerging from threats such as competitors, the natural environment, changing business patterns and trends, and unanticipated events such as weather emergencies, pandemics, and social unrest — information that is critical to a proactive response, mitigating threats to business continuity and improving business resilience. Finally, location analytics helps companies direct their philanthropic endeavors and shape corporate social responsibility (CSR) strategy and initiatives.

Principles of Business Location Analytics Business Location Analytics, which recognizes the critical importance of location in

business, helps organizations make decisions informed by data (location as well as non- location) and using sophisticated analytical methods and spatial analysis techniques.

Underpinning business location analytics are the following four principles of location:

 Location Proximity and Relatedness;

 Location Differences;

43

 Location Linkages; and,

 Location Contexts.

Location Proximity and Relatedness As businesses seek ideal customers, their own locations relative to the locations of

prospective customers and competitors becomes paramount. For example, a bakery and coffee retail chain seeks customers with certain consumer preferences and wants to ascertain where these customers live, work, and shop. What are their usual routes as they travel from work to school, or from home to work, and how far do they live from various points of interest (POIs) such as grocery stores, gyms, libraries, and restaurants? What are their demographic, socioeconomic, and psychographic characteristics?

The first principle of Location Proximity and Location Relatedness stems from Tobler’s (1970) first law of geography. This seminal law states: “Everything is related to everything else, but near things are more related than distant things.” “Related” and “near” are the cornerstones of this first principle of location. In business decision-making, proximity—whether to customers, suppliers, competitors, complementary businesses, assets, or infrastructure—is the basis of important strategic, tactical, and operational considerations. Proximity implies relatedness, or spatial heterogeneity, and suggests that local factors can make one area significantly different from others. In a business context, nearness or proximity is often measured in terms of driving distance, driving time, or walking time, and is fundamental to the delineation and description of trade areas and service areas, discussed later in this chapter.

For example, consider a full-service insurance company. To determine premiums, an insurer would factor in proximity of businesses and customers to coastal areas that are prone to flooding, population density, and crime rates among many other factors. It is often the case that insurance premiums of two similar properties, located in the same neighborhood, close to each other and equidistant from the coast (nearness) are likely to be comparable (relatedness) than to properties located in a different neighborhood (dissimilar population density and crime rate) farther inland.

For location analytics modeling and decision-making, nearness and relatedness have several implications. Nearness and proximity are often measured for business locational decision-making using distances that are factored into various models for description, prediction, and ultimately decision-making. However, nearness could potentially introduce spatial bias into location-based analytical models which stems from a pitfall of spatial data, known as spatial autocorrelation. These implications, particularly the issue of spatial autocorrelation (Longley, Goodchild, Maguire, & Rhind 2015) has methodical implications for location analytics models.

Location Differences, Linkages, and Contexts The next three principles that inform business location analytics are Location

Differences, Location Linkages, and Location Contexts (Church and Murray 2009). The first principle, Location Differences, indicates that some locations are better than others for a given

44

purpose. The second principle is Location Linkages, meaning an optimal multisite pattern must be selected simultaneously rather than independently, one at a time. The third principle, Location Context, indicates that the context of a location can influence business success (Church and Murray 2009).

The principle of Location Differences can be illustrated through the lens of industry clusters - geographic concentrations of interconnected firms and institutions in a particular field (Porter, 1998) that are known to be engines of economic development and regional prosperity. For example, Napa Valley in northern California is a leading wine production cluster worldwide due to a climate that is conducive to grape production and consequently the presence of hundreds of independent wine grape growers among many other factors. On the other hand, Carlsbad near San Diego in southern California has a specialized cluster of golf equipment manufacturers that has its roots in southern California's aerospace industry which spawned manufacturing businesses specializing in metal castings and other advanced materials. As a consequence of location differences between the two regions, businesses engaged in sports equipment would have more affinity for the Carlsbad region, while manufacturers or parts suppliers of irrigation and harvesting equipment are more likely to find the Napa region more attractive. Regional economics uses a measure, “Location Quotient (LQ),” to measure this industry concentration of a region.

Next, to understand Location Linkages, consider medical device manufacturing industry which is linked to nine other industry clusters: biopharmaceutical, distribution & e-commerce, jewelry, recreational goods, electrical wiring, plastics, IT, production technology, and downstream metals (Cluster Mapping, 2021). A recent study (Munnich, Fried, Cho, and Horan 2021) has shown that the U.S. state of Minnesota is a national leader in medical devices manufacturing. The development of the medical device cluster in Minnesota originated from greater Minneapolis metropolitan area due to Medtronic, a leader in medical devices innovation. Now, in Minnesota, almost 500 medical device companies statewide are complemented by over 6,000 businesses in the aforementioned nine linked industries (Munnich et al. 2021).

In terms Location Context, the Minnesota's medical device cluster relies on a robust professional, scientific, and technical services workforce as well as a reliable transportation network that enables efficient goods movement. Alongside the Mayo Clinic, the greater Minneapolis-St. Paul metropolitan area is home to the University of Minnesota, a large, flagship university, producing a readymade workforce comprised of medical, business, engineering, IT, and computer science professionals, analysts, and researchers for medical device and other companies that offer plenty of high-paying jobs. In addition, from the perspective of Location Context, Minnesota's medical device industry benefits from the presence of the Minneapolis-St Paul Airport (MSP), which acts as a gateway for high-valued and often critical of medical devices and equipment that are exported to various parts of the world.

These principles have the following implications for business location analytics. To develop an effective location-based business strategy, it is essential to adopt a clear understanding of how locations affect business success. Locations can provide a competitive

45

advantage due to their distinctive physical, environmental, and human characteristics. Locations can have vital business linkages for talent, customers, talent, and supply chain and logistics networks. This creates a location context that if properly understood can be a key factor is business strategy and success. Location analytics provides to tools to assess such factors.

Hierarchy of Location Analytics Stemming from three well-known categories of analytics – descriptive, predictive,

prescriptive--the hierarchy of business location analytics is comprised of Descriptive Location Analytics, Predictive Location Analytics, and Prescriptive Location Analytics, as shown in Figure 3.1. Each step of the hierarchy is informed by increasing levels of sophistication of analytical (mathematical and statistical) methods. The hierarchy of spatial analysis techniques – spatial data manipulation, spatial data analysis, spatial statistical analysis, and spatial modeling (O’Sullivan and Unwin 2014) informs the hierarchy of Business Location Analytics. As the extent of sophistication of mathematical and statistical modeling as well as spatial analysis increases, the extent of location intelligence and consequently location-based insights increase as well. This can be leveraged by a business to design spatially-aware business strategies that yield competitive advantage.

46

Figure 3.1 Hierarchy of Business Location Analytics, comprised of descriptive, predictive, and prescriptive location analytics

(Source: Author)

Descriptive Location Analytics Descriptive Location Analytics is often used to analyze current or historic business data

to understand what has or is occurring and where. Descriptive location analytics is characterized by spatial visualizations such as maps, reports, and dashboards. These show where customers, employees, stores, competitor locations, suppliers, distribution centers, transportation hubs, critical infrastructure and assets, and points of interest are located, often relative to each other. They also reveal spatial patterns and changes of important business Key Performance Indicators (KPIs) such as sales, profits, revenues, and consumer preferences, as well as geographically referenced characteristics of areas of interest, such as socioeconomic attributes of trade areas.

In other contexts, maps, reports, and dashboards may show important parts of a supply chain. These may include connections, linkages, and routes between customers, suppliers, and distribution or transshipment hubs, as well as territories vulnerable to business disruption—for example, due to a natural disaster or other emergency such as the failure of critical infrastructure. Descriptive location analytics provides important visual cues, uncovers patterns, and reveals location-specific insights previously unknown to a business.

The techniques of descriptive location analytics often include various forms of spatial visualization and mapping. It involves descriptive data modeling of features that are relevant to a business, showing patterns and trends that form the basis for exploratory analysis. Spatial data manipulation and rudimentary spatial analysis functions such as intersection, union, overlay, querying, and basis summary statistics characterize descriptive location analytics. Apart from these techniques, distance and proximity analysis, buffering, density analysis, and 3-D modeling are often part of descriptive location analytics. As mapping provides visual cues of change, descriptive analysis is an efficient way to turn large, complex datasets into effective visualizations. These maps bring valuable spatial context to decision making. In many cases, descriptive analytics may be the greatest depth needed to draw a statistically backed conclusion.

The process of moving from exploratory to more advanced analytics is considered “enrichment” and can introduce several layers: a customer layers, a geo-demographic layer, a facilities layer and so forth. Visual patterns may emerge as obvious or may necessitate descriptive spatial statics such as hotspot and cluster analyses.

Descriptive analytics may be used to further prepare data for predictive and prescriptive analytics. It is often a manual process and may be seen as a first pass of data discovery. A series of spatial layers may be overlapped to observe correlations among varying datasets. Additionally, spatial layers may be used as the foundation to deeper analysis. Whereas the information products created in descriptive analysis can then be applied as the foundation for predictive and prescriptive analytics.

47

Illustration of Descriptive Location Analytics Consider the example of Sephora, a beauty and personal care specialty brand, which is

considering business development and expansion in the greater Houston, Texas, metro market. To examine market saturation and analyze opportunities, it has created a web-map comprised of several layers, shown in Figure 3.2.

Alongside existing Sephora locations (black circle with Sephora logo) and Sephora-at-JC Penney locations (red circle with Sephora logo), market opportunities are shown as five categories: proposed locations of new stores (red circle with a number), locations with ongoing negotiations (blue asterisk), areas of interest for business development (red asterisk), locations of interest but no opportunity to develop at the present time (red square), and locations that have been evaluated but ultimately not selected for development (black square). Geo-enriching the map are some of the external variables such as major shopping center locations with attributes including gross leasable areas, anchor stores, year opened, and annual sales, as well as various population and demographic variables.

On an ongoing basis, these map layers can provide descriptive insights to Sephora’s business development team. In terms of location analysis, the map layers are rich with possibilities. For example, drive-time buffers can produce trade areas to be analyzed for market potential. Proximity analysis to competitors, nearby shopping centers, and retail locations can inform sales forecasts; and co-tenancy, i.e., two tenants leasing the same property, is a useful metric for site comparison and site suitability analysis.

Figure 3.2 Web-map layers of Sephora locations and opportunities for business development, greater Houston TX metro area

(Source: Esri, 2021c)

NOTE TO EDITOR: Prefer this Figure in LANDSCAPE MODE

48

Predictive Location Analytics Descriptive location analytics provides organizations with location intelligence about the

current state of business or about past business patterns. While insightful, descriptive location analytics only scratches the surface to what is possible with location analytics. Predictive location analytics provides a deeper understanding of what is happening in current and past states, while simultaneously detecting meaningful statistical patterns to predict future business outcomes. Through the identification of spatial patterns, business outcomes may be forecasted which can help decision makers to see opportunities for growth, profitability, and risk.

With the availability of very large and timely data sets, many organizations increasingly rely on predictive location analytics to discover meaningful, statistically significant spatial patterns, spatial clusters, and outliers, related to the prediction of business outcomes such as profitability, growth, risk of customer defection, and incidences of fraud. Whereas standard (non-spatial) predictive analytics determines what is likely to happen in the future; predictive location analytics determines not only what is likely to happen in the future, but where. In this way, predictive location analytics provides guidance to organizations about the likelihood of future events, contingent on location.

Predictive location analytics methods are more sophisticated that descriptive location analytics. These techniques involve the application of traditional statistical and geostatistical approaches to analyze spatial clusters that may become evident from descriptive mapping. For example, clustering models may confirm the importance of a region of parts suppliers in an organization's supply chain. Analysis of spatial clusters of internet users in a region may provide insights about a region's preference for e-commerce; this has implications for an organization's business development strategy. Statistical models may show hotspots and coldspots of customer activity on social media. For example, the use of social media data may be used to discover pockets of expressive sentiments about companies and their products across the US. Geotagged sentiments can be mined to uncover a range of customer experiences and emotions and predict regions where customer churn is likely and therefore customer retention efforts need to be initiated or redoubled.

Another form of spatial statistical analysis involves regression-based modeling that establishes associations between a dependent variable of interest and several independent predictor variables. For example, an insurer may model insurance premiums based on location, type of client, type of insurance, proximity to risk factors, etc. Such models that factor in locations relatedness, nearness, and differences may be prone to spatial bias. Diagnostic tests that measure spatial bias are essential for such models.

With the increasing availability of geospatial big data, spatially mature organizations are deploying data mining and geo-Artificial Intelligence (GeoAI) based predictive models that use machine learning to explore spatial relationships between dozens of factors and an outcome of interest for a business. For example, GeoAI models can uncover potential threats to a firm's supply chain in the event of a natural disaster or an emergency.

Illustration of Predictive Location Analytics

49

In the agriculture sector, effective weed control is essential to that minimizes damage to crops spread over tens of thousands of hectares. To accomplish this, farmers need to know the precise location of each crop. They also need to accurately pinpoint the application of herbicide, as well as of fertilizer and fungicide. Each crop has it its own set of conditions, and spraying herbicide outside precisely defined buffers can not only destroy crops but render entire fields unfit for farming while reducing costs related to overused herbicides and wasted water. Another consideration is how aggressively to treat the weeds. Growers need information on the types of weeds, as well as their number and location, so that herbicide programs can be tailored accordingly.

To address this problem, cameras and sensors outfitted on tractors capture geotagged images every 50 milliseconds from different angles, creating enormous volumes of spatiotemporal big data on crop and weed growth. The cameras and sensors on these “see and spray” machines (Figure 3.3, Peters 2017) use deep learning algorithms that are in many ways similar to facial recognition.

Figure 3.3 Blue River Technology’s (acquired by John Deere) LettuceBot with See and Spray Technology, at work, in Salinas CA

(Source: Chostner, 2017)

To recognize weeds from crops, deep-learning-based neural networks are trained by tens of thousands of images––unstructured big data––stored in huge image libraries (Chostner 2017). With sufficient training, these systems are able to recognize different types of weed with high degrees of accuracy. Matching weeds to locations, “see and spray” machines spray precise amounts of herbicide within precisely defined crop buffers. Deep learning algorithms are enriched to take into account other location variables such as gradients of hillsides. This deep- learning-based location intelligence approach has been shown to save tremendous amounts of herbicide compared to conventional spraying technology, enabling farmers to grow more with less. The automation of weed control also results in cost savings for labor, especially on large farms.

Prescriptive Location Analytics Prescriptive location analytics is the most advanced form of location analytics. Reliant

on historic data and trained models, prescriptive location analytics can calculate the probability of events occurring, which consequently produces a suggested course of action. While

50

predictive location analytics generate a forecast or predict the likelihood of an event occurring, the output of a prescriptive model is a decision, much like a prescription written by a doctor to treat an ailing patient. When viewed through a spatial lens, prescriptive location analytics model business scenarios by factoring in locational attributes and constraints of specific location to prescribe the best course of action to determine optimal business solutions and outcomes. Prescriptive location analytics satisfy business objectives by factoring in mathematical equations which connect data based on proximity, relatedness, and calculation of spatial differences.

Prescriptive location analytics are widely used in facility location decisions, supply chain network design, and route optimization. It is particularly relevant for optimally siting manufacturing facilities relative to the locations of suppliers, customers, transportation options and environmental considerations, and for siting retailers’ warehouses, fulfillment centers, and distribution centers in coordination with complex and/or shifting demand patterns. Spatial objectives may include the minimization of transit costs, maximization of population coverage, or a combination of several objectives. Non-spatial constraints may include schedules of deliveries to be made, delivery time windows, trucking capacity, and regulations imposed by state and federal departments of transportation. Spatial constraints may occur as a result from barriers imposed by the physical geography of a region (for example, mountainous terrain, or temporary road closures), street attributes (for example, one-way streets in central business districts, historic traffic patterns, speed limits), and distance or travel time restrictions.

Prescriptive location analytics can support organizations to address strategic, tactical, and operational problems and can be especially helpful when problems are repetitive in nature. Often underpinned by optimization approaches that are grounded in operations research and enriched by spatial analysis, prescriptive location analytics can provide optimal or close-to- optimal solutions for large, complex problems. Apart from site location analysis and distribution system design, such models are used for routing optimization, demand coverage, analysis of cannibalization, and for informing relocation strategies in myriad settings.

Illustration of Prescriptive Location Analytics Consider the example of CIDIU S.p.A., an Italian company that works in the sector of

environmental services, dealing with all aspects of the waste management cycle: collection, treatment, disposal, recycling and energy recovery, integrated sophisticated optimization modeling with GIS to schedule the weekly waste-collection activities for multiple types of waste without imposing periodic routes.

The company's main objective was to generate efficient weekly shifts of garbage pickup by reducing operational costs and minimizing the total service costs, including environmental costs. Main decisions to be made included the weekly assignment of a vehicle to a garbage type and the daily route of each garbage pickup vehicle for each shift (Fadda et al, 2018).

By innovatively using the IoT paradigm, the company outfitted dumpsters and garbage pickup vehicles with sensors that would monitor the capacity of garbage in dumpsters and vehicles. As soon as capacity of dumpsters approached 80%, depending on the type of trash, an

51

appropriate vehicle from the company's daily operational fleet working three shifts of 6 hours each would be routed (or, re-routed) to pick up the trash, depending on location proximity, capacity of the truck, and several other factors. The service area was an urban area near Turin, Italy. By integrating IoT, GIS, and optimization modeling and using a location-aware approach (see Figure 3.4), CIDIU S.p.A. was able to completely eliminate the third shift for the entire service area. In addition, the number of vehicles used during a test period decreased by 33% reducing waste-collection operational costs and increasing the company’s competitiveness (Fadda et al, 2018).

Figure 3.4 CIDIU S.p.A., Solution Architecture comprised of field components, middleware, route optimization algorithm, web portals, and apps

(Source: Fadda et al, 2018)

Location Analytics Across the Value Chain The application of descriptive, predictive, and prescriptive location analytics are spurred

by specific business needs. Table 3.1 and the subsequent descriptions serve as an overview of various types of business needs chain that necessitate the application of location analytics. They are offered as a starting point for assessing an organization's value chain and how different parts of the value chain can stand to benefit from the deployment of location analytics.

Research and Development Recent location intelligence market studies have revealed that location intelligence is

critically important for the Research & Development (R&D) function, more so than for other organizational functions such as operations, marketing, and IT (Dresner 2020; Spatial Business Initiative 2018). Drivers of this trend include the proliferation of mobile geolocation data and

52

the development of mobile-friendly location-based services. Such studies have also shown that R&D interest in location intelligence is highest for data visualization purposes, followed by middling interest for conducting real estate investment and pricing analysis, geo-marketing, site planning and site selection, territory management and optimization, and fleet routing and tracking.

Emerging areas of R&D interest in location intelligence are for business purposes such as supply chain optimization, indoor mapping, and IoT. Unlocking location intelligence from massive mobile data to understand patterns of human movement is another critical contemporary area of R&D activity in many sectors. By understanding human mobility, businesses can forecast supply and demand efforts to account for locational preferences and seasonality differences. Knowing when to deploy new products and services is just as important as knowing where.

Table 3.1 Applications of Location Analytics along the Organizational Value Chain

Value Chain

Location Analytics

Descriptive Predictive Prescriptive

R&D and

Market Research

Customer Preferences

Mapping of customer behaviors to understand product preferences.

New Product Prediction

Analyzing demographic, income, and spending behaviors to predict the likely success of a product in different locations.

Feature Segmentation

Use of Telemetry data to understand feature and functionality response, which is funneled into a dashboard to indicate where to first launch a new product feature.

Marketing and Sales

Audience Targeting

Identifying target areas for the purpose of marketing to select audience types, developing sales territories to service the needs of a region.

Customer Prediction

Identifying common consumer behaviors over time to predict where you will find more customers like them to increase lift to a retail location.

Campaign Attribution

Discovering the right advertising channel to focus marketing efforts on based on location-based target audience behavioral response to different media types.

Location Planning and Real Estate Strategy

Expansion Analysis

Comparative economic, talent,

Site Selection

Trade Area analysis to determine location of new

Virtual Site Creation

Creation of a Digital Twin that mimics the characteristics of

53

customer, community attributes of business sites for expansion.

facilities. Analysis of supply chain network to determine location and needed capacity of a new distribution center.

the real world site, that can be manipulated with varying datasets to analyze design and implementation plans and identify risks.

Operations Monitoring Dashboards

Dashboard that monitors activities across key business locations.

Employee Safety Management

Analyzing employee safety trends to improve health and safety management in high risk locations.

Supply Chain Optimization

Projecting incremental store traffic lift to aid in procurement efforts to keep shelves stocked with product during seasonal high demand.

Corporate Social Responsibility

Diversity Progress

Mapping business and community data to tell a story of diversity and the impact on the local community.

Environmental Impacts

Dashboard that visualize and predict environmental impacts of operations in order to assess needed changes.

Health Targeting

Understanding the likelihood of a population to receive a vaccine, deploying more communication efforts in areas where response may be low.

Descriptive Location Analytics may look at store locations with consumer attributes attached such as overall sales, distance to store, and so forth. Predictive Location Analytics may take this a step further, by utilizing historic consumer behavior data, such as demographics, purchase behaviors, and foot traffic to predict the likeness of a consumer to buy a certain product within a certain market. Prescriptive Location Analytics will project consumer behavior data, while estimating the likelihood of certain outcomes, to help the business hone new product features to defined market segment and locations.

With location at the forefront of decision making, R&D efforts can sway with the ebbs and flows of different trade areas, while also taking into consideration consumer demographics, behaviors, and store proximity. It can also help retailers to understand which areas are more likely to buy online vs. brick-and-mortar. How to advertise to audiences while en route to a location. And how to restock supply based on shelf replenishment needs, in the quickest, most efficient ways possible. Beyond retail, urban mobility patterns can also be used to determine risk - particularly in the insurance industry, which may be experimenting with new terms and policies based on location. By performing location intelligence research and analyzing data through the various location analytics techniques, businesses can stay ahead in understanding how to deploy critical business developments in the right place, at the right time.

Other areas of R&D inquiry include strategic location planning at scale, involving networks of stores, competitors, traffic, demographics, psychographics, urban mobility patterns, and other factors. Beyond location planning, manufacturers and utility companies are using digitalized representations of facilities and critical infrastructure to create digital twins. A

54

GIS can then simulate movements of people and parts and track the use of machinery, equipment, and other assets using sensor data. This can help predict equipment breakdowns and facilitate preventive maintenance.

Marketing and Sales Taking customer data a step further, businesses that seek to understand their markets

and audiences for expansion find great insights through location analytics. Descriptive mapping of an organization's customer data provides insights about customer segments that may otherwise be locked up within Customer Relationship Management (CRM) databases and Customer Data Platforms (CDPs). By enriching demographic data with socioeconomic attributes and business data, businesses can segment customers into audiences based on consumer and lifestyle preferences. This can be combined with trade areas analysis to determine locations of new facilities.

Thinking of the typical data a business collects on its consumers in relation to place, many outcomes may unfold based on how the data is joined together. Through geoenrichment, location data, or perhaps in this case – shopping locations--can be enriched with rich attributes such as customer demographics, ability to pay, basket size, time of purchase, and product upsells. When tied to marketing and sales efforts, businesses are now able to deploy trackable advertising campaigns – from the moment a target audience sees an advertising, to the moment the audience crosses a store threshold, to the moment the audience purchases a product.

All marketing and sales outcomes can be measured and tied back to marketing campaigns by utilizing enriched location data to understand consumer behavior. This information is then funneled back to the organization to analyze market behaviors. As it has been found, market behaviors are not always fixed to regional location, but may sometimes shift based on proximity to similar population characteristics, environmental surroundings, and overall neighborhood tendencies. The processes mentioned above can help businesses to identify new customers, based on existing customer behaviors, to consider to cross- or up-sell, and predict the risk of churn. With these insights, a company can decide how to optimize the prices of products and/or services based on spending and income/wealth profiles, determine hyper-local product assortments, and develop mitigation strategies to retain high-risk, yet profitable customers.

Real Estate and Site Selection Location analytics can assist in understanding current and future business locations. One

of the significant objectives of business development is to estimate the future sales potential of markets. Forecasts of sales or any other business KPIs can be modeled using statistical regression models. Such models take into account demographic and psychographic attributes of sites, population density, past sales, and foot traffic, and use them as independent variables to explain current and predict future site performance current and site performance.

At a more advanced level, sophisticated GeoAI-based machine learning and deep learning models can be used for predicting risk exposure and related KPIs. Location intelligence

55

derived from such predictive models is critical for executive leadership in making decisions on capital-intensive business development projects. Such models can also be leveraged to make merger and acquisition decisions and to accelerate strategic expansion in both domestic and international markets. Advanced techniques such as Digital Twins can create a virtual replica of the physical entity or network, allowing for virtual simulations and assessments. Creation of such a virtual site that mimics the characteristics of the real-world site can be manipulated with varying datasets to indicate how to develop blueprints, landscape, wayfinding, indoor navigation, and emergency evacuation.

Operations Operational activities rely heavily on spatial monitoring systems. Tied closely to the

location value chain, operational intelligence allows for businesses to understand business needs on large-scale enterprise levels and also within more granular segments of day-to-day business function. Dashboards inform operations strategies, with descriptive analytics communicating data through mapping and visualizations. Whereas in the past, managing decision making of multiple business locations may have been a more manual process, spatial technology has enabled a new generation of business management practices. As operational data is geo-visualized and analyzed, operational awareness is heightened, and this facilities timely decisions to ensure consistent operational performance.

This data also enables predictive and prescriptive decisions to be made. To aid in employee safety and comply with regulations, data can be tracked over time to predict future occurrences. Operations managers may see safety violations regularly occurring within a specific time frame, within a specific area. Perhaps spills are regularly occurring on a particular platform or equipment is consistently damaged when accessing a particular route, thus lowering productivity. Spatial manipulation of data can predict the likelihood of occurrences to happen and can help to price out circumstantial risk. Predictive analytics can demonstrate the likelihood of the occurrence to happen again and prescriptive analytics will inform the optimal solution, operational decision makers can mesh spatial techniques together to understand why violations are occurring and how to solve the issue from resurfacing again.

Supply chain network visualization, transparency, and product traceability are often informed by detailed network maps, while supply chain resilience – an issue of great importance in light of the COVID-19 pandemic, can be modeled using geostatistical approaches. Modern supply chains are complex and consist of a network of facilities. Facility locations along a supply chain and overall supply chain network design can be informed using optimization approaches which can also facilitate efficient routing and navigation of people and assets. This is critical for business given the rapid growth of e-commerce deliveries. Spatial mapping and modeling of risk for situational awareness, real-time tracking and monitoring of assets, people, and processes, as well as potential hazards. Business disruption can be prevented by accurately and spatially modeling risks, frauds, and other disruptive events. This can help generate risk mitigation strategies that ensure business continuity, enhance disaster preparedness, and ensure regulatory compliance.

56

Corporate Social Responsibility The value of the company in the community is a critical component to contemporary

business environments. For Corporate Social Responsibility (CSR) elements, timely information is needed across a broad range of areas, including diversity, community impacts, and environmental performance. Mapping business and community data to tell a story of the economic impact of the business in various locations. Diversity data can be spatially analyzed to track the diversity in the company relative to the surrounding communities. Dashboards can be utilized to understand and predict the level of corporate environmental sustainability. Prescriptive analysis can be done to guide efforts during a public health crisis, such as has been experienced during the COVID-19 pandemic.

Though aspects of CSR requires data beyond the company’s data, when integrated he output may be informative and groundbreaking. Spatially understanding a business's impact on a local community, exploring matters at the heart of employee culture, and deploying location solutions set to an ethical standard example, can positively impact the sentiment in which a business leaves on its customers, which will affect loyalty of a brand. Spatial knowledge leads a business to make impactful decisions, that with the support of a community, can do great things for all – from disruptive change to devoted customer communities

It may feel like a microscopic lens has been placed on the behaviors of the 21st century business. However, this lens has forced necessary change which has impacted the planning and management of operations. Whether it is the changes within the natural earth, such as climate or event hazards, the changes in economic security, or changes in overall resources to combat health and human services impacts; businesses have an opportunity to create “shared value” solutions to these challenges.

To conclude this review of the location value chain applications, it is important to note that the value proposition of location analytics depends on the breadth and depth of applications. Broad application, spanning multiple business priorities, and deep analysis using sophisticated, context-appropriate combination of descriptive, predictive, and prescriptive location analytics is likely to maximize the value of location analytics. This can shape decisions and actions in different parts of an organization spanning multiple levels of the organizational hierarchy facilitating enterprise-wide spatial transformation.

The analytical methods underpinning these applications are key; they may range from simple descriptive mapping-based visualizations, smart mapping, and geo-enrichment with external data, to sophisticated dashboards that enable reporting, disclosures, or organizational deliberations. Predictive models that range from traditional time series, regression models to more sophisticated geo-artificial intelligence-based (GeoAI) based machine learning and deep learning models are increasingly popular as data science teams in organizations mine structured and unstructured geospatial big data to uncover and decipher patterns and relationships among dozens of variables. Finally, optimization models such as location-allocation, demand coverage, product mix, vehicle routing are informing site selection, last mile logistics, supply chain optimization, and related strategic, tactical, and operational decisions and actions. Alongside, these methods, it would be remiss to not reiterate the critical role of location data

57

(internal and external to the organization) in framing, developing, testing, and validating location analytics models and approaches.

Closing Case Study: John Deere As the global population continues to expand, sustainably growing enough food to feed

every person on the planet is a fundamental challenge. The challenge of increasing farming productivity while at the same time addressing climate change and extreme weather conditions confronts not only farmers but also manufacturers of farming equipment, such as John Deere.

Founded in 1837, John Deere is well known as a global manufacturer and distributor of a full line of agricultural, construction, turf, and forestry equipment (Deere, 2021). Headquartered in Moline, Illinois and operating in 27 countries with a corporate presence in 19 US states, Deere operates 23 manufacturing plants, with more plants overseas. Deere products are sold by its vast network of dealers in over 100 countries (Deere, 2021). A cornerstone of John Deere's is advanced location analytics across its range of products and services (Esri, 2020).

A major area of development has been "precision farming" (Deere, 2022). Now, with IoT sensors embedded in John Deere equipment, customers within the global farming ecosystem are now more connected than ever, with the ability to produce enormous volumes of geographic data and imagery. With John Deere equipment primed and ready for the collection and management of massive data streams, the company is able to derive location intelligence to build better products, provide customers with the tools to streamline farming operations, and support the farming community with sustainable development efforts (Kantor, M., & van der Schaaf, F.,2019, Deere, 2022a). Agribusinesss intelligence translates to increased efficiency and productivity to farm and cultivate crops, with attention to granular details for process improvement and product enhancements.

Location Intelligence for R&D in Precision Farming As with all spatial problems, having access to timely and relevant data can make all the

difference in running location analytics. Data as it is streamed onto a dashboard will provide descriptive location intelligence, but data that is collected over time and enriched with additional attributes, can advance agribusiness through predictive and prescriptive location intelligence (Esri, 2020). With John Deere integrating data collection into farming equipment, data collection can be automated in the field, and efficiently managed to make better business decisions. A connected equipment network aids customers in planning and managing growing seasons, from precisely planting seeds to maximizing harvest yields (Deere,2022a). John Deere Operations Center (Figure 3.5) is activated by field data which can indicate overall equipment performance and inform research and development efforts.

58

Figure 3.5 John Deere Operations Center, monitoring and directing operations, and aiding research and development

(Source: John Deere, 2022b)

In today’s sustainable precision farming movement, farmers need access to reliable geo- referenced information. To gain intelligence, a variety of data is brought into GIS to analyze and make descriptive, predictive, and prescriptive decisions. This data may focus on the weather and environment. It may look at soil type and nutrients, precipitation, groundwater level and runoff, air pollution, and other factors (Deere, 2020) . In this, farmers can make informed choices to maximize limited budgets and predict change over time across vast areas of farmland. To help farmers georeference field data, analysts at John Deere collect Landsat satellite imagery that is useful for understanding changes in the land over time. Every few days, farmers can see, for example, if flooding due to a rainstorm has damaged a certain crop, or determine if additional fungicide needs to be applied in a certain area (Esri 2018).

John Deere Operations Center then leverages immense volumes of satellite imagery and weather data to enrich the customer farming data (Deere, 2022b). The imagery provides location intelligence which may then be mined using artificial intelligence and deep learning algorithms. As data is mined, intelligence that is uncovered may include optimal planting time and growing time for a given location. This intelligence may indicate which type of crop to grow each year and help to determine the type of farming equipment that may be required to produce a quality harvest. Farmers can intuitively take action based on their location, knowing the precise level of nutrients to put into the soil, the correct amount of water to release, how much fertilizer, seed, and tillage is needed and when the optimal time is to take action. This ensures sustainable use of resources, maximizes productivity, and minimizes soil erosion and chemical damage to the subsurface, thereby protecting precious farmland for future generations (Esri 2018, Deere, 2020).

Location intelligence also facilitates predictive maintenance of farm equipment. Deere’s equipment and machinery consist of parts sourced from various plants all over the world. Its advanced telematics systems remotely connect agricultural equipment owners, business

59

managers, and dealers to agricultural equipment in the field, providing real-time alerts and information about equipment location, use, maintenance, and performance (RPMs, oil pressure, etc.). In the event of breakdown, locations are determined. For example, did the equipment break down over a steep hillside? Is a cluster of breakdowns tied to a particular location? In one such instance, there were repeated issues with fuel pumps. After analyzing location data, analysts determined that fuel coming from a local refinery was the culprit, adversely impacting a critical component within the pumps (Esri 2020). Accordingly, measures were taken to service equipment proactively before failure occurs.

Location intelligence for business development and sales John Deere is a technology and data company as much as it is an agribusiness company

(Deere, 2021). Using a location-scientific approach, Deere’s data science team examines thousands of variables during the early stages of market studies to identify geographic areas of potential growth (Kantor and van der Schaaf, 2019, Esri, 2020) This includes land cover analysis using satellite imagery, which helps decision-makers estimate how various grasslands, crop fields, or lawns correlate with consumer purchases in rural communities. Ultimately, about 20 variables such as land cover, historical customer sales, income, demographics, existing dealers, competitive dealers, and distances to all of those features are incorporated into an AI-driven regression model to predict the commercial potential of US Census blocks. The predictive AI models also factor in market characteristics, for example, the presence of more lawns than crop fields in a target market, to refine sales forecasts (Esri 2020)

Location intelligence for real estate strategy and store operations To help dealers expand their retail footprint and build additional dealership locations,

Deere’s data science team provides a wealth of psychographic insight for target locations. With products that range in price from $1,600 to $600,000, customer segmentation is a must—both in the private user segment (lawn and garden maintenance) and the commercial customer segment (golf clubs, sports facilities, etc.) (Kantor & van der Schaaf, 2019). Psychographic analysis is deployed to determine the lifestyles of potential consumers so that dealers can make site selection decisions and determine appropriate marketing channels for their potential customers. For instance, online marketing campaigns could be targeted at consumers living in higher-acreage homes in affluent areas, versus direct mail to target customers who prefer to pay bills in person and avoid using the internet for financial transactions.

In addition, depending on location-based psychographic intelligence, dealers can stock appropriate products and product mix at stores. Because any given Deere product line could be arranged into tens of thousands of configurations, detailed location intelligence regarding consumer preferences can help Deere’s product marketing group, sales leadership, and dealer council steer customers towards an optimal set of product configurations for the local market. This can help dealers avoid inflated overhead costs due to expansive product lines without disappointing customers or sacrificing profits (Yunes, Napolitano, Scheller-Wolf, and Tayur 2007).

60

Environmental and Societal Elements Climate change is one of the greatest threats of the 21st century. Scarce rainfall,

extreme droughts, and shrinking farmland are becoming commonplace. Despite slowing population growth, the United Nations projects global population to approach 10 billion by 2050. To combat challenges to food security and prevent hunger and malnutrition, smart precision agriculture that is environmentally sustainable is critical to maximize crop yield and produce enough food while preserving the land for future generations of farmers.

Location intelligence at John Deere is poised to catalyze innovation in every part of the company's value chain impacting farmers, dealers, and consumers while transforming the company to a techno-centric sustainable agribusiness (Deere, 2020). As farm land contracts worldwide and farmers age, spatial intelligence is central for a newer, younger, and technologically-savvy generation of farmers to make sure that their farms are operating at maximum potential at sub-inch accuracy with optimal use of scarce natural resources and minimal use of fertilizer, fuel, herbicides, and pesticides. Among other benefits, location intelligence is expected to help the new generation of farmers meet the challenges and needs of the "business of modern-day farming" in which they have to wear multiple hats - those of brokers, bankers, chemists, agronomists, and technologists, all at the same time (Esri, 2018). In each of these roles, John Deere is leveraging geo-AI based modeling approaches to help farmers improve yield, increase productivity, lower costs, and achieve more precision while factoring in shifts in weather patterns and other uncertainties such as commodity prices and grain prices, sometimes a year or more in advance of the growing season (Deere, 2022a).

While advancing precision farming and agriculture, geo-AI powered innovations are poised to assist farmers to become stewards of the land through data-driven decision-making and strive for a symbiotic relationship between those linked to the land and the land itself. Using location as a guiding principle and advanced location analytics, John Deere is enabling sustainable precision farming that is integrated with the environment while driving growth and profitability for all stakeholders.

61

SPATIAL BUSINESS: COMPETING AND LEADING WITH LOCATION ANALYTICS

PART II

62

CHAPTER 4

Growing Customers and Markets

Introduction Understanding customers and markets has always been a key to business success.

Whether a firm's customers are individual consumers or other businesses, understanding their needs, preferences, attitudes, value propositions, priorities, challenges, and purpose provides insights about customers that can inform the development of a differentiated business strategy compared to peers and competitors.

Many of these attributes of customers are tied to location. In the case of individuals, where they live, work, shop, engage in professional or social activities provides location-specific insights to companies about their lifestyles and consumer preferences. This is especially key at a time when there is an unprecedented acceleration in e-commerce growth. This has resulted in shift from physical stores to online shopping. This digital-first shift is impacting many consumer- facing industries, creating competitive advantage for some, while the laggards are left scrambling.

When the customer itself is a business, the geographic locations of their operations, key business assets such as personnel, spare parts, and inventory, critical facilities along the supply chain (for example, warehouses and distribution centers) relative to service and fulfillment territories provides clues about processes and workflows, allocation of resources, prioritization of key business objectives, risk tolerance, and overall business resilience. During the COVID-19 pandemic, the lack of a location-based view of business operations, supply chain locations, network connectivity, and other contributing factors has exposed loopholes in business strategy and severely disrupted business continuity in organizations across several sectors and industries.

Understanding Business Markets As introduced in Chapter 2, industry clusters play a critical role in individual business

growth as cumulated growth of the regions they operated in. (Porter 1998a; Porter 1998b) The Porter cluster is based on competitiveness of firms within a geographical unit, that could be a state, region within a state, or metropolitan region. It is dynamic -- clusters sprout up and do not necessarily last forever. Internal or external forces can lead to the decline or death of a cluster (Porter, 1998a).

Industry clusters are defined as a geographic agglomeration of firms and institutions in a similar economic sector, interconnected in multiple ways (Porter 1998a, 1998b). Businesses and institutions in clusters share infrastructure and often a shared pool of human resources. They relate upstream to a common set of suppliers and downstream to related companies and institutions, such as universities, think tanks, trade organizations, and specialized government

63

offices. Porter’s regional cluster constitutes an agglomeration of firms of regional, national, and worldwide impact of such strength overall that the cluster is considered a world leader

A variety of cluster mapping tools have been developed to help business understand we certain clusters are developing. This includes a cluster mapping tool developed by the Harvard Business School in collaboration with the Economic Development Agency (Porter, 2021) (Figure 4.1)

Figure 4.1 Employment Specialization in IT and Analytical Instruments Cluster across US Counties, 2016 (blue indicates counties with high employment specialization and share, green indicates counties with high employment specialization, and yellow indicates high employment share)

(Source: U.S. Cluster Mapping Project, 2021)

64

Figure 4.2 San Diego’s Business Clusters, 2021

(Source: Author)

Data from this tool can be enriched by other locational data. For example, the University of Redlands has created an enriched map of San Diego Industry Clusters using Esri’s Business Analyst (see Figure 4.2). This enrichment, for example provides more economic and community layers to be assessed as well as more about the cluster including data on individual firms in the cluster.

The Porter cluster concept has bearing on spatial business. First, spatial business involves decision- making on locations of companies, and that is influenced by the draw of being located in or near an industrial cluster. Among the reasons to do so is the marketing benefit. Marketing may gain potency by emphasizing, for example, “Silicon Valley firm” or “Hollywood talent agency.” Second, for leading businesses located in a cluster region, spatial business marketing can be strengthened by the dynamics, visibility, and expanded customer target pools associated with the cluster.

Environmental Scanning Environmental scanning is the process of obtaining, examining, and disseminating

marketing information for tactical or strategic objectives, such as improvement of competitive

65

position by analyzing competitor supply chains, determination of where to offer insurance by examining insurance risks throughout a region, and advancement of R&D by deciding on where and why to locate a new R&D center by assessing labor markets. Environmental scanning can be done once, several times, or continually. It is achieved by the use of descriptive analytics, often involving locational analytics, as described in chapter 3. Companies justify environmental scanning as providing a view of the current status of markets, yielding information that can be applied to company strategy and decision-making.

Figure 4.3 shows three levels of scanning: internal to the organization, in the immediate industry environment, and in the macro environment of external factors and forces broader than the industry (Kumar 2019). Scanning a company’s industry environment includes gaining current information on the firm’s stakeholders such as customers, suppliers, partners, and investors, as well as its competitors.

Environmental scanning extends to the macro environment within which the company does business, including political, economic, social, and technological factors (Kumar 2019). Scanning of all these elements can involve location.

Figure 4.3 A graphic of environmental scanning has the organization at the central core, surrounded by a ring of the industry environment, outside of which appears the macro environment of political, social, economic, technological, forces

(Source: adapted from van der Heijden, 2002)

Request this Figure to be re-drawn

Environmental scanning is also used by firms in developing nations to expand their customer base. For instance, in India, location-based environment scanning is done by companies that seek to send goods into India’s rural villages. Dabur is the dominant world provider of ayurvedic goods and also markets consumer product staples, mostly in India but also in 120 other nations. It uses GIS mapping for its environmental scanning of Indian demographic data from government sources and private vendors, surveys of indicators of

66

community wealth, and data on different groups’ values, attitudes, and behavior (Kapur et al. 2014). In this way, Dabur identified 287 well-off rural districts in ten Indian states as having potential for markets. Each month, the firm focuses its marketing on a new rural district, and deliveries are optimized by using routing features of GIS software (Kapur et al. 2014). Dabur has been successful in introducing its goods to rural areas, topping its original goal of providing service to 30,000 villages within a year and a half of commencing marketing to the rural districts. The initiative has benefited the firm, which now has over 40 percent more business in rural than in urban areas (Kapur et al. 2014).

Trade Area Analysis Knowledge of the target market is an important precursor to expanding the business.

Irrespective of sector or industry, businesses strive to make important decisions on expansion such as site selection, store layout design, merchandise selection, customization, ability to fulfill demand, product pricing, and available workforce, based upon intimate knowledge of target markets. Target markets, in turn, lead to the question of trade areas, and how they can be delineated, described, and modeled through spatial analysis.

In the context of business geography, trade areas define specific market segmentation areas and help better understand the existing or potential customer base. What, then, are the factors that need to be considered to determine the trade area of a business that is considering strategic expansion? A firm's trade area depends on the variety of goods and services offered by the business and also by its proximity to competitors. Different types of businesses have different trade areas, and customers are more likely to travel greater distances to purchase certain types of goods and services and/or buy online with home delivery. In order to strategically expand, businesses need to estimate the market or trade area of a store or fulfillment center at a specific site by factoring in the geographic distribution of existing customers, potential customers within a defined service / delivery area that encompasses the site, and potential competitors.

Popular and intuitive methods of delineating trade areas involve radial distance-based concentric rings and irregular travel-time-based trade area polygons, as shown in figures 4.4 and 4.5. Such trade areas are based on customer spotting (Applebaum 1966) and business's trade area can be divided into primary, secondary, and tertiary (or fringe) areas, which are determined by the distance from the firm's site. From the standpoint of a business, the primary trade area is key. To statistically evaluate revenue performance with respect to opportunity, the primary trade area is thought of as the geographic core in which at least 50 percent of the business's customers live and work (Church and Murray 2009). This is the area closest to the store or center of the trade area, as measured by driving distance or by automobile driving time, and is expected to contain the highest residential density of the store's customers and the highest per capita sales by residence locations. Adjoining the primary trade area lies the secondary trade area, from which a store anticipates approximately 25 percent of its customers, followed by the fringe, or tertiary, area.

67

In figure 4.4, two restaurant locations of a quick service restaurant (QSR) chain have reasonably significant difference in daytime population density in their primary and secondary trade areas, defined by 1- and 2-mile distance buffers. Daytime populations in the proposed location’s primary and secondary trade areas, seen on the left, are significantly higher compared to those of the existing store, seen on the right, while daytime population in the tertiary (fringe) areas is not as significantly different.

Figure 4.4 Existing and proposed store locations are mapped, each surrounding by 1-, 2-, and 3-mile distance rings representing trade areas, which are superimposed on a thematic map layer of daytime population densities

(Source: Author)

68

Figure 4.5 shows trade areas for the same two restaurant locations, defined by 3-, 6-, and 10-minute drive times. These drive-time buffers are distorted in certain directions since travel speeds in certain directions vary, depending on time of day, or even day of the week. In fact, in modern GIS software packages, it is possible to refine such drive-time-based trade areas and model them, based upon historic traffic data, for particular times of the day and days of the week, along with direction of travel. This can be valuable for analysis of densely populated, high-traffic urban markets.

Like distance-based trade areas, drive-time-based trade areas may overlap. For example, in figure 4.5, the 3-minute drive-time trade areas of the existing store and proposed location of a new store do not overlap; however, there is slight overlap of the 6-minute drive- time-based trade areas and significant overlap of the 10-minute drive-time buffers. Overlap can alert the manager of the proposed store to strong competition and cannibalization if both stores belong to the same company.

Figure 4.5 3-, 6-, and 10-minute Drive-time buffers are mapped for two nearby stores - to represent trade areas that are partially overlapping, which are superimposed on a thematic map layer of median household income (2021)

69

(Source: Author)

A firm can approximate the customer base within each travel time zone, for example in terms of demographic attributes such as daytime resident population and characterize them, for example in terms of economic attributes such as median household income using simple spatial analysis functions within a GIS such as overlay, union, intersection, querying, and summary statistics.

Often internal organizational data, such as spatially referenced sales transactions, or external third-party data, such as live traffic feeds, can be incorporated within a GIS to geo- enrich trade area zones. Such analysis can be conducted efficiently using GIS software, and the resulting trade area reports, and map visualizations can provide location-based intelligence that informs business strategy. Also, trade areas can be compared to each other, based on the demographic, psychographic, and socioeconomic attributes of customers in those areas, or other local, state, and national geographies and benchmarks. Such comparisons can inform and guide senior leaders as they consider strategic expansion opportunities in competitive markets.

Such environmental scanning of trade areas constitutes exploratory analysis and descriptive location analytics, often the first, foundational step in analyzing trade areas. As the next step, a statistical analysis using regression-based models can incorporate ––

 Demand factors such as population density of trade areas, extent of competition in trade areas

 Site characteristics of an existing/proposed site, such as square footage, number of employees, available parking, easy roadway access and signage

 Demographic, socioeconomic, and psychographic attributes of customers in trade areas

 Geographic attributes such as distances, directions, and elevations

Regression models can predict the brand share of wallet, overall sales, revenue, and profit potential of trade areas, providing guidance for business growth.

Trade area analysis can also play an important role in prescriptive analysis of site location. Consider the quick service restaurant (QSR) with one of the existing restaurant locations, shown in Figures 4.4 and 4.5. That QSR is interested in opening a new location. By factoring in business constraints such as key performance indicator (KPI) benchmarks (for factors such as labor costs), supply capacity constraints, demand requirements, and local zoning regulations, plus geographic constraints such as those imposed by natural barriers (for example, mountains or rivers) that impact mobility and travel times, the QSR can optimize a specific business objective (for example, market potential measured by sales) and select an optimal location for a new restaurant. Prescriptive modeling, using operations research methods, can aid such sophisticated optimal location modeling and is incorporated within contemporary GIS software.

70

Growing Customers It is often said that the purpose of a business is to create and keep a customer. This

section examines the customer side of business growth. Market research and analysis is a critical function across the value chain. The purpose of marketing is to identify, attract, and retain the customer, a goal abetted by today’s technologies, which provide a multi-faceted and continually-updated view of the customer. This is seen in contemporary customer relationship management (CRM) systems, in which the customer can be an individual or a business.

Geo-marketing and Location-based marketing utilize an array of data sets and analytics that can be used to can a multi-faceted understanding of customers. They provide a platform to deliver precise insights on customer segments, interests and location contexts. These insights can both inform new products and services as well as fine tune current strategies. These can also be used to reach new markets and customers. At the same time, some forms of location- based marketing raise ethical-privacy considerations that businesses cannot afford to ignore.

Market Segmentation: Geodemographics Customer Segmenting has been a cornerstone technique in marketing for several

decades. There are varies ways to define segments. Five common segments are demographic, geographic, psychographic, behavioral, and firmographic. The first four are segments for business to consumer (B2B) and the last about firm characteristics for business to business (B2B) marketing. Technological advances coupled with rapid advance of large data sources has allowed companies to combine elements of these segments for a desired on-to -one business to consumer marketing that closely connects with the customer’s journey (Elliot and Nickola, 2021).

One approach to this integration is geodemographics. The foundation for geodemographics was with the 1970 US and UK censuses, which produced, for the first time, massive amounts of computerized information. As censuses have improved over time, so has the availability of accurate and extensive geodemographics, not only for the US and UK but for Canada and several other nations. Today, software can characterize every census tract in the US making it is possible to use geodemographic mapping and further enrich it with social and economic variables to get a more refined view of potential customers.

There are over ten major geodemographics products in the US, several developed in the UK, such as Acorn and Mosaic, and others suitable for developing nations. Commercial geodemographic software will often have from 50 to 80 neighborhood types; Esri’s Tapestry, has 67 distinctive neighborhood market segments, which are estimated by cluster analysis and other statistical methods, based largely on census data. The segments can be mapped at the zip code, census tract, and block group levels. An advantage of having census tracts of 5,000– 10,000 people is that individual identity can be suppressed, protecting personal privacy.

In the US, census data that underpins such categories is mostly accurate but is only collected every 10 years, so a weakness of geodemographics is the ageing of data as the decennial census period nears its end. The model for Tapestry is rebuilt at every decennial census, but the demographic balance and set of constituent neighborhood segments change

71

yearly (Thompson 2020). Updates to the base data imply that a neighborhood may have a shifting geodemographic composition and dominant segment over time.

Another example of a geodemographics tool is Acorn which provides classification of consumers for segments in the United Kingdom (UK) by post code. The post code had an average population in 2020 of 533,000 (CACI, 2020). The data sources for the tool are open data, government data, commercial data, and data collected by the Acorn firm, CACI. Acorn classifies each post code into one of 62 types, which can be aggregated into 18 groups. Groups in include such categories as city sophisticates, successful suburbs, comfortable seniors, student life, striving families, and modest means.

An example of a type is Semi-Professional Families, Owner Occupied Neighborhoods. It applies to 1.2 million adults, which is 2.3% of UK adults. This type belongs to the category, Successful Suburbs. The typical location is “found in villages and on the edge of towns” and “more than average of these couples are well educated and in managerial occupations, while the neighborhoods will contain a broad mix of people.” (CACI, 2020). The geodemographic profiles offer a richer characterization than is possible with a single variable. As seen in Figure 4.6, this type’s annual household income is 47,000 pounds (32,082 dollars), which is above the UK average, and the typical adult age range is 25-34, with two children per household. The profile also provides average financial, digital attitudinal, technology, and housing information. For instance, 42 percent of households stream TV services and 59 percent indicate “I am very good at managing money” (CACI, 2020). This segment can be compared with 67 other types in the full Acorn set.

Figure 4.6 An Acorn geo-segmentation dashboard displays a photo of a typical dwelling for the segment of semi-professional families, as well as the segment’s indicators of the average family financial situation, digital capabilities, and demographics

(Source: CACI, 2020)

As a marketing tool, geodemographics provides considerable insights to businesses and allows for geo- targeted campaigns to customer segments. At the same time, geodemographics

72

has limitations (Dalton and Thatcher 2015; Leventhal 2016). Specifically, in addition to the only once-a-decade public data update, outlier residents are obfuscated. Emphasis on the average profile of a neighborhood misses the outliers, at either end of the scale. This prevents the full perception of a neighborhood. A further subtle issue with geodemographics is “commodification.” This means that naming and branding a neighborhood followed by broadcasting the profile may itself affect its composition and changes. A neighborhood branded and marketed as “City Sophisticates,” for instance, subtly encourages outlier persons to leave and new arrivals to resemble the branded image (Dalton and Thatcher 2015).

In addition to geo-demographic marketing, businesses are devising and implementing more personalized, “relationship” marketing approaches. Business may prefer to segment its customers through its own customer feedback and survey inputs. Another approach is to use the firm’s internal business-transaction data and customer relationship information to segment its customers into “loyalty” categories. Psychographic analysis can also be applied to characterize the behavior and attitudes of customers, yielding an alternative customer segmentation. Some firms combine these segmentation approaches: for instance, Nike segments its customer base by demographic categories, geographic variables such as metropolitan/non-metropolitan, and behavioral variables that emphasize customer feelings about the firms’ products (Singh 2017).

Location Analytics Across the 7-Ps For business-to-consumer (B2C) marketing, the well-known seven Ps of marketing

apply—product, price, place, and promotion, physical evidence, people, and process (Investopia 2019). For B2C, the seven P’s connect with location as follows:

 Product. GIS and location technologies are embedded in many products and services, adding to their value—in services such as delivery, and in products such as cars, cell phones, consumer- level drones, and wayfinding devices, among others. This added value, in turn, enhances the marketing potential of those products and services.

 Price. The pricing of a product or service is based on the real or perceived tangible value. Spatial features of the product/service can enhance or lower value, depending on user perception. For instance, for a wholesale store, location-based inventory and distribution may marginally lower cost, enabling a comparable decrease in pricing.

 Place. Predictive locational analytics can be helpful in choosing where to place a product or service. For instance, some fast-food companies, including Kentucky Fried Chicken, employ geospatial tools to assist in physical placement of a new outlet, considering the locations of competitors, traffic flow volumes and directions, signage of competitors, and socioeconomic attributes of the area.

 Promotion. Promotion is how the customer becomes aware of the product or service. It can occur through public relations, advertising, direct marketing, media attention, going viral on social media, all of which can focus on getting the word out to target markets across geographies.

73

 Physical Evidence. This refers to the physical spaces where customers interact with business representatives. Although such spaces were restricted during the COVID-19 pandemic, they generally include retail stores, customer field visits, meetings, conferences, and other venues. For many companies, use of Salesforce and other customer relationship software provide tracking of physical interactions. CRM software can be linked with GIS software, which can then apply spatial analytics to better understand where a customer has “touch points” with a company’s employees and other channels of physical interaction with the firm.

 People. Marketing professionals who include location intelligence and location analytics in their knowledge and skillset are better prepared to conduct marketing and customer engagement. This enhances the company’s ability to incorporate geographies in better identifying, engaging, and serving the customer.

 Process. This consists of well-designed process steps to provide goods and services to customers, and to influence the customer experience. A process can be made faster, more efficient, and more satisfying to the customer by including steps that utilize location analytics. For instance, the express delivery services by FedEx optimize the process of routing using location analytics which results in faster delivery and, concurrently, it enhances the customer- delivery inquiries by providing the customer with the current tracking location of the package being shipped. The same process enhancements occur with B2B deliveries. As a result, location intelligence has become a competitive aspect for local niches—as in the below example for at- home food delivery in New York City. This example emphasizes the P’s of Product, Price, and Place.

FreshDirect in New York City In the nine boroughs of New York City, citizens live in a dense urban setting and often

prefer not to own their own personal transport vehicles. It is difficult for many to shop for groceries at full-sized stores, so they revert to local corner markets and small venues. FreshDirect was first to the market as a city-wide fresh food delivery firm and it holds about 63 percent of the market (Boyle and Giammona 2018).

Founded in 2011, the firm ten years later has over $750 million in annual revenue and a 400,000-foot distribution facility in the South Bronx. It dispatches a fleet of delivery trucks that are GPS-enabled (see Figure 4.7) and monitors the fleet through a GIS-based control room that serves to optimize routing and maintenance.

The market, however, heated up during the second half of the teens decade and following. Rearing their heads as competitors to FreshDirect in delivery of perishable food are Instacart, Shipt, and Amazon Fresh. The perishable competitors currently together have 23 percent of the market and are chipping away at FreshDirect’s lead. Instacart started up with $1 billion in funding and is taking the tactic of partnering with large supermarket chains in the city (Boyle and Giamonna 2018). Shipt, founded in 2014 in Birmingham, Alabama, and now part of Target, focuses on vetted reliable shoppers who partner with local retailers to procure items for

74

delivery (Shipt 2020). Amazon Fresh, with huge resources, is aggressively seeking to grow in the fresh food market. Because New York City is so densely populated, refrigerated delivery trucks are not always needed, so competitors seek to deliver in under two hours to the market of 9 million people. Spatial technologies are used by all three competitors.

Figure 4.7. A gps-enabled FreshDirect delivery truck rounds a corner in New York City

(Source: Krendra Drischler)

FreshDirect’s new fulfillment center includes a control center for inventory and smart routing of its truck fleet, kitchen facilities, nine miles of conveyer belts, as seen in figure 4.8, robotic order picking, and rooms set at temperatures for different product types (Retaildi2018). The center also is linked upstream to a distribution and production network. All this is calibrated to satisfy projected daily market demand (Wells, 2018).

FreshDirect exemplifies how the P’s of Product, Place, and Process are affected by location analytics. Location is embedded in the Product’s servicing, i.e. location-based home delivery. Place concerns the location intelligence determining the location of the fulfillment center and of the geographic boundaries of the service area. Process is the supply chain process which includes the location-based navigation steps in B2B delivery of food by suppliers to the fulfillment center, and following sorting for at the center, the B2C location-based navigation in delivery to the customer.

75

Figure 4.8 A photo of FreshDirect’s automated order fulfillment facility shows automated conveyers, which sort inbound logistics supplies for placement on gps-enabled delivery trucks

(Source: Hiruka Sakaguchi, 2021)

Amazon Whole Foods constitutes the most powerful competition, with automated fulfillment centers, offering New York customers two-hour delivery times through Instacart. There are many dimensions of competition between these rivals, one of which is competing in spatially driven delivery, which depends on integration of each rival’s fulfillment center with inbound distribution networks of available foods. Ultimately the customer will make the decision, and in New York customers are especially hard to please. Effective marketing, assisted by knowledge of customer locations and tastes in this densely distributed and sophisticated customer base, is crucial.

Location-Based Marketing “Rapidly increasing digitalization across industry verticals, growing penetration of

internet & GPS enabled mobile devices, and increasing utilization of consumer data by marketers are the primary factors fostering growth in Location Based Advertising” (LBM) to become a $62.35 billion global industry (GrandView Research, 2020, p.x). Location-based marketing is the strategy that matches opted-in, privacy- compliance location data received from smartphones to points of interest such as restaurants, grocery stores, and shopping malls. Marketers then use this data to create location-based audiences and analytics. Marketers create and reach their desired audiences in order to serve them more relevant advertising and content (Handly, 2019).

76

The three main components to location-based marketing are geofencing, geotargeting and geoconquesting (Handly, 2019).

 Geofencing

Geofencing allows the marketing company to assign boundaries for a geographic area where customers of a certain type are expected to visit. Once the customer is within the geo- fenced area as measured by her cellphone, she will receive consumer marketing messages that are keyed to the expected type of customer for the area. An example would be for a customer who enters a geofenced area for auto dealerships. While in the area, the consumer will receive marketing messages related to the automotive industry and related concepts, in the example, messages for a car brand, car accessories, local travel, vehicular services, or car insurance.

 Geotargeting

Geotargetting refers to doing marketing based on geo-demographic characteristics of an area, a topic discussed earlier. For instance, consumers in a geographic area in the UK with the geodemographic profile shown Figure 4.6 could be geo-targeted by advertising for moderately priced, financial management software. Consumers in a zone that has the geo-demographics of older, retired people who own their homes, could be geotargeted for newspapers, hearing aids, walkers, or in-home healthcare services, and home repair services.

 Geoconquesting

Geoconquesting is trying through spatial technology to pry away a competitors’ customers. This would consist of targeting customers who geographically can be identified as visitors or nearby to competing firms’ stores and, consequently, attracting those competitor- customers to an their store location. For instance, a fast-food retailers have put a geofence around its nearby competitors and once the customers enter the geofence, send them special offers for food at reduced pricing to try to snare them away.

Benefit of location-based marketing A company benefits through location-based marketing by (a) increased access to market

to customers in or nearby their stores, (b) capability to decide on the best geographies for its products or services and target-market those geographies, and (c) market to competitors’ customers with offers to draw them away to become a company’s own customers. At the same time, customers may benefit by receiving mobile advertising for products that are likely to be of interest and by being alerted to shopping or service opportunities nearby their locations throughout the day.

Location-based “on demand” marketing is a type of marketing defined as the use of locational knowledge for marketing efforts. It can involve the internet, mobile devices, and social media, as well as enterprise analytics and desktop/server platforms accessing customer CRM and other data.

77

Location-based marketing has the following goals:

 Input and maintain accurate and up-to-date digital marketing information.

 Map and analyze customer data at varied scales and geographical units.

 Use locational data from business transactions, mobile apps, social media, subscriptions, memberships, loyalty cards, text mining, and web mining to increase marketing success.

 Pinpoint new markets, sales territories, asset locations to stimulate marketing progress and identify what channels are used by which customers to buy through.

The approach used in location-based marketing depends on the goal and customer type. Customers can be typified as local or distant, high or low in extent of internet and social media use, and primarily mobile-user or not. In its smartphone app, for instance, Burger King targeted mobile users who were within 600 feet of a McDonald’s (Kantor 2018a). A Burger King customer located inside this McDonald’s radius and having a smartphone with location turned on was notified that she was able to order a Burger King Whopper for a penny and directed to the nearest Burger King outlet. This marketing approach has been effective in diverting many customers away from the competitor.

Location Based Social Media Marketing Social media has become more and more location enriched, for purposes of marketing

as well as for data-sharing, customer tracking, and varied business analytics. Several types of social media, such as social networking, collaborative projects, blogs and microblogs, content communities, and virtual social worlds, include the use of location.

Locational social media has varied time lags. If it is time sensitive, it can rapidly inform spatially referenced decisions. An example is rapid check-in to a location in Foursquare, read synchronously by others. If it is not time-sensitive, the message has a locational tag and is read asynchronously (Kaplan 2018). By contrast, some messages are neither time- nor location- sensitive—for example, reading an article on a mobile device.

Since social media is mostly accessed on smartphones, the smartphone’s location becomes a source of information that can be tagged on social media, which allows others to identify the location of the smartphone and, by proxy, the sender. On the positive side, locational tagging can benefit the user with useful information about nearby friends, places, products, and services, or allow her to receive emergency notifications. It can also help in monitoring the locations of children or the disabled. With smart phones now expanded worldwide, these tradeoffs are becoming more salient and have led to regulation of locational privacy in EU countries, and, by contrast, to control of content in China and other countries.

Social media on smartphones serves as a major source of location-based marketing information, such as the location-tagged commentary that TripAdvisor and other travel apps receive. Also, in some cases, smartphone users voluntarily act as location-based sensors on

78

social media, email, and texting (Ricker 2018). An example is volunteered geographic information (VGI), a form of crowdsourcing in which a citizen acts as a sensor by identifying a phenomenon at a location and communicating it, adding that information as a map point. VGI has been used in emergency situations, such as hurricanes and wildfires. In terms of the spatial business of marketing, VGI can be a means to build geo-referenced datasets when technical means are too expensive or not yet smart enough. It has been used more in government than in business but has potential to grow for gathering specialized marketing data.

Heineken’s use of social media for a marketing campaign Heineken sought beginning in 2016 to market its beer brand to a target of 21- to 26-

year-old millennial men, who provided real-time suggestions of nightspots that were trending in their cities. A guy could engage with this service, @WhereNext, via Twitter, Foursquare, or Instagram. The digital platform was a response to Heineken’s research showing that its young, male consumers felt they were missing out on nightlife by not being informed. The Twitter- based service sorted and ranked nightspots by aggregated geo-referenced tweets, Instagram photos, and Foursquare check-ins to provide prioritized suggestions (MMAglobal.com 2019). The campaign expanded public perception of Heineken as being hip with social media and youth.

The service allowed followers to have a stream of nightspots in 15 major cities worldwide. The data- gathering work was outsourced to social media users on a voluntary basis, and a complex algorithm was developed by the outsourcer R/GA London to support the sophisticated social media service. It was “able to discover new venues, popups, and parties, which may never have been found via traditional sources” (MMA Global 2019). The highest priority nightspots were summarized in a map form, as seen in figure 4.9. This application was successful as a campaign highlighting the Heineken brand as contemporary and appealing to millennials. It also went along with a broader “Cities of the World” campaign of the company at the time.

79

Figure 4.9 A diagram of a city’s downtown that illustrates five prioritized @wherenext nightspot locations (A - the best - through E) relative to a social media user shown in the center. surrounded by distance perimeters; locations of two more distant nightspots are shown

(Source: Author)

REQUEST FIGURE 4.9 TO BE RE-DRAWN; INSTRUCTIONS FOR ILLUSTRATOR -- Please put a city street map as the "base map" underneath these locations. It does not matter which city is used and the city will not be identified. Alternatively, the illustrator could draw a fictional street map as the "base map." Do not show the layers, just a fictional map.

A different type of locational social media focuses on social networking, i.e., using social media to arrange for meet-ups of family and friends, or for business and marketing meetings. These meet-up services include Foursquare and Meetup LLC, which is now a part of AlleyCorp. Meetup is website oriented and focuses on people arranging group meetings and events.

Foursquare, officially Foursquare Labs Inc., has two offerings, Foursquare City Guide, which focuses on its customers searching and finding their way around cities drawing on ideas from an online community of users. Foursquare Swarm is an active community where users can check in to a location and meet up with friends or business associates, even maintaining a lifelong log of check ins.

Mining locational social media provides a remarkable picture of what topics people are interested in. This goes beyond what is possible with geodemographics, which reflects average sentiment of a group, rather than of the individual. In one example, researchers studied the

80

tweets of London Underground passengers while underway. This study was possible because the Underground has its own Wi-Fi system, conveying tweets that are geotagged and time- stamped (Lai et al. 2017; Kantor 2018b). The goals of the study were (1) to understand the dominant topics of tweets at locations throughout the Underground system, including hourly patterns throughout the day, and (2) to be able to relate these topics to the outdoor advertising appearing near the station exits.

The study showed that popular topics change as an underground passenger moves on a line from the Underground in the center of London to its peripheral arms (see figure 4.10). In the central stations, dominant topics are Social and Business, Food and Drink, Sports and Tourist Attractions. Topics in the intermediate areas include Movies and Shows, while at the end of the Underground network’s long arms, stretching up to 20 km from the center, topics tend toward Work and Home. Dominant topics also relate to neighborhood sites—for example, the sports topic dominates in the two stations near Wembley Stadium, while topics about museums and galleries dominate around the station areas near the Science Museum, the Victoria and Albert Museum, the Natural History Museum, and the Tate.

Figure 4.10 A map of downtown London’ underground station locations portrays, for the city’s areas of egress of subway passengers, the dominant social media topic of the passengers

(Source: Lai et al., 2017)

Hourly patterns of tweets for weekdays reveal a complex pattern of changes in tweet interest from hour to hour, and between weekdays and weekends (Lai et al. 2017). This space-

81

time social media monitoring approach differs from geodemographics in capturing the ideas of an individual and “could become a critical element in measuring and improving the effectiveness of future out-of-home advertising” (Kantor 2018b). Recent reports indicate that in the US, huge numbers of consumer daily pathways are being monitored and recorded not only by well-known tech giants such as Apple and Google but also by specialized providers that sell the information to other companies, activities that are legal as long as the consumer assents to her location being turned on (Valentino-DeVries et al. 2018). The ethical aspects of this type of data collection are debatable.

Privacy Issues Related to Markets and Customers While there is rapid growth in location-based marketing, there are also ethical issues

associated with its use. This section expands on the challenge and need to maintain location privacy. Prominent among them is customer privacy. The consumer included in a location- based marketing database might have little or no knowledge that her personal information is included and thus lose control of this data and the purposes for which it is used. A sub-industry has developed in the US that sells location-based databases of potential customers. The sub- industry enterprises mostly strive to maintain the personal privacy of the information. A related issue is the challenge of protecting the security of locationally private information. Hackers have broken into databases of private companies containing customer addresses and even into US federal government databases, stealing personal information that they can geo-locate.

In the US, there is no federal legislation to uniformly protect personal privacy information (PPI) with associated geolocation data (Boshell 2019). However, some states, including California, Massachusetts, New York, Hawaii, Maryland, and North Dakota, have put laws into effect that restrict access to PPI for extraction and selling (Green 2020). In California, a business is not permitted to sell a consumer’s PPI without giving visible notice and allowing the consumer to opt out. Also, US federal law has specialized restrictions on PPI for certain sectors, such as healthcare (HIPAA regulations) and data of the US Federal Trade Commission (Boshell 2019).

While the question of protecting personal information and locational data is being debated in US courtrooms and boardrooms, the European consumer already has greater protection. Under Europe’s General Data Protection Regulation (GDPR), passed by the EU Parliament in April 2016 and put into effect in May 2018, personal information is protected unless the consumer opts in. Privacy incursions without prior assent are subject to large fines.

Social media tagging raises ethical questions because the user might not be aware that it has taken place (Angwin and Valentino-DeVries 2011). The exposure of an individual’s location without her consent is a violation of personal privacy, as discussed in chapter 4. And even if the user is aware of the tagging, social media and technology companies may still share and monetize the data (Valentino-DeVries 2018a).

Other ethical concerns are highlighted in a study of the use of the Foursquare app for locating retail outlets in Kansas City, Missouri. The study found that only about a third of Kansas

82

City’s 2,668 accommodation and food outlets were available on the app (Fekete 2018). The deficits in access represent a digital divide and raise questions of corporate social responsibility.

Nonetheless, social media and GIS are converging, not only for a variety of personal uses, but also for business marketing purposes, as social becomes a larger advertising and marketing channel. Although dynamically changing, location as part of social media is here to stay as a potential source of locational value.

Closing Case Study: Oxxo Mexico Oxxo is a convenience retail chain in Mexico, with thousands of stores all over the

country, and increasing market penetration aided by rapid growth, not only in Mexico but also in South America (Chile, Colombia, and Peru). Oxxo’s 18,000 stores and gas stations (2018) in Mexico and South America are part of a vast network of stores and gas stations. Oxxo served 120 million customers in Mexico and the company employed approximately 225,000 people in stores, gas stations, and distribution centers in 2018. An Oxxo store (shown in Figure 4.11) carries an average of 3,200 SKUs such as food, beverages, mobile phone cards, and cigarettes (FEMSA 2018). With the acquisition of fast-food restaurant chains such as Gorditas Doña Tota and the introduction of financial and payment services, Oxxo’s store-level business has diversified over the years to cater to customer needs for convenient, efficient shopping experiences.

Figure 4.11 A photo illustrates the typical storefront of an Oxxo convenience store in Mexico

(Source: Author)

Understanding markets and customers Starting in the early 2000s, Mexicans increasingly shopped at convenience marts on

their way to and from work. This trend reflected a rise in two-income households as well as increasing traffic in densely populated urban areas. As consumers became more time-poor,

83

they demanded convenience and flexibility in their shopping experiences and were drawn to bright aisles, longer hours, and varied product selections in convenience marts, compared to mom-and-pop corner stores or street concessions.

Location intelligence has been at the core of Oxxo’s expansion and sustained growth. Spatial thinking at Oxxo stems from the need to continue to enhance its value proposition— provide proximity, accessibility, and convenience to its customers. With GIS, Oxxo’s location intelligence team conducts demographic and psychographic analysis of its markets to better understand its customers and drive decisions on the type of store to open in its trade areas.

Location differences drive a differentiated retail approach Store segmentation is an important strategic function of GIS-based location intelligence

at Oxxo. For example, Oxxo uses GIS to map population densities, income, traffic patterns and directions, the rate of local car ownership, and shifts in demographics in the new markets (Elliott 2019). This helps executives decide if small convenience stores for on-the-go purchases, larger outlets similar to grocery stores, or stores that combine both formats are appropriate for certain markets. In other words, based on location differences between markets, Oxxo executives decide the type of store appropriate for a market.

Location analytics provides Oxxo’s real estate and expansion team with location intelligence on sites previously deemed unprofitable, for example, niche stores in smaller spaces at airports, train stations, and other such locations (Sandino, Cavazos, and Lobb 2017b). As GIS drives store segmentation depending on market conditions and differences, product placement in stores is optimized and appropriate SKUs are introduced, depending on local consumer preferences, yet another manifestation of location differences and location context.

In addition to store segmentation and product customization, location differences have informed Oxxo's differentiated retail approach for its store locations. Location strategists at Oxxo realized that, as much as on-the-go customers visit Oxxo’s stores to take advantage of one-stop convenience—for example, to purchase a quick drink, grab a prepared meal, or buy a household product—they would value additional services. Accordingly, Oxxo introduced services such as diverse banking, by partnering with ten banking institutions; cash remittances; in-store bill payment of phone and electric bills; prepaid gift cards for streaming online services; and replenishment of calling cards. By 2016, 70% of daily cash at Oxxo stores came from financial and payment services, with the rest from sale of merchandise (FEMSA 2018). Oxxo has also provided a solution to Mexican consumers for whom there are significant barriers to online shopping, by entering into a “click and collect” partnership with Amazon that allows customers to securely pick up their Amazon packages at their local Oxxo store (FEMSA 2018).

Location intelligence drives business expansion The first Oxxo store opened in Monterrey, Mexico, in 1978. By the year 2000, the

company had almost 1,500 stores in Mexico, which ballooned to 10,600 stores in 2012, and finally 18,000 stores in Mexico, Peru, Chile, and Colombia by 2018, when it became Mexico's largest retailer. On average, a new Oxxo store opens every six hours, and the company plans to

84

responsibly expand its retail footprint by opening approximately 1,300 new stores per year, with a goal of 30,000 stores by 2025.

Each Oxxo store is part of a geographic and strategic unit called a Plaza. In 2020, there were 52 such Plazas, each of which has an expansion team of 5–6 people, led by an expansion manager, responsible for the performance of stores in its trade area. The expansion manager collaborates with hundreds of field workers as fieldwork is an important component of Oxxo’s expansion strategy. Fieldworkers provide expansion managers valuable guidance about local needs and business conditions. As part of its workforce, Oxxo employs “brokers,” who collect information on potential sites for stores using mobile data collection apps. This authoritative data is uploaded to form layers in Oxxo’s GIS and ultimately used at the operational level at Oxxo’s Plazas.

Business expansion at Oxxo is informed by location intelligence - particularly spatial statistical analysis. Oxxo’s GIS includes spatial layers of locational data on various demographic and socioeconomic attributes, and complementary or competing businesses such as grocery stores. Other layers include hospitals, schools, malls, and other generators of business activity (location linkages) that are part of the trade area (usually 300 meters from an existing/planned store location). As a whole, these layers provide location context and the foundation for forecasting models of sales potential of an existing trade area (figure 4.12), generate sales forecasts, comparison sales potential of potential market opportunities, and assess the risk of cannibalization. Ultimately, using location analytics, executives at Oxxo are able to make faster decisions about store openings in new markets.

Figure 4.12 A mixture of images portrays Oxxo’s location analytics, such as maps of drive times and store segmentation and a screen shot of a user request for store characteristics

(Source: Esri, 2019)

85

Location intelligence at Oxxo: The future Oxxo’s sustained growth has been marked by an intimate understanding of the Mexican

convenience retail landscape and customers’ wants and needs. From the beginning, proximity, accessibility, and customer service have been hallmarks of Oxxo’s value proposition. To remain flexible and adapt to local customer needs, Oxxo’s department of expansion and infrastructure has prioritized store segmentation, product customization, and an expansion strategy backed by authoritative data and location analytics. Senior leaders of Oxxo are strategic consumers of GIS, driving Oxxo’s continued expansion within Mexico as well as growth in Latin America. Oxxo aspires to diffuse GIS adoption and usage more broadly across the enterprise in departments such as supply chain, for integrated management of a vast network of suppliers. Although GIS adoption and use at Oxxo is not yet enterprise-wide, Oxxo is positioning GIS to support management of digital transformation and enhance its value proposition to customers as consumer preferences continue to evolve in light of the COVID-19 pandemic and its aftermath.

86

Chapter 5

Operating the Enterprise

Introduction Operations is an integral part of an organization's value chain that is responsible for

producing goods and/or delivering services. The creation of goods or the delivery of services requires support and inputs (for example, labor, capital, and information) from other organizational functions. The inputs are transformed to generate outputs (the good or the service itself). During the transformation process, value is added by different operational activities such as product and service design, process selection, selection and management of technology, design of work systems, location planning, facilities design, to name a few.

Intrinsically linked with supply chains, the need to improve business operations stem from competitive pressures to offer an expanding array of new products/services to customers, shorter product development lifecycles, increased demand for customization, increasing customer reliance on e-commerce, and improving the resiliency and transparency of supply chains that are prone to risks posed by climate crises, economic uncertainties, unsustainable and sometimes unethical business practices.

Given the centrality of the operations function in the organizational value chain and its interdependence with supply chain management, this chapter provides an in-depth overview of the role of location intelligence to inform decision-making relative to "operating the enterprise", beginning with operational considerations and then broadening to supply-chain considerations.

The remainder of this chapter is organized into five main sections. Each section explores in-depth the role of location intelligence to –

 Provide real-time situational awareness,

 Monitor operations Key Performance Indicators (KPIs),

 Design efficient distribution systems,

 Optimize facilities layouts, and

 Design resilient and transparent supply chains and logistics systems.

Real-time Situational Awareness Location intelligence is critical for situational awareness - "the perception of the

elements in the environment within a volume of time and space, the comprehension of their meaning and a projection of their status in the near future" (Endsley, 1988, p. 97). In the context of business operations, it is essential to know what is happening when and where. With

87

distributed networks of assets, facilities, and infrastructure, breakdowns can happen anywhere, any time, and in many economic sectors, most of the workforce may be mobile. Organizations in sectors such as transportation, logistics, utilities and telecommunications, to name a few, need to have real-time knowledge of the locations, condition, risks, and performance of their assets to improve decision-making, particularly in emergency situations. Real-time tracking and monitoring of asset location and condition can also improve productivity, prevent breakdowns, ensure safety, and reduce costs.

GIS-powered spatial platforms provides a holistic visual overview of the performance of a system—people, assets, sensors, devices, and other internal assets—which may be affected by external factors such as weather, emergencies, or network and technology disruptions. Using dynamic maps, apps, and dashboards, firms can track movements and changes within a system in real time and ensure that both field personnel and office staff use the same authoritative data. This can help an organization boost data accuracy, reduce errors, adopt automated workflows, and improve efficiency.

Location intelligence can also provide guidance for the dynamic navigation of field assets (people and vehicles), reducing travel time and ensuring that service time windows are honored. In the event of breakdowns or emergencies, location intelligence can reroute drivers and vehicles, ensure safety, and maintain timeliness of operations. Beyond navigation, location intelligence can help trigger predictive maintenance or interventions such as reducing the temperature of mobile trucks transporting perishable goods or medical supplies.

Technologies such as AR and IoT-based sensors and devices complement location intelligence in providing real-time situational awareness. Using IoT-based sensors mounted on infrastructure and assets, both above and below ground, additional streaming data may be generated on KPIs of assets. All this data can be managed, organized, and geoenriched within a GIS for visualization and analysis to provide situational awareness in real time (Radke, Johnson, and Baranyi 2013).

Real-time situational awareness at an Electric Utility Sulphur Springs Valley Electric Cooperative (SSVEC) is a distribution cooperative

providing electricity to consumers measured by more than 52,000 meters over 4,100 miles of energized line in southeastern Arizona. The cooperative’s service territory covers parts of Cochise, Graham, Santa Cruz, and Pima Counties. Apart from cities such as Tucson AZ in Pima County and Sierra Vista which is a medium-sized city with urban and ex-urban areas, other parts of SSVEC’s service areas are largely rural and include meters that serve agricultural areas. As a medium-sized not-for-profit entity with 175 employees, SSVEC’s annual revenues, generated predominantly via sale of electricity, have grown steadily at 2-3% per year in recent years. SSVEC’s highest annual load in a year is a moderate 250 instantaneous megawatts.

Agricultural meters account for 50% of SSVEC’s electricity sales, another 45% is residential. The balance is commercial, industrial, other use categories. SSVEC’s service area in southern Arizona's high desert (about a mile above sea level) has high soil quality making the

88

region attractive for agriculture, but it also needs a lot of water. In these agricultural areas, the primary use of electricity is to power irrigation equipment

In the nineties, engineers and technicians relied upon paper maps to guide field operations. For almost a decade, service technicians utilized map printouts to navigate to customer locations, and find poles, transformers, and meters for routing and emergency maintenance and repairs. The first major GIS initiative in 2003 centered on creating an exhaustive inventory database of field assets to reduce response times during power outages. This was especially critical for field crews deployed to conduct repairs in the middle of the night when it is hard to locate power lines located 1,000 feet off a dirt road in a mountainous area. In 2008, SSVEC’s crews started using tablet computers and subsequently mobile devices to access digitized maps when conducting field maintenance and repair.

Presently, SSVEC’s enterprise GIS, equipment at any given moment, has approximately 1,000 open work orders that reflect some change to the asset infrastructure and power systems. Some of these involve installing IoT-enabled sensors on critical assets that stream geotagged systems performance data in real time. The company’s operations managers monitor this “health data" of company assets in real time at SSVEC’s command center using the Line Patrol Dashboards (Figure 5.1). In other cases, manual intervention is needed to monitor the health of assets such as wooden power poles. When put in the ground, wooden poles have to be monitored for rotting on a cyclical basis. This work is outsourced to a third-party contractor, which inspects the locations of SSVEC’s power poles.

The dashboard in figure 5.1 is used by the company to plan overhead pole inspections. It shows locations of over 4,000 poles in a part of SSVEC's service area by pole type along with the outcomes of inspections (percent passed versus percent failed) conducted over the past month and year. This provides the company a simple comparison. If there is an uptick in failed inspections for a given month compared to the past year, underlying root causes can be examined along with their location characteristics (for example, if the poles failing inspection are predominantly in one part of the transmission network). In addition, the dashboard shows The number of poles that require inspection are shown along with their type (light poles, primary, and secondary). Depending on pole type and their locations, inspections can be scheduled and inspectors with the right skills can be assigned by SSVEC.

Based on the inspections, SSVEC updates the enterprise GIS layer of assets to reflect whether a pole is serviceable or needs to be replaced. If replacement is needed, the enterprise GIS system automatically produces a work order and notifies the engineering team. Armed with mobile devices (Figure 5.2 shows SSVEC's mobile iOS app for field inspections), repair crews and service technicians see repair orders and inspection status, conduct repairs, and update work orders from their respective field locations. Back at the company command center, supervisors see updated status of equipment and work orders in real time and make adjustments in repairs and technician schedules as needed. Among other benefits, this helps SSVEC reduce overtime and optimize its deployment of service crews. Armed with spatial intelligence that originates in the field, decision-makers in various units leverage SSVEC’s enterprise GIS to seamlessly automate the predictive maintenance, repair, and customer-service decision-making processes.

89

This reduces system failures, manages service inefficiencies, and improves customer satisfaction.

Figure 5.1 SSVEC's Line Patrol Dashboard, providing a real-time view of outcomes of inspections of critical transmission infrastructure along with facilities awaiting inspection

(Source: SSVEC)

Location intelligence for life cycle repairs of electric poles has an important secondary benefit for SSVEC. Because its infrastructure has demonstrable reliability, telecom companies partner with SSVEC and enter into “joint use attachment” agreements to attach their telecom assets and equipment to SSVEC’s poles. These “attachment” locations are also monitored by SSVEC using its enterprise GIS, which allow additional revenue capture to help offset the cost of maintaining the pole infrastructure.

The next planned phase of location analytics innovation at SSVEC is real-time location- based intelligence from the field using a distributed network of IoT-enabled sensors. Outage management is an innovation which allows SSVEC to take phone calls and out-of-power meter messages as inputs to a predictive analysis of where the outage is happening. An improvement over sporadic phone calls from customers, outage management systems use GIS-maintained network graphs to identify locations where crews are needed. Dispatchers manage the lifecycle of an outage from identification, verification, and repair through to restoration. When the outage is verified and later restored, SMS messages are automatically sent to affected customers to communicate the incident status and provide better customer service.

90

Figure 5.2 SSVEC's Mobile iOS App for Field Inspection, showing inspection status of a particular pole along with the option of reporting a failed facility

(Source: SSVEC)

Besides the utilities sector, similar needs arise in other asset-intensive industries such as telecommunications, oil and gas, and transportation and logistics. However, the business need for dynamic monitoring of system performance transcends industry verticals. GIS coupled with other technologies and data such as IoT-based sensors, drones, augmented reality, mixed reality, radio-frequency identification (RFID), and machine learning can provide sophisticated real-time geotagged data and locational insights of considerable business value for operational as well as tactical decision-making in close to real time.

91

Monitoring operations KPIs using Dashboards Monitoring the fluctuations of operations Key Performance Indicators (KPIs), often in

real time, is critical for business continuity. Observing and analyzing the spatial variation of KPIs such as raw materials availability, supply capacities, demand requirements, inventory levels, stockouts, manufacturing productivity, system efficiency, operations startup and shutdown times is central to efficient business operations and supply chains. Diagnosing spatial patterns of customer service needs, including service outages, equipment breakdowns, customer service complaints, quality issues, and on-time deliveries improves customer satisfaction and consequently customer retention.

Operations managers face the challenge of continually monitoring system performance. In a manufacturing setting, this could entail monitoring operational Key Performance Indicators (KPIs) such as:

 Productivity and Efficiency (multifactor productivity, process efficiency, capacity utilization, scrap rates),

 Quality Control (machine downtimes, Mean Time Between Maintenance, startup and shutdown times, breakdowns),

 Material Requirements Planning and Inventory Management (supply capacities, demand requirements, inventory levels, and stockouts on a plant-by-plant basis), to name a few.

 In a service scenario, some generic yet important KPIs closely tied to operations include:

 Business Performance (numbers and types of work orders, on-time repairs and resolution rates, incomplete work orders and their reasons, extent of delays, on-time arrivals and deliveries),

 Sales Performance (Lead conversion rates, revenue by product, channel, and market, sales versus targets), and

 Customer Service (Customer loyalty, customer churn, customer complaints, and Net Promoter Score), among others.

Like manufacturing KPIs, service KPIs need to be tracked on a location-by-location basis to analyze spatial patterns and trends. To track KPIs as well as for monitoring performance and reporting purposes, businesses increasingly rely on dashboards. With the rapid adoption of data science in both public and private sectors, dashboards have become ubiquitous and ranked as the highest-rated type of business-intelligence technology use (Dresner 2019). Increasingly, dashboards incorporate a location component to examine geographic patterns, variations, and trends over space and time.

In the previous section, SSVEC’s line patrol dashboard was described. It enabled the electric utility to monitor the performance of critical assets and perform predictive maintenance. A node utilization dashboard (see figure 5.3) for an internet services provider in the greater Tampa, Florida, has a chart of real-time performance of nodes, i.e. devices actively

92

connected to its wireless network. Maps display the provider's market area including Tampa’s international airport, railroad facilities, a US Air Force base, the University of South Florida, and a variety of businesses, tourist hotspots, and residential communities. The maps, charts, and graphs on the dashboard provide operational insights into historical average bandwidth capacity over the previous 12 weeks.

With approximately 800 nodes servicing this major metro market, operations managers need to closely monitor average bandwidth capacity, compare it to demand, and quickly pinpoint any service nodes experiencing issues that might disrupt service. Maps in the top layer of the dashboard provide detailed locational insights on capacity utilization at various units of geographic resolution. By clicking on any red dot in the node performance scatterplot, an operations analyst is able to pinpoint a node experiencing service issues and connect it to a trouble ticket on the bottom set of maps in figure 5.3, so that field crews can be deployed. Also, by monitoring geographic fluctuations in capacity utilization—a key operational planning KPI— managers can decide to split nodes into sub-nodes for areas experiencing spikes in demand and predict trouble hotspots ahead of time. Among other things, this can affect resource allocation planning, prevent outages, and ultimately improve customer service.

Besides the utilities sector, similar needs arise in other asset-intensive industries such as telecommunications, oil and gas, and transportation and logistics. However, the business need for dynamic monitoring of system performance transcends industry verticals. GIS coupled with other technologies and data such as IoT-based sensors, drones, augmented reality, mixed reality, radio-frequency identification (RFID), and machine learning can provide sophisticated real-time geotagged data and locational insights of considerable business value for operational as well as tactical decision-making in close to real time.

Distribution System Design The design of an efficient distribution system comprised of a network of facilities is a

strategic challenge for many businesses. An efficient distribution system allows businesses to meet the needs of their customers, for example deliver products and services in an e- commerce setting within tight delivery time windows, strategically maintain inventory levels, manage service level agreements, and also reduce impacts to the environment, for example in transportation and logistics settings. Integral to the design of an efficient distribution system is the strategic location of facilities that are going to serve customers and allocating customers to those facilities.

Facilities Location A critical aspect of designing an efficient distribution system is to make optimal facilities

location decisions—for example, locating a manufacturing plant and a set of stores of a retail business to accomplish a desired objective. Such objectives may include maximizing market share, maximizing demand coverage, minimizing transportation or shipping costs, and in some cases, minimizing the number of facilities.

93

Figure 5.3 Operations Dashboard of Broadband Service Provider Showing Average Bandwidth and Average Capacity Utilization, Tampa FL

(Source: Esri, 2022)

Consider a manufacturer that wants to build a set of warehouses to supply or stock stores in a target market so that distribution costs are minimized. Two sets of decisions are involved: (1) where to locate the warehouses, and (2) how to allocate demand originating from stores to the warehouses. This combination of location and allocation arises frequently in supply-chain contexts and has been formulated as an optimization problem, classically known as the location-allocation problem (Cooper 1963). The general planning problem may be stated as follows:

94

Locate multiple facilities in a service area and allocate the demand originating from the area to the facilities, so that the system service is as efficient as possible (Church and Murray 2009).

In a typical location-allocation scenario, some candidate locations are pre-selected and pre-specified. For example, when designing a distribution system as a location-allocation problem, a set of location-specific criteria such as local rent and taxes, workforce availability, labor costs, and distance from highway can help to identify candidate locations, from which the optimal ones can be selected, based on demand efficiencies.

Figures 5.4 and 5.5 illustrate the application of location-allocation modeling to determine new store locations of a retailer with one existing store in the San Francisco market. In the risk-averse scenario, with business expansion budget restriction in mind, the retailer wants to open only 3 additional stores with travel times from 208 demand locations that are not to exceed 5 minutes.

Due to these constraints, close to half of the demand points (115 out of 208) are allocated to the 3 best locations, as shown in figure 5.4, achieving a maximum market share of 33% as determined by the popular spatial interaction model known as the Huff model (Huff 1964). The three new store locations are dispersed and only one is close to the competitor’s locations, as seen in figure 5.4.

Due to the retailer's rather low market share, the company further examines how many stores might be required to achieve market coverage of at least 70%. As shown in figure 5.5, the retailer must open 9 additional stores in addition to its existing store to cover at least 70% of the demand. This analysis provides guidance to the retailer’s senior leadership, which can now prioritize appropriate parts of the market, and also enable the retailer’s real estate team to focus on additional market context factors such as possible co-tenants.

In short, location-allocation modeling is a manifestation of the use of prescriptive location analytics for supply chain optimization, demonstrated here at the demand end of the supply chain. From siting distribution centers and basing the transportation plans of goods on more sophisticated industry-specific modeling of supply chains (Kazemi and Szmerekovsky, 2016), optimization models provide prescriptive decision-making guidance and valuable location intelligence to achieve supply chain efficiency.

95

Figure 5.4 Location-Allocation Model for Retail Site Selection with travel time (not to exceed 5 minutes) & facility (3 additional stores) constraints, San Francisco CA

(Source: Author)

96

Figure 5.5 Location-Allocation Model for Retail Site Selection with travel time & facility constraints (cover at least 70% of the demand), San Francisco CA

(Source: Author)

97

Routing Optimization Leading package delivery and logistics service providers such as UPS and FedEx have

leveraged routing optimization using GIS as a competitive advantage for many years. With the sustained growth of e-commerce and explosion of delivery services (meal delivery, grocery delivery, etc.), there is renewed focus on the topic of routing optimization in a variety of sectors.

Routing of delivery vehicles is a natively spatial problem and routing optimization is at the heart of designing efficient supply chains. UPS’s ORION (On-Road Integrated Optimization and Navigation) popularized the notion that “left isn’t right,” minimizing unnecessary left turns on drivers’ routes (Horner 2016). The system was developed and refined over the years to guide UPS drivers making dozens of deliveries over dense, complex traffic networks. In 2016, 55,000 ORION-optimized routes were documented to have saved 10 million gallons of fuel annually, reduced 100,000 metric tons in CO2 emissions, and saved an estimated $300 million to $400 million in cost avoidance (Horner 2016). The full case study on UPS in chapter 9 sheds more light on ORION’s strategic role in shaping UPS as a spatial business leader.

Another example is Instacart, whose green-shirted “personal shoppers” are now instantly recognizable at our neighborhood grocery stores, especially in the aftermath of the COVID-19 pandemic. As explained in Chapter 4, Instacart competes with Amazon’s FreshDirect, AmazonFresh, Peapod, and services run by big grocery chains to deliver groceries to an ever- broadening base of customers. Instacart deploys sophisticated routing optimization models (Stanley, 2017) to route its personal shoppers and streamline their deliveries. For personal shoppers who make multiple trips to the neighborhood grocery store to deliver multiple sequential orders to customers within tight time windows, routing optimization algorithms factor in multiple sequential trips made by the shoppers to deliver groceries to customers using personal vehicles with limited capacity (in some case, by bikes) within narrowly defined time windows pre-specified by customers.

Over the past several decades, tremendous advances have taken place in routing optimization. Many of these advances have been catalyzed by blending location analytics and optimization modeling. Yet, in real life operations, the quality of a route – often defined by its theoretical length, duration, and cost, can be improved by a driver’s tacit knowledge about the complex operational environment in which they serve customers on a daily basis (MIT, 2021). Location analytics can play a central role in improving last-mile routing in such environments leading to the design of safer, more efficient, and environmentally sustainable deliveries and overall route planning.

Facilities Layout Designing facility layouts is essentially a spatial exercise. Facilities layout involves many

of the same issues of proximity, distance, separation, layering, and organizing that are fundamental in GIS-based location analytics. For single facilities, layout may include spatial problems such as placing aisles within a warehouse, situating job shops in a manufacturing plant, organizing cubicles in an office, locating self-service kiosks within a restaurant, or co-

98

locating products on shelves in a retail store. In manufacturing, efficient layouts can improve the efficiency and productivity of production lines or assembly lines, reducing costs.

For multi-facility networks, such as networks of stores, warehouses, and distribution centers, the layout challenges are more complex. But descriptive mapping of multi-facility networks, along with spatial optimization models based on operations research, can produce optimal layouts in retail, transportation and logistics, as well as in industries such as utilities and telecommunications. For example, in transportation and logistics, location analytics can help design efficient networks that consolidate the number of dispatch facilities and depots, avoid overlapping service territories, and reduce the time spent routing and miles per stop—with the added benefit of narrowing delivery time windows and improving on-time performance. This, in turn, helps to minimize costs, enhance customer service, and reduce emissions.

Indoor GIS for Facilities Layout Design and Management Industry forecasts indicate significant growth in the indoor location analytics segment in

the next five years. The segment is estimated to grow from $3.9 billion in 2019 to $8.4 billion by 2024, at a CAGR of 16.5% (Sreedhar & Bhatnagar 2019). Indeed, maximizing the value of the indoor built environment is increasingly a strategic differentiator for many businesses. Whether for a company campus with multiple connected buildings, a sprawling multi-floor convention center or mall, or a very large facility like an airport, indoor GIS holds promise.

With indoor GIS, companies can track the movement of people, goods, and assets, improve productivity and throughput of the space, enhance situational awareness in real time, and provide navigation and routing services, saving time, effort, and money. At the macro level, indoor GIS can provide location-based intelligence for building layout planning and space optimization. In large public transit facilities such as airports and in healthcare facilities such as hospitals, multi-purpose spatial data is used for asset management including underground utilities, architectural planning, space optimization, and for tracking the movements of goods and people. At transportation hubs such as airports, rail and bus terminals, facility managers can ensure smoother passenger flow and a stress-free travel experience by optimizing the facility layout comprised of parking, check-in, security, duty-free shopping, and ultimately departure/arrival. Indoor GIS is also critical for emergency managers at airports, convention centers, and hospitals to plan and execute emergency procedures that can track both employee and customer locations in real-time to maximize safety and minimize response time in the event of a large-scale emergency.

Indoor GIS at Los Angeles International Airport Consider for example Los Angeles International Airport (LAX) where geospatial data and

indoor GIS is key for understanding and visualizing changes to the built environment such as passenger terminals as well as for infrastructure management so that the maintenance and renovation of critical infrastructure such as runways can be performed efficiently. At LAX, Indoor Mobile Mapping Systems (IMMS) uses LiDAR (Light Detection and Ranging) processes together with 360° imagery (shown in Figure 5.6) to capture large, complex, and dynamic interior spaces as digital, interactive visual representations of data (LAWA, 2019). IMMS facilitates the management of space leased by airlines, freight companies, and concessionaires

99

and ultimately inform the management of tenant lease and rental agreements and related pricing (Stenmark, 2016). In addition to facilities management and serving as a wayfinding platform, indoor maps in LAX’s IMMS leverage business rules, localization and IoT to create a smart airport that enables users to visualize spatial data and create real-time indoor location intelligence.

Figure 5.6 360˚ Indoor Imagery shows Ghost Figures of Passengers and Camera Imagery Location Points (prior to post- processing of imagery)

(Source: LAWA, 2019)

At LAX, indoor GIS provides up-to-date building floor maps and planning maps, facilitates asset management, and forms the basis of a variety of surveys such as emergency evacuation surveys and planning, signage surveys, design surveys, surveys of “as-builts”, and equipment clearance surveys. Indoor GIS also facilitates line of sight analysis which is critical for ensuring security within terminals and other important airport buildings and facilities (LAWA, 2019). LAX’s indoor GIS can be used to identify locations of security breaches, hazmat incidents, equipment breakdowns, and maintenance issues, and deploy the nearest personnel and crews. Using location intelligence derived from indoor GIS applications, command centers can be set up, nearby assets such as surveillance cameras can be queried, non-operating assets can be taken offline, and staff, tenants, and occupants can be evacuated. In short, indoor GIS can be incredibly effective for designing incident management strategies and implementing them in real time. Overall, LAX’s indoor GIS is a hub of authoritative, accurate, and up-to-date data that is used by airport staff as well as by first responders and emergency managers to develop familiarity with the buildings and plan and execute emergency procedures.

Combining indoor mapping with existing geospatial data and workflows provides a wealth of vital information to several different stakeholders – Environmental, Operations, Security, Safety, Commercial, Engineering, Facility Management, HRM and external

100

organizations such as police and fire departments. This facilitates coordination, planning, and airport project management, and accelerates the speed of decision-making. The value of LAX’s indoor GIS is multiplied when the geospatial data collected is mapped, shared, mined, and the location intelligence consequently produced is consumed beyond its initial intended purpose by multiple stakeholders for a wide-range of benefits (LAWA, 2019).

Overall, indoor GIS adoption and use for space optimization and facilities layout planning and design is burgeoning in the private sector. Emerging trends such as coworking and the resulting coworking spaces will catalyze further innovation and investment in indoor GIS. Indoor GIS is well positioned to provide guidance, after the COVID-19 pandemic, in reconfiguring the layouts of workspaces, plants, warehouses, and congregate facilities to ensure workers' health and safety. To safely reopen the workplace and restore employee and customer confidence, indoor GIS is well-positioned to provide location-data-driven solutions. Indoor mapping to reconfigure facilities for safe use and advanced spatial analysis to identify congregation hotspots, proximity, distancing, and separation are all critical to business continuity in the post-COVID era (Chiappinelli 2020).

Supply Chain Management and Logistics At the systems level, location intelligence is required for managing and designing robust

and resilient supply chain and logistics networks. Modern supply chains are critical for business continuity. Consider this extreme example from March 2011, when a magnitude 9.0 earthquake hit Japan, followed by a tsunami. As the disaster unfolded, a nuclear facility in Japan was compromised, resulting in severe radiation leakage. In the days that followed, an international consulting firm forecast that as a result of the disaster, the cumulative production of Japanese automakers would drop by 2.2 million units, compared to 2010, when annual production of Japanese-made passenger cars totaled 8.3 million. Post-disaster, Toyota suspended production in 12 of its assembly plants and estimated a loss of around 140,000 vehicles. Due to keiretsu, that is, interlocked supply chains, disruption in parts supplied by Tier 2 and Tier 3 suppliers affected Tier 1 suppliers, in turn disrupting the entire supply chain.

As parts shortages hit and shipping parts from Japan came to a complete halt, production stopped in Toyota’s US assembly plants. Honda also had to stop its operations as more than 20% of its Tier 1 suppliers were affected by the earthquake. Nissan was the hardest hit and lost 1,300 Infiniti and 1,000 Nissan cars to the tsunami, shutting down operations in five plants for several weeks (Aggarwal and Srivastava 2016). Each day of lost production was reported to cost Nissan $25 million.

In another, more recent example, Wendy’s restaurants all over the US experienced beef shortages as employees at congregate facilities such as meat processing plants contracted and then spread the COVID-19 virus, resulting in communal outbreaks. The price of beef increased significantly and a federal executive order invoking the Defense Production Act to classify meat plants as essential infrastructure became the subject of robust public discourse (Yaffe-Bellany and Corkery 2020).

101

Whether of cars or burgers, the objective of a supply chain is to be efficient and cost- effective across the entire network. Hence, supply-chain management requires a systems approach. Also, since a supply chain integrates suppliers, manufacturers, warehouses/distribution centers, and stores as part of a network, network planning is essential. Network planning is the process by which a firm structures, optimizes, and manages its supply chain in order to —

 Find the right balance among inventory, transportation, and manufacturing costs.

 Match supply and demand under uncertainty by positioning and managing inventory effectively.

 Use resources effectively in uncertain, dynamic environments (Simchi-Levi, Kaminski, Simchi-Levi 2004).

Supply chain network planning involves —

 Network design: Decide the number, optimal locations, and optimal sizes of plants, warehouses, and distribution centers.

 Inventory positioning and management: Identify stocking points, optimal stocking levels, and facilities to stock.

 Resource allocation: Determine when and how much to produce, procure, or purchase and where and when to store inventory (Simchi-Levi, Kaminski, Simchi-Levi 2004).

Consequently, managing a supply chain involves making site location decisions—a natively spatial problem that has strategic, tactical, and operational implications. Alongside this are inventory management and resource allocation issues. All three—site location, inventory management, and resource allocation—have strong spatial connections to business operations.

Spatial Technologies for Supply Chain Management Managing a supply chain effectively starts with combining internal organizational data

with external data from varied sources. Internal enterprise data may originate from CRMs and Enterprise Resource Planning (ERP) systems, bills of materials, business forecasts, schedules, and project management systems. External data may be sourced from various governmental agencies, third parties, business partners, and industry organizations, as well as digital sources such as social media. Use cases may originate in different areas of the enterprise according to business needs such as inventory management, forecasting demand, and generating just-in- time production plans. Other needs may include material requirements planning (operational), territory optimization, route planning, warehouse layout and facilities design (tactical), outsourcing, and site selection (strategic).

As these disparate data streams reside in individual silos, their business use needs, whether upstream- or downstream-facing, are equally siloed. However, as an interface, a GIS acts as an integrative platform between the siloed data streams and use cases, as shown in figure 5.7. GIS enables users across the enterprise with different business needs to collaborate,

102

drawing upon descriptive visualization of georeferenced datasets, which provides the foundation for predictions, decisions, and informed actions.

Figure 5.7 GIS as interface between data silos & supply chain use cases (adapted from Chainlink Research)

(Source: Author)

Digitizing and mapping a supply chain as part of organizational digital transformation can have several benefits. For a global business, it can provide a reliable operating picture of how the supply network chain performs and where it might be failing. In addition, a digital, fully mapped supply chain can deliver valuable situational awareness powered by real-time alerts and notifications, asset tracking, and monitoring. It can also identify geographic stress points that may be prone to risks. This can help accelerate a company’s response time during the normal course of business and enable it to plan, prepare and respond in an emergency.

Concluding Case Study: Cisco Consider the case of global networking giant Cisco, which provides networking

hardware, software, telecommunications equipment, among many other high-technology

103

services and products to its customers. A critical issue for Cisco's customers is network downtime which may be caused by the failure of a hardware product, a part, or any related equipment. When such breakdowns happen and data transmissions are disrupted, customers naturally are not able to access the digital world. Therefore, prompt restoration of networking connectivity, often in a matter of hours is critical. However, the networking equipment or part may not be available in the immediate proximity of a customer. Even if a replacement part were available at a parts supplier located within the firm's territory, logistical issues may come into play that may slow down the actual transportation of the part from the supplier to Cisco's customer. In some cases where specialized training is needed, such as, for hotswapping (replacement or addition of parts or components without shutting down or rebooting a system), another issue may be the lack of availability of a nearby field engineer.

Next, Cisco's global supply chain network is incredibly vast and complex. At any given moment, Cisco services networking and telecom at roughly 20 million or more customer sites in 138 countries. When breakdowns happen at any of these sites, parts are sourced from 1,200 warehouses globally, and a delivery driver is assigned to ship the part from the depot to the customer. In addition, of approximately 3,000 field service engineers (FEs), an individual with the right skills who is within a reasonable service time needs to be deployed. It is important to note that the warehouses are not owned by Cisco, but by third parties. Similarly, delivery drivers, and field service engineers are part of Cisco's vast network of partners. In short, this vast network of business partners adds an additional layer of complexity to Cisco's operations.

To ensure that customer networks are back up and running as soon as possible when breakdowns happen, Cisco has deployed location analytics to produce two- or four-hour service level agreements with its customers depending on the locations of customers and their proximity to parts warehouses and FEs. To do this, Cisco's GIS maps its entire supply chain that produces descriptive visualizations of facilities, and two- and four-hour drivetime polygons (based on local traffic and weather conditions) indicating proximity of customers to parts and FEs. Plotting these locations and intersecting them with drivetime buffers allows Cisco to color- code its customer sites based on whether they are in two- or four-hour service windows for a given warehouse, depending on the type of part ordered.

The process of assignment of customer sites to warehouses is automated. As soon as a part if requisitioned, the automated assignment system recommends the appropriate service level agreement (two- versus four-hour) to a customer and initiates the delivery of a part from the warehouse to the customer site. In addition, the automated system produces real-time reports of parts inventory, oversubscribed parts that run the risk of becoming out of stock, heavyweight parts that may require special shipment, for each warehouse location. Armed with this intelligence, Cisco's partners can replenish inventories at specific locations, or move parts to other warehouses based on local needs. This location-based holistic approach minimizes the risk of stockouts system-wide. Using location analytics, Cisco also assign the correct FE to a customer site, minimizing the lead time between the arrival of a field engineer and a replacement part and providing notifications to a customer when a FE is en route.

104

This use case primarily highlights the principles of location proximity (between warehouses and FEs to client sites) and location differences (differences in inventory portfolio and quantities at warehouses). It also showcases the use of both descriptive and prescriptive location analytics steps of the spatial analysis hierarchy.

Using location analytics, Cisco can solve its customers' most pressing problem: timely and accurate resolution of networking issues that disrupt business operations causing losses, lost revenues, etc. Guided by spatial modeling, the company sells the right service contract to each customer and services them in the quickest time possible by deploying appropriate assets and resources. This enhances their post-purchase experience.

In summary, Cisco's GIS platform provides a common operations platform to its complex and expansive network of customers, parts suppliers, warehouses, and logistics service providers. Using location analytics, the company achieves improved visibility of service territories, eliminates coverage overlaps, removes service gaps, and optimizes the service part delivery network.

105

Chapter 6

Managing Business Risk and Increasing Resilience

Introduction

The chapter is an in-depth exploration of the role of location intelligence for risk management and mitigation in business operations. The COVID-19 pandemic ravaged the world in 2020. Nations around the globe took unprecedented measures to contain the spread of the novel coronavirus by closing national borders, stopping international travel, shutting down schools and non-essential businesses, and implementing stay-at-home policies. The economic fallout has been unlike any in contemporary history and it may take years for communities and businesses to fully recover. Pandemics represent the newest frontier of risk factors affecting businesses, along with economic and geopolitical uncertainties, rapidly changing customer preferences, evolving competitive threats, climate-change-induced shifts in weather patterns, and data/IT security breaches.

From business and operational risk, to risks posed by market factors and competition, to environmental factors, the timely assessment and mitigation of risk is key to sustaining growth and staying ahead of the competition. Strategic risk occurs whenever a business voluntarily accepts some risk to generate superior returns from its strategy. For example, expanding a business into new territories—domestic or international—is an avenue for strategic risk. On the other hand, external risks arise from events outside a company’s influence or control, such as climate change, natural disasters, pandemics, economic fluctuations, geopolitical uncertainty, regulatory environments, and cybersecurity breaches.

In confronting external risks such as geopolitical crises and natural disasters, the lack of a visible plan can render CEOs and their organizations vulnerable. Recent surveys have shown that during such times, two out of three CEOs feel concerned about their ability to gather information quickly and communicate accurately with internal and external stakeholders (Pricewaterhouse 2020). As a framework for gathering, managing, and analyzing many types of data on risk, a GIS may be viewed as a unified source of truth. For example, it can combine layers of internal organizational data on customer locations, store closures, supply network disruptions and compromised infrastructure with public data such as imagery, regulations, restrictions, and mandates from governmental agencies. In addition to organizing data, a GIS can ground-truth data and identify inaccurate data. Dynamic mapping, using real-time location data, can improve situational awareness and help with risk identification and mitigation. This can provide reliable, timely guidance to CEOs and senior leaders needing to mitigate risk by critical decision making in real time.

106

Location Analytics for Risk Management Understanding risks specific to place is key to reliable risk assessment, risk

preparedness, mitigation, and crisis response. In the case of a manufacturer, risk management entails understanding spatial exposure to risk of facilities along the supply chain. In non- manufacturing settings—for example, in transportation, utilities, and telecommunications— physical infrastructure and field assets constitute the geographic exposure to risk. Risk management using a GIS offers location-based insights on emergency preparedness. It also helps companies develop business continuity plans and an overall appraisal of business resilience.

A GIS-based risk management process can be synthesized by the five steps of planning, mitigation, preparedness, response, and recovery, as shown in figure 6.1. At each step of the process, location analytics plays an important role underpinned by the principles of location proximity and relatedness, location differences, location linkages, and location contexts.

Figure 6.1 The Risk Management Process comprised of five steps, each of which uses location analytics

(Source: Author)

 Planning: In the initial planning step, location analytics can be deployed for descriptive visualization of areas, facility locations, and assets exposed to risk. Geostatistical models could predict areas most likely to be affected by a risk, along with the threat level and how risk might propagate spatially.

 Mitigation: Once risk assessment has been completed, protective or preventive actions can be taken in areas exposed to risk. This can be accomplished using spatial analysis in a GIS.

 Preparedness: Preparedness involves planning for asset deployment in areas likely to be impacted, to minimize response times. Prescriptive optimization models could be used within a GIS to identify optimal locations for assets and resources to be deployed for maximum coverage of affected areas, populations, and facilities.

 Response: The response phase occurs after an emergency when business and other operations are disrupted and do not function normally. Response activities might include activation of disaster response plans and implementation of strategies and tactics to deploy location-specific assets to assist, protect, and save employees, customers, properties, and the community affected by an emergency. Layers of

107

geospatial data organized in a GIS can be leveraged to obtain location intelligence that can ensure safe access, navigation to affected people and communities, and timely evacuation. Location-based intelligence can also help local governments and communal organizations, federal and state agencies to obtain a common operating picture ensuring coordination of response activities.

 Recovery: During the recovery phase, restoration efforts occur in parallel to regular operations and activities. Location intelligence can power the timely and reliable restoration of utilities, telecommunications and other essential services, rebuilding damaged properties, and reducing vulnerabilities stemming from diseases and other threats. Location intelligence can also provide guidance on areas where it is safe to resume regular business operations and other areas that require resource-intensive efforts.

Location Analytics for Supply Chain Risk Management at General Motors (GM) GM’s supply chain is a complex network of relationships between Tier 1, Tier 2, and Tier

3 suppliers, globally dispersed. 5,500 Tier 1 suppliers ship parts directly to GM manufacturing plants. Tier 1 suppliers receive their parts from 23,000 Tier 2 suppliers, creating a network of approximately 53,000 Tier 2 to Tier 1 relationships. There are tens of thousands of Tier 3 to Tier 2 connections. The breadth, scope, and complexity of such a vast, geographically dispersed supply chain makes planning, mitigation, and recovery extremely challenging in the event of a disruption. Clearly, the sheer volume of cars, parts, plants, suppliers, and shipments makes the management of GM’s geographically dispersed supply chain a complex task under normal circumstances. In the event of a disruption, such complexity poses enormous challenges for planning, mitigation, and recovery.

GM’s Supply Chain Risk Management (SCRM) platform consists of a comprehensive database of suppliers including location, parts supplied, connections, and information on key contacts. All locations are mapped, and using a tracing function, all connections between plants and Tier 1 suppliers and between Tier 1 and the sub-tiers are established. This descriptive location intelligence immediately reveals –

 Which parts were dual sourced or even triple sourced. This could be vital insight during a crisis.

 The extent of supplier convergence. For example, when one Tier 2 supplier provides parts to many Tier 1 suppliers, it increases risk exposure, should the Tier 2 supplier’s operation be disrupted.

In its supply chain GIS, the company also incorporates a variety of data feeds including weather, local and international news, and so on. This produces 24/7 notifications about current events. In the event of a fire or deleterious weather event, the company’s supply chain GIS prepares and delivers a report with an overview of the event as well as high-level statistics, such as the number of GM plants and suppliers that could be affected and the part numbers involved. This critical intelligence helps GM’s SCRM team answer the following questions to speed up its mitigation and recovery responses:

108

 Which GM plants are at risk? Which vehicle lines do they produce?

 Which Tier 1, 2, and 3 suppliers are potentially impacted?

 Which parts are involved?

 Who are the key contacts at affected supplier sites?

For example, when Hurricane Harvey was projected to make landfall in Houston in August 2017, the SCRM system identified Tier 1 and 2 suppliers in the area and their parts. After reviewing the suppliers likely to be affected, the SCRM team had all Tier 1 suppliers ship parts to GM plants 2–3 days ahead of the hurricane’s landfall. Similarly, Tier 2 suppliers would ship parts to Tier 1 suppliers a few days ahead of schedule. There were two additional benefits.

By implementing a reliable yet nimble SCRM process, GM dramatically improved its supplier footprint analysis to better prepare for risks. As a result, there were significant savings in the company’s Contingent Business Interruption (CBI) insurance coverage, which protects against losses due to supplier issues (Kazemi 2018).

Improved supply chain visibility enabled GM to focus on ethical sourcing of raw materials, including minerals such as gold, tin, tantalum, cobalt, and tungsten. The use of conflict minerals for parts or product manufacturing may tarnish the reputation of a company, and GM’s GIS system has enabled it to remain vigilant about the origins of the raw materials it requires (Kazemi 2018).

Real Estate Risk Management In its 2020 annual report, Macys identified one of its strategic, operational, and

competitive risks as the following: “We may not be able to successfully execute our real estate strategy.” In the same report, Macys further added: “We continue to explore opportunities to monetize our real estate portfolio, including sales of stores as well as non-store real estate such as warehouses, outparcels and parking garages. We also continue to evaluate our real estate portfolio to identify opportunities where the redevelopment value of our real estate exceeds the value of nonstrategic operating locations. This strategy is multi-pronged and may include transactions, strategic alliances or other arrangements with mall developers or other unrelated third parties. Due to the cyclical nature of real estate markets, the performance of our real estate strategy is inherently volatile and could have a significant impact on our results of operations or financial condition.” (Macys 2020, pg. 8).

The development of a real estate strategy is key to business success. A location-based real estate strategy can inform business development, help uncover underserved markets, show strategic growth opportunities, model market saturation, and visualize the risk of competitive threats as well as cannibalization. Such a strategy can produce actionable business intelligence that can inform strategic decisions such as site selection; operational decisions such as store design, pricing and promotions, product selection and placement; and support tactical decisions, offsetting manufacturing risk by efficient spatial allocation of scarce resources. Yet,

109

due to ever-changing business environments, shifts in demographics and consumer preferences, socioeconomic changes, geopolitical volatilities, public health crises and climate change, real estate decision-making in a 21st-century business is riddled with uncertainties.

The Rise of 3D Using GIS as a platform, real estate companies deliver location intelligence to their

clients by fusing together data from multiple first- and third-party sources. Using powerful visualizations, including 3D, real estate companies help clients make sense of data visually to provide a holistic understanding of markets, customers, spaces, facilities, infrastructure, and their spatial interactions. For example, a property management company can visualize a redevelopment project using 3D views in a GIS (figures 6.2 and 6.3) which integrates layers showing streets, neighborhood green zones, parks, and neighboring property types.

By toggling between the alternative 3D views, the real estate team of the property management company is able to visually explore the space, conduct before-and-after comparative analysis of places, and zoom into specific buildings to reveal building data. The perspective can be enhanced further by using location analytics to model transportation access (for example, to underground subway stations and parking garages), access to amenities such as parks and restaurants, and overall quality of life indices, such as noise pollution levels. Advanced forecasting models can also be incorporated within 3D views to provide clients with market potential and relevant KPI estimates based on such factors.

Armed with such 3D visualizations on mobiles and tablets, brokers are able to provide clients with a nuanced, realistic view of market opportunities, advise them about site suitability, and even offer a broad view of profitability. Using advanced 3D information products built within a GIS, the clients of real estate market leaders such as Cushman and Wakefield can enter a space they are appraising and conduct a virtual tour, using a mobile device. Having developed this immersive virtual experience before the COVID-19 outbreak, Cushman and Wakefield considered it indispensable during the pandemic in providing safe, insightful interactions for clients, from afar (Lowther and Tarolli 2020).

110

Figure 6.2 3D view of a fictive redevelopment project, showing the built environment in a Philadelphia city block

(Source: CityEngine, 2016)

Figure 6.3 3D view of a fictive redevelopment project, showing proposed additions to the built environment in a Philadelphia city block

(Source: CityEngine, 2016)

Business risk mitigation and building business resilience Location intelligence can also help businesses analyze risks associated with supply chain

interruptions, which disrupt the flow of raw materials essential for business continuity. Many

111

businesses such as grocers establish layered and complex supply chains sourcing raw materials, such as perishable frozen food items, from geographically disparate suppliers. Any food safety event, including instances of food-borne illness (such as salmonella or E. coli), could adversely affect the price and availability of raw materials such as meat and produce. In addition, food- borne illnesses, food tampering, contamination, or mislabeling could occur at any point during the growing, manufacturing, packaging, storing, distribution or preparation of products. Descriptive mapping with reporting dashboards provides a unified, up-to-date, reliable “source of truth,” producing actionable business and location intelligence.

Business Continuity: The Case for Dashboards Inclusion of mapping in dashboards was recently found to be the highest rated feature

of location visualization in an industry survey (Dresner, 2020). Consistent with this trend, organizations have embedded smart mapping capability within COVID-19 business dashboards to monitor facility status (for example, what factories are open or closed, what capacity where warehouses are operating), identify business segments (customers, trade areas, markets) most vulnerable to risk, develop contingency plans, manage and care for a distributed, remote workforce, improve supply chain visibility, and craft strategies to safely reopen businesses and facilities where appropriate. Such dashboards also enabled businesses to untangle myriad local, state, and national regulations and guidelines that were frequently at odds with one another. Examples include Wal-Mart’s Store Status dashboard and Bass Pro Shops’ supply chain risk mitigation and business continuity dashboard (Sankary 2020).

As part of their public health response to the COVID-19 pandemic, governments— national, state, county, and city—implemented stay-at-home orders, including curfews, in places and recommended social distancing. In addition, non-essential businesses in various sectors were ordered to close. What was defined as non-essential varied from place to place, creating a jumble of local, county, and state regulations for businesses to sift through. Grocery stores, pharmacies, and big-box retail were deemed essential, but operations at Costco, Wal- Mart, and Target were somewhat altered to allow time for sanitization work. In many cases on- premises pharmacies and departments such as optical shops, photo printing units, tire and auto centers, and gas stations were closed. Under such circumstances, business continuity dashboards are an effective tool in the location intelligence arsenal of businesses

Consider the case of Bass Pro Shops’ retail operations which comprised stores in 45 U.S. states and 8 Canadian provinces. In Alaska, one of the world’s finest sources for wild seafood, fishing is a critical for the local economy. At the outset of the pandemic, the fish retailer was among essential businesses in the state of Alaska. However, under more restrictive local orders at Anchorage, the company’s two main stores in the state were deemed non-essential. As Bass Pro executives had been using GIS for making real estate decisions, its GIS team constructed a dashboard (Figure 6.4) showing the exact state of store operations for 169 stores in the United States. Open, closed, modified, curb-side pickup, and ship only stores were distinctively shown on this internal dashboard enabling decision-makers to develop business strategies that were aligned with local restrictions.

112

Figure 6.4 Bass Pro COVID-19 Retail Dashboard showing status of store operations in the United States during the COVID-19 pandemic

(Source: WhereNext, Sankary, 2020)

For example, in Anchorage, Bass Pro’s two stores were designated at ship-only since the retailer was deemed non-essential. A vast majority of Bass Pro’s stores could remain open (140 out of 169) but had to implement 6-foot social distancing guidelines. This meant that like many other businesses, store employees and managers have had to implement queueing strategies outside of the stores to prevent too many people in a store at any given time.

A unified source of truth, the dashboard also showed stores impacted by closures due to employees who has tested COVID-19 positive. Employees at those locations had to be immediately quarantined or switched to remote work. Most importantly, the dashboard enabled senior leaders in different departments to collaborate closely and be on the same page. Additionally, it transformed the conversation from closed stores to those locations that remained open (a vast majority). This prevented the onset of unnecessary panic and focused decision-makers on amending retail strategies consistent with local health guidelines. Last but not least, as COVID-19 cases spread rapidly, the case count information provided location intelligence on how to allocate one million masks donated by the company's founder to healthcare workers on the frontlines of the crisis.

While descriptive in nature, Bass Pro’s business continuity dashboard provided valuable location intelligence-based situational awareness guidance during a volatile period of uncertainty. In addition, using data and location intelligence, philanthropic efforts were guided provided a humanitarian angle to the deployment of the company's COVID-19 dashboard.

Business Recovery: Real Time Monitoring Real-time monitoring of disruptions can aid in rapid recovery. In the aftermath of

Hurricane Florence, CSX, a large railroad company with more than 21,000 miles of lines

113

extending through 23 states, Washington DC, and 2 Canadian provinces, needed to rapidly and accurately identify compromised lines and other infrastructure. With an army of drones, CSX conducted reconnaissance in flooded areas with no electricity and poor cell coverage. The firm was able to identify washouts (figure 6.5) that were displayed in a central GIS-based war room in real time. This information enabled senior leaders to work in concert with field crews who had eyes on the ground to prioritize deployment of resources to fix compromised infrastructure and proactively shut down compromised assets to protect workers and communities from harm. This, in turn, allowed operations to begin in a safe and timely manner, ensuring that critical supply chains in North Carolina, severely affected by the hurricane, could be restored expeditiously.

Figure 6.5 CSX culvert washout in North Carolina after Hurricane Florence, September 2018

(Source: Slide 9 of PDF presentation, available at https://www.esri.com/~/media/Files/Pdfs/events/Rail%20Summit/2018_Rail/Erik_CSX)

Requesting assistance to locate the Esri Conference at which this presentation was given.

Risk Resilience: Predictive modeling Trees are good for the environment. For an electric utility, though, tree cover in forested

areas poses a significant business risk. Downed powerlines are known to cause forest fires that may ultimately spread to nearby communities, causing untold damage and suffering through loss of property, livelihood, and even life.

Mid-South Synergy’s Electric Cooperative’s grid provides service to 23,000 customers across six Texas counties, Grimes, Montgomery, Madison, Walker, Brazos and Waller, located north-northwest of Houston. Part of Mid-South’s service area overlaps with Sam Houston

114

National Forest. Historically 30 percent of Mid-South’s power outages are due to fallen trees and branches and vegetation encroachment. Exacerbating the risk, many trees had been killed in its service area by a severe drought. Despite right-of-way maintenance, dead trees outside the company’s service area increased the risk of power outages as well as fires in the National Forest. Mitigating this risk to improve the reliability of service and reduce the likelihood of catastrophic forest fires prompted the company to leverage GIS technology.

Using soil data from the US Geological Survey and tree coverage from the US Forest Service, Mid-South located tall pine trees in dry soil types, which posed the greatest hazard to its power lines. Using predictive modeling in Esri’s ArcGIS, the company generated the probability of dead trees falling on the utility’s electric wires across the service area. The entire service area was subsequently categorized by risk level using a weighted overlay model (figure 6.6), which helped Mid-South’s field crews prioritize areas that posed the greatest hazard. Trees in those areas were then removed and encroaching vegetation cleared.

Figure 6.6 Weighted Overlay Analysis by Mid-South Co-op for showing Risk Levels posed by Dead Trees, Sam Houston National Forest TX

(Source: Esri, 2020)

115

Within the first year, tree-related outages dropped by over 60 percent while customer complaints about trees dropped by over 90 percent. Armed with predictive location intelligence, the company could proactively schedule the removal of dead trees from parts of its service areas before they posed significant threats to service. This boosted the utility’s system reliability, secured its grid, improved customer service, and provided location data and analytics-based guidance for risk mitigation (Esri 2020).

Innovations in Unmanned Aerial Systems (UASs) now enable utilities such as Mid-South Synergy to collect high-resolution imagery, for example, of tree cover, and then use machine- learning-based pattern recognition to identify threats to their infrastructure. This automated processing and pattern recognition from imagery accelerates the generation of accurate location intelligence from geospatial big data. Such advances illustrate the promise and potential of predictive modeling of risk in the private sector.

Business Risk Management: Predictive Big Data Modeling Geo-artificial intelligence (Geo-AI) methods are at the forefront of predictive modeling

of business risk. With the prevalence of geospatial big data, the role of predictive analytics for risk management and mitigation are expanding. A large amount of big data is unstructured—for instance, spatial imagery captured by drones or curated from satellite imagery. Allied with powerful visualization, data mining enabled by machine learning and pattern recognition can help uncover patterns and relationships to accelerate decision-making during emergencies such as natural disasters. For example, a major full-service insurer uses thousands of aerial photos of residential properties damaged by forest fires to train machine learning algorithms to detect the extent of property losses. When the Malibu area of Southern California was devastated by the deadly Woolsey Fire in November 2018, geo-AI models applied to before and after imagery of properties damaged by the fire were able to detect with close to 100% accuracy those that were a total loss. This helped the insurer proactively reach out to affected customers, ensure their safety, and expeditiously process their claims.

Concluding Case Study: Geospatial Innovation at Travelers Insurance Travelers is a leading property and casualty insurer, with over 30,000 employees and

13,500 independent agents and brokers. It operates in four countries including the United States and Canada, with revenues totaling around $30 billion in 2019 (Travelers 2019). Travelers provides coverage to individuals and businesses; its product portfolio also includes bond and specialty insurance such as contract and commercial surety. The firm counts Allstate and Nationwide among its main competitors.

Over the years, Travelers’ use of GIS and location intelligence across the enterprise for competitive advantage has encompassed many organizational functions, making it a spatially mature firm. Enterprise GIS at Travelers is a platform for collaborative engagement that is guided by three main principles:

1. Pay what you owe. The business objective is to accurately price risk for a location in order to underwrite appropriate location-specific insurance policies.

116

2. Improve customer experience. The business objective is to provide speedy claims resolution and timely assistance to customers whose lives may have been greatly affected by an emergency.

3. Increase efficiency and productivity. The business objective is to match resources such as adjustors to the realities on the ground so that claims can be appraised quickly, with shorter processing times.

Pay what you owe As an insurer, one of Travelers’ main business objectives is to accurately price risk for

any location. To do so, the company’s research and development team examines vast repositories of data that includes addresses, property taxes, and weather history. Weather history consists of multiple peril layers such as hurricane, tornado, hail, wildfire, and earthquake. Other factors include weather/climate variability, population density, population growth (in areas with weaker enforcement of building codes), urban expansion, and increase in the average size of a house.

Travelers also examines up to 120 location-specific risk factors, often at the street level. These factors, along with other geographic data, are modeled to predict the risk level for that location. This allows Travelers' R&D team to determine if existing insurance products are suitable for a market or if new policy products need to be developed. Also, based on risk, the extent of coverage can be determined, then reconciled with regulatory requirements. This drives insurance premiums and deductibles, ensuring optimal polices for customers and better returns for Travelers.

Travelers’ use of location intelligence for hyper-local modeling of risk has several related benefits. In addition to “rate-making,” Travelers is able to make decisions on reinsurance purchase. Depending on the risk profile of a location or business, Travelers decides whether to purchase insurance for its own insurance policies. Location intelligence also provides inputs to the company’s annual financial planning.

Improve customer experience using innovative predictive models Predictive modeling of risk has been a hallmark of Traveler’s focus on improving its

customers’ experience, often at a traumatic time in their lives. For this reason, Travelers has made significant investments in its technology platforms, talent and data to integrate geospatial capabilities and location intelligence into all areas of the insurance life cycle. For example, the insurer employs 650 certified drone pilots, who logged over 53,000 flights in the lower 48 states by early 2020 to build a robust portfolio of high-resolution imagery. Travelers National Catastrophe Command Center teams also aggregate millions of data points from weather services, satellite imagery, geospatial and location information

117

In addition, Travelers partnered with the National Insurance Crime Bureau (NICB) and the Geospatial Intelligence Center (GIC) to capture “blue and grey sky” (before and after) imagery of events. The imagery is critical during the response and recovery stages of risk management. For instance, after the Kincade fire that ravaged California’s Napa Valley in October 2019, Travelers’ claims professionals began manually to review images of the devastation within days to assess the damage at each insured location. By comparing before and after images (figures 6.7 and 6.8), properties that were total losses were identified manually before many of the affected customers could re-enter the area.

Figure 6.7 Neighborhood in City of Santa Rosa, Sonoma County CA, Before Kincade Fire, 2019

(Source: City of Santa Rosa, n.d.)

Figure 6.8 Same Neighborhood in City of Santa Rosa, Sonoma County CA, After Kincade Fire, 2019

118

(Source: City of Santa Rosa, n.d.)

Teams of insurance experts, software engineers, and data scientists work in tandem with the enterprise GIS team to automate pattern recognition from imagery for faster and more accurate incident response and recovery. By combining artificial intelligence with location analytics, these teams built the company’s innovative Wildfire Loss Detector, a PyTorch-based deep learning model that analyzes tens of thousands of images from damaged and undamaged homes to evaluate the space around a property and determine its likelihood of damage during a wildfire (Travelers 2019). Using its Wildfire Loss Detection model, the company anticipates advancing payments to its customers prior to physical inspection for over 90% of its claims. Overall, AI-based innovations are being used for a variety of purposes at Travelers, including catastrophe modeling.

Increase efficiency and productivity Location intelligence also plays an important role in deploying assets on the ground in

the aftermath of an emergency. With location analytics, the company has now fine-tuned its staffing approach during the recovery phase of an event, depending on location and the type of emergency. This efficient allocation of critical resources (adjustors and claims handlers) helps Travelers handle and pay out claims expeditiously and accurately.

Finally, location intelligence can be key to detecting fraud and reducing costs stemming from fraudulent claims. All fraud happens somewhere; it has a spatial dimension. Many insurance companies use mapping and statistical modeling to detect spatial patterns of claims, especially those that are much higher than average for particular types of repairs. In this way, insurers can isolate high-value claims and trace them to particular service providers, e.g. auto repair shops, by determining service outlets to which customers have traveled unusually long distances.

119

Chapter 7

Enhancing Corporate Social Responsibility

Introduction In making important decisions, organizations must consider how such decisions will

affect shareholders, management, employees, customers, the communities in which they operate, and the overall planet. Finding optimal solutions that are in the best interests of all the varied stakeholders is not always easy, but it is an important responsibility of business. This chapter examines how location intelligence can guide and shape ways in which a contemporary business can balance its financial goals and competitive pressures with environmental, social, and human objectives.

Corporate Social Responsibility (CSR) calls for a company to be socially accountable in ways that go beyond making a profit. The company takes a broader view of its goals, thinking not only of its stockholders, but also of the benefits to its employees, customers, community, the environment, and society as a whole. As mentioned in chapter 1, the 2019 Business Roundtable's “Statement of Purpose of a Corporation” made a major shift by declaring that a company serves the needs of all its stakeholders (Business Roundtable, 2019). The statement includes commitments to development and to compensating employees fairly, fostering “diversity and inclusion, dignity and respect,” interacting fairly with suppliers, enhancing the communities a business is involved with, supporting sustainable practices, and generating “long-term value” for shareholders (Business Roundtable 2019).

Environment, Society and Governance It has been observed that CSR articulates the range of business societal intent, while

environmental, social, governance (ESG) represents the actionable practices and benchmarks for intended practices, and the alignment of their business strategy, initiatives, investments, and partnerships. The broadest and most widely accepted ESG framework is the United Nations Sustainable Development Goals (SDGs), adopted in 2015 with targets in 17 areas set for 2030 (Figure 7.1). As noted in the UN statement:

“On behalf of the peoples we serve, we have adopted a historic decision on a comprehensive, far-reaching, and people-centered set of universal and transformative goals and targets. We commit ourselves to working tirelessly for the full implementation of this Agenda by 2030. …We are committed to achieving sustainable development in its three dimensions – economic, social, and environmental – in a balanced and integrated manner. “

Countries and companies are now taking aggressive steps to advance and their ESG agenda and utilizing locational analytics and related GIS systems and data to do so. The successful implementation of United Nations Sustainable Development Goals (SDGs) Data Hubs by more than 15 countries over the last four years has established a consistent, scalable pattern for reporting on and monitoring the progress of the SDGs (Esri 2021).

120

Figure 7.1: United Nations Sustainable Development Goals

(Source: United Nations, 2022)

Business Implementation of ESG As noted in the Chapter 1, a substantial percentage of global businesses report on ESG

achievements. Location plays an important role in integrating this information. Using GIS mapping and location analytics, a company can—

 Collect and analyze data pertaining to its ESG practices, in a scalable way, and share it across various business platforms.

 Geo-enrich such data with location-specific indicators of interest to the firm, for example, indicators of health and wellness, or racial/ethnic diversity. Other indicators might include psychographic attributes of customers such as attitudes towards the environment, social causes, and activism.

 Prepare location-specific GIS maps, reports, and dashboards that inform business strategy and ESG practices, monitor progress towards specific goals, and measure their impact.

 Engage various stakeholders, both within and outside the organization, who are likely to be affected by the firm’s ESG actions.

 Gain necessary insights about the demographic and socioeconomic composition of communities in which the company operates. This, in turn, can inform community engagement projects.

121

Sustainable Supply Chains One area of ESG focus is supply chain transparency. This requires companies to know

what is happening in their supply chains and to communicate that both internally and externally to employees, customers, and other stakeholders. The reputational risk and cost of engaging with suppliers who unethically source raw materials or manufacturing partners who do not hire locally, pay fair wages, or engage in child labor can be immense. To this end, using mapping and geo-enrichment, many companies are ensuring supply chain traceability and providing full disclosure about internal operations, direct suppliers, indirect suppliers, and origins of raw materials to various stakeholders.

Early adopters of supply chain mapping are companies such as Nike that maps its manufacturing plants and offers insight about individual factories (Bateman and Bonanni, 2019). UK retail giant Marks and Spencer provides interactive mapping (Bateman and Bonanni, 2019) of its more than 1,300 factories worldwide in 44 countries employing over 900,000 people. As shown in Figure 7.2, these factories produce Marks and Spencers' food and beverages as well as clothing, accessories, beauty products, footwear, giftware, and items for the home.

Figure 7.2 Marks and Spencer Global Supplier Locations for Food, Clothing, and Home products

(Source: Marks and Spencer,2022)

122

Figure 7.3 Marks and Spencer Wool Supplier Locations in New Zealand

(Source: Marks and Spencer,2022)

Users can query the interactive map to determine the exact locations of plants from which certain raw materials are sourced. For example, Figure 7.3 shows farm locations in New Zealand from which Marks and Spencer sources wool for clothing apparel and related other household products, along with the number of employees and animals at a particular farm.

VF Corporation, whose brands include Timberland, The North Face, Wrangler, Dickies, and Vans has taken one step further in leveraging location analytics to improve product traceability and supply chain transparency (VF Corporation, 2018). After creating an exhaustive database of Tier 1, 2, and 3 suppliers of materials (such as fabric, leather, yarn, foam, laces, trim, etc.), traders, textile mills, factories, and distribution centers, VF Corp. has created traceability maps of its brand name products that communicate to consumers exactly where raw materials for a particular product was sourced from, where it was manufactured, assembled, and shipped for distribution, and how components of the product flow between different facilities in the supply chain.

For example, Figure 7.4 shows Timberland's Women's Waterproof Boots that are traced back to 20 facilities across produced in 7 countries over 4 continents. Traceability maps also include information on each facility's workforce diversity, as well as certifications on (a) sustainable materials use, (b) environmental and chemical management, (c) health, safety, and social responsibility, along with worker well-being, community development, and environmental sustainability programs. These product-by-product supply chain traceability maps take transparency and disclosure to a high level, inviting all stakeholders including consumers to be more informed about where and how the products they buy are made.

123

Figure 7.4 Traceability map of Timberlands Women's Premium Waterproof Boots, showing production and distribution facilities (factories, textile mills, material suppliers, and distribution centers) worldwide

(Source: VF Corp.,2020)

Preserving Biodiversity Companies can play an important role in preserving biodiversity. An example is Natura,

the largest manufacturer and marketer of cosmetics, household, and personal care products in South America. As part of its commitment to sustainability, Natura seeks to conserve biodiversity in the Amazon region, where its agroforestry farming and employment strategies aim to build community wealth. Using a spatial approach, Natura has fostered interactions with rural communities and developed sustainable value chains that generate superior returns for the company (Cheng, 2021).

In the early 2000s, Natura launched its Ekos line of beauty products comprised of bath products, premium fragrances and cosmetics, hair care, and skincare products, in addition to products for infants and children. Raw materials for these products included Brazil nut, passion fruit, andiroba plant-based oils, murumuru butter, cacao (from which cocoa butter is sourced), and other biodiverse inputs native to the Amazon rainforest in Brazil. By the 2010s, with burgeoning demand for such products, one of the problems encountered by Natura was how to find potential suppliers in a region plagued by a lack of logistics. To do so, the company needed to compile production and harvest data, including the locations of thousands of participating farms. Retention of suppliers was another challenge. But the company’s policy was to maintain open relationships with suppliers and constant interactions with the community (Boehe, Pongeluppe, and Lazzarini, 2014).

To achieve these objectives, the company built a geospatial platform. Supply chain data collected from the field was combined with internal business data, analyzed, and then published in the form of interactive web maps and apps. Using the company’s geospatial

124

platform, farming cooperatives, consumers, and shareholders could design different views of data, specific to their workflows, and gather location intelligence to make decisions about sourcing, pricing, and distribution. The company’s platform also improved traceability and transparency of its investments, production, and supply chain infrastructure. With a greater ability to view the entire supply chain, Natura has maintained its commitments to biodiversity and environmental stewardship while generating sustainable competitive advantages for the company (Esri, 2015). By using a "quadruple bottom line" approach that balances financial, environmental, social, and human objectives, Natura has continued to diversify its product offerings using an expanded array of supplier communities and bio-ingredients while simultaneously protecting the Amazon and committing to the ethical sourcing of biodiverse ingredients (Natura, 2020).

Climate Resiliency A recent study has shown that the world economy could shrink by 10% if the 2050 net-

zero emissions and Paris Agreement targets on climate change are not met (Swiss Re, 2021). Around the world, business leaders are enacting strategies and tactics to address climate change. As climate crises disrupt business operations and increasingly pose threats to business continuity, companies can monitor their environmental and carbon footprints over space and time by using geo-visualization, dashboarding, and predictive modeling approaches.

AT&T serves as an example of a corporation taking an active role in understanding the impacts of climate change and taking actions to impact global warming as well as to assist businesses in mitigating climate impacts. Through their Climate Resiliency initiative, the company (in collaboration with Argonne National Laboratories) is building a Climate Change Analysis Tool. Using data analysis, predictive modeling, and visualization, this tool enables AT&T to react to climate changes by making the adaptations necessary to help increase safety, service, and connectivity for its employees, customers, and communities (AT&T, 2021). An example of this initiative is assessing the potential impact of climate-induced flooding on their infrastructure (see figure 7.5) and taking appropriate mitigation actions.

AT&T plans to make the tool widely available to other businesses and the public, citing a recent survey which found that the majority of US businesses (59%) view climate change as a priority, yet less than a third (29%) have assessed the risks of climate change to their business (AT&T, 2019).

COVID-19 Pandemic Dashboard Dashboards provide visual displays of critical business information arranged on a single

screen to provide a consolidated, unified view of a business or phenomenon. (Sharda, Delen, and Turban 2016). The COVID-19 pandemic has accelerated the need for businesses, organizations, and communities to visualize fast-moving and rapidly changing business patterns and trends, often in real time. Due to the rapidly increasing volume of data from disparate sources, the demand for scalable, efficient, locationally sophisticated visual analytics is at an all- time high. The importance of accurately depicting on-ground realities and “telling the story” to different stakeholders, from senior leaders to frontline employees, has never been higher. This

125

unprecedented demand for data visualization and predictive modeling of business risks in real time has catalyzed the use of sophisticated dashboards, with mapping, for COVID-19 and other government and business applications.

Figure 7.5 Visualization of AT&T Fiber (black lines) and Cell Sites (green dots) in Savannah, a coastal Georgia city, at risk of flooding, with darker colors indicating high flood levels

(Source: AT&T, 2019)

Johns Hopkins University (JHU)’s COVID-19 dashboard (Figure 7.6) received worldwide attention during the pandemic because it seamlessly combined data from dozens of sources to provide spatial and temporal depictions of critical COVID-19 trends in real-time. Viewed up to a trillion times or more, the dashboard fuses COVID-19 data from hundreds of sources such as the World Health Organization (WHO), European Centre for Disease Prevention and Control (ECDC), US Center for Disease Control and Prevention (CDC), and various country, state, municipal, local governments and health departments.

To identify new cases, JHU researchers have monitored various Twitter feeds, online news services, and direct communication sent through the dashboard. COVID-19 cases, incident rates, fatality rates, and other metrics of interest to governments, health authorities, businesses, news organizations, and the general public were reported at various geographic resolutions. Initially, for some countries, such as the United States, Australia, and Canada, data were reported at the city level, and at the country level otherwise. At present, US COVID-19 data is reported in the dashboard at the county level. Data were updated multiple times each

126

day to keep the dashboard up-to-date and meet expectations as an authoritative source of COVID-19 data and its visualization at a time when the spread of the disease became rampant in many parts of the world (Dong, Du, & Gardner, 2020).

Figure 7.6 Johns Hopkins University's COVID-19 dashboard showing cumulative cases worldwide in December 2020

(Source: Johns Hopkins University ,2020)

(PLEASE PRINT IN LANDSCAPE MODE)

It has also informed modeling efforts by experts in governments, public and private sector agencies, and academia to generate accurate spatiotemporal forecasts of transmission and spread of the disease. These models have informed the formulation of public health policy of governments and health organizations worldwide to prevent further escalation and spread of the virus. As such, JHU's COVID-19 dashboard is a perfect illustration of the power of dashboards for data description, fusion, and visualization to inform numerous stakeholders.

In the private sector, organizations have embedded smart mapping capability within COVID-19 business dashboards to monitor facility status (for example, what factories are open or closed, what capacity where warehouses are operating), identify business segments (customers, trade areas, markets) most vulnerable to risk, develop contingency plans, manage and care for a distributed, remote workforce, improve supply chain visibility, and craft strategies to safely reopen businesses and facilities where appropriate. Such dashboards also enabled businesses to untangle myriad local, state, and national regulations and guidelines that were frequently at odds with one another. Examples include Wal-Mart’s Store Status dashboard and Bass Pro Shops’ supply chain risk mitigation and business continuity dashboard (Sankary 2020).

Predictive Modeling of Pandemic While John Hopkin’s Dashboard provided an update to indicators of the pandemic’s

outbreak, another predictive analysis was needed to assist in targeting resources. Such a model

127

was developed and implemented by Direct Relief. Direct Relief is a non-profit humanitarian aid organization operating in the United States and more than 80 nations worldwide, providing critical relief to improve the health and lives of the most vulnerable populations affected by poverty and crisis (Direct Relief, 2020). The company's mission is to "improve the health and lives of people affected by poverty or emergencies – without regard to politics, religion, or ability to pay." (Direct Relief, 2020)

To achieve its mission Direct Relief needs precise geographic information and accurate spatial models to predict the needs for humanitarian assistance when normal information channels have been disrupted or destroyed. The range of relief events include catastrophic floods, storms, fires, and earthquakes, and as pandemic diseases such as COVID-19, Ebola, and Zika as well as persistent infections like HIV, malaria, and tuberculosis threaten millions annually (Schroeder, 2017). Such information helps the organization identify specific needs on the ground in an impacted area, coordinate its response with dozens of other organizations, and then deliver targeted relief (supplies, equipment, personnel) in a timely manner.

During the COVID-19 pandemic, providing protective gear and critical care medications all over the world to as many healthcare workers as possible and as quickly as feasible was a crucial part of Direct Relief’s operations. Timely shipping of personal protective equipment (PPE)—millions of N95 and surgical masks, gloves, face shields, and tens of thousands of protective suits—in coordination with public health authorities, nonprofits, and businesses posed an immense logistical and supply chain challenge.

To address the challenge and meet critical needs at the point of care in the US, Direct Relief needed to identify hotspots of COVID-19 infections and hospitalizations. To do this, Direct Relief used Facebook-provided data, data from other third parties, and AI to predict, visualize, and analyze the spread of COVID-19 in US counties (Smith 2020). Specifically, Direct Relief employed a neural-network-based AI model developed by Facebook’s AI Research (FAIR) team. This FAIR model combines reliable first- and third-party data on a wide variety of important factors such as confirmed cases, prevalence of COVID-like symptoms from self-reported surveys, human movement trends and changes across different categories of places, doctor visits, COVID testing, and local weather patterns to forecast the spread of COVID-19 in the US (Le, Ibrahim, Sagun, Lacroix, and Nickel 2020).

Using spatial cluster and outlier analysis based on the outcomes of the FAIR model, Direct Relief identified emerging, receding, and persistent hotspots, coldspots, and outliers of COVID-19 spread. For instance, figure 7.7 illustrates outcomes of cluster and outlier analysis to predict that Los Angeles, San Bernardino, and Riverside counties in California would become hotspots of COVID-19 spread between December 20, 2020, and January 9, 2021, while Maricopa County, Arizona, Miami-Dade County, Florida, and Cook County, Illinois, were predicted to be outliers (counties with high prevalence of COVID-19 surrounded by other low- prevalence counties).

128

Figure 7.7 Predicted COVID-19 Case Spread from Dec 20, 2020 to Jan 9, 2021 (based on FAIR Model) in U.S. Counties

(Source: Direct Relief ,2020)

In addition to the FAIR model, by analyzing human mobility patterns, Direct Relief was able to predict surges and regional acceleration of COVID-19 in rural northern Ohio and western Pennsylvania, and in states such as Wisconsin (figure 7.8). The organization predicted accurately that hospitalization rates were likely to lag periods of elevated mobility by about 12 days, while mortality rates would lag by another 10 days (Smith 2020).

Figure 7.8 Change in Human Movement during the COVID-19 pandemic in Wisconsin counties between Oct - Dec 2020

(Source: COVID-19 Mobility Data Network,2020)

Armed with such predictive spatiotemporal insights into disease spread, Direct Relief overlaid its shipment facilities on its dashboard of COVID-19 spread to identify their proximity to hotspots. This, in turn, enabled the organization to position critical care resources and

129

supplies, track deliveries, and prioritize its financial support to healthcare facilities responding to the pandemic. Such predictive location intelligence can also inform and accurately time the responses of public health officials, help them develop policies, and provide recommendations to the public to slow the spread of the virus. Overall, using location modeling and intelligence, the company is able to fulfill its mission of serving the most vulnerable populations with no expectation for payment or profit.

Diversity, Equity, and Inclusion (DEI) Issues of racial inequities and injustice have been growing in societal and business

discourse in recent years and came to the forefront in 2020. As corporations have navigated social and racial unrest, their role in addressing socioeconomic inequities has increasingly come under the microscope. There has never been a more important time for businesses to understand the importance of location and local geographies in addressing these deep-seated challenges.

Location intelligence can inform a company's efforts to engage in racial justice efforts in their immediate communities by providing a geographically nuanced, data-enriched view of community conditions (for example, access to affordable housing, healthcare, education, transportation, banking, and financial services, parks and open spaces, to name a few), and identifying gaps that stem from causes such as racial inequities, discrimination, and lack of access to power and resources.

Spatial statistical modeling can yield powerful location-specific insights about correlations between community conditions and barriers to equality (for example, race/ethnicity-based discrepancies in reliable broadband access and usage spawns economic, educational, housing, and health inequalities, thus deepening the understanding of root causes of racial injustices. This can inform a company's decisions to prioritize their racial justice efforts, customize location-specific resources in alignment with community conditions, identify partners in the community, provide a platform for collaboration, monitor the progress of initiatives, and communicate their impacts using tools that are enriched by location insights and intelligence.

For example, the “Business Case for Racial Equity” study estimated that $135 billion could be gained per year by reducing health disparities (Turner, 2018). Healthier workers have fewer sick days, are more productive on the job, and have lower medical care costs. They estimate disparities in health in the U.S. today represent $93 billion in excess medical care costs and $42 billion in untapped productivity. This is in addition to the human tragedy of 3.5 million lost life years annually associated with these premature deaths (Turner, 2018). Innovative companies ProMedica, Kaiser Permanente, Cigna, and UnitedHealth Group have created geographically focused shared value approaches that address racial health inequality in a manner that improves health and reduces costs (deSouza and Iyer, 2019). Unfortunately, the COVID-19 pandemic has made the challenge greater, as it has had a disproportionate impact on underserved communities (United Health Foundation, 2021).

130

Location intelligence can also provide companies with a valuable window to create a diverse, more equitable workplace. Location-infused dashboards can provide insights into the workforce diversity of an organization operating in multiple locations. This can be benchmarked against industry standards to identify gaps based on demographic, socioeconomic, and diversity metrics such as duration of tenure and prior experience. Based on these gaps, organizations can target specific employees for transfer from one location to another and tailor attractive compensation packages based on locational analysis of factors such as the cost of living.

For example, San Diego has undertaken an inclusive economy initiative. The goal is to contribute toward the regional goal of 20,000 skilled workers (degree or credential holders) in San Diego County by 2030, and to do it through a more “Inclusive economy”. Part of the initiative is the use of location analytics as we will look inward to address regional talent shortages and strengthen the relationship between to under barriers to such as education, and access (figure 7.9.)

Figure 7.9 Disparities in Access to Jobs by Race and Ethnicity within the context of an Inclusive Economy in San Diego CA

(Source: San Diego Regional Economic Development Corporation, n.d.)

Location intelligence can also shape HR recruitment strategies by providing insights into the diversity of graduates in immediate communities surrounding an organization. Companies can direct resources accordingly to participate in job fairs and launch geotargeted advertising of open positions to create a diverse pool of prospective applicants. Armed with location intelligence on the composition of students in institutions that serve underrepresented

131

populations, including historically Black colleges and universities (HBCUs), tribal colleges and universities (TCUs), and women’s colleges, organizations can build a pipeline of new job candidates.

Community Development Finally, spatial analysis of industry clusters can produce location intelligence on regional

concentrations of employers and employees for a particular industry. Regions comprised of diverse communities such as multinational, multicultural ethnic melting pots can also be geotargeted for recruitment of employees who are likely to contribute to creating a diverse and more equitable workplace. One example of this is the creation of Opportunity Zones as part of the 2017 Tax Cuts and Jobs Act (TCJA). A total of 8,764 Opportunity Zones have been

designated in the United States (figure 7.10), many of which have experienced a lack of investment for decades. The Opportunity Zones initiative is a tax policy incentive to spur private and public investment in America’s underserved communities. The aim of the program to encourage private investment in communities of need. The Council of Economic Advisors (2020) estimates that by the end of 2019, Qualified Opportunity Funds had raised $75 billion in private capital. Although some of this capital may have occurred without the incentive, the CEA estimates that $52 billion—or 70 per- cent—of the $75 billion is new investment. These activities are tracked by multiple sources, including a StoryMap (Figure 7.11) by Economic Innovation Group (2021).

132

Figure 7.10 Over 8,700 Census Tracts Designated as Opportunity Zones in the United States

(Source: HUD, n.d.)

Figure 7.11 Opportunity Zones Activity Portal, showcasing economic activities and development in Opportunity Zones in the United States

(Source: Economic Innovation Group, n.d.)

One specific example of this type of shared value is in Detroit. JPMorgan Chase is a leading bank worldwide, with 2019 revenues of $110 billion, 257,000 employees, and a large suite of products and services including corporate banking, risk management, market-making, brokerage, investment banking, and retail financial services. JPMorgan was a signatory of the Business Roundtable statements on CSR and has embedded CSR as part of its culture. One of its exemplary CSR projects has been a long-term program to help small businesses thrive in the city of Detroit, which had been on a downward spiral for several decades, exacerbated by the great recession of 2007–2009. As a result, many downtown neighborhoods and major streets were deserted and in disrepair, with small businesses bankrupt or enduring heavy losses.

JPMorgan decided to foster a campaign named “Invested in Detroit” across these neighborhoods to seek to rebuild the city and its economy (Heimer 2017). It would do this through a program of credit for small businesses combined with bank-initiated training of affected managers and employees, and a highly-skilled analytics team rotated to Detroit from the bank’s array of prosperous divisions and offices. The bank invested $150 million in this project, partnering with local redevelopment enterprises to identify businesses with potential that were not able to meet loan criteria.

The bank discovered it could help these businesses most effectively by using Community Development Financial Institutions (CDFI) loans, which are keyed to low-income areas to help

133

disadvantaged small enterprises and nonprofits. JPMorgan’s analytical team assisted in locationally analyzing a plethora of data points by neighborhood and pinpointing micro-districts of 10 to 15 blocks each for the initial credit and job-training push. The bank has succeeded already with loans to three initial micro-districts and will scale this up to a dozen more while rolling out this CSR approach to depressed areas of other American cities. JPMorgan is also realizing eventual financial benefit since in Detroit it holds $20 billion in deposits. Hence, the "Invested in Detroit" project can stimulate long-term business growth and conversion of many new and renewed businesses into bankable entities adding to JPMorgan's market share of deposits and loans (Heimer, 2017).

Corporate initiatives based on shared value, such as JPMorgan's "Invested in Detroit" allow businesses to contribute to the greater good. This can ultimately enhance their reputation and standing as ethical, responsible, and trustworthy enterprises, which in turn can help retain customers, improve employee morale, lower turnover, and instill a purpose-driven corporate culture.

Closing Case Study: Nespresso Nespresso is an operating unit of Nestlé, headquartered in Lausanne, Switzerland.

Nespresso produces coffee pods in aluminum-coated packets, which can be used in Nespresso espresso machines or equivalents. Raw, high-quality coffee from developing nations, mainly in Africa and Latin America, is shipped to three factories in Switzerland, where the coffee is ground, encapsulated in aluminum pods, and sold worldwide. The Nespresso single-serve system was patented in 1976 and today is sold in over 81 nations (Nespresso, 2022).

Nespresso is committed to the UN 2030 goals, tracking their performance on the relevant 10 of the 17 goals in various initiatives (Chiappinelli,2018). In 2003, Nespresso formed the AAA Sustainable Quality program with the assistance of the Rainforest Alliance. The AAA Program guides and equips farmers with the technical knowledge and financial resources necessary to pursue sustainable practices. Nespresso then pays the farmers a premium market price for coffee that meets the AAA Program quality standards (Rainforest Alliance, 2021).

The AAA Program started with 500 farmers in 2003 and now reaches more than 122,000 farmers in 15 countries representing a total annual investment of over US $43 million per year (Nespresso, 2021). The company sources 93% of the coffee through that program and 95% of our global coffee purchases for 2019 met the Fairtrade Minimum Price (Nespresso, 2021). The AAA program is part of Nespresso’s “Positive Cup Framework,” This framework focuses on long- term sustainable coffee supplies, analytics to support farmers, transparent communication to customers, and responsible practices in communities. These priorities are supported AAA platform, which includes F.A.R.M.S (Farm Advanced Relationship Management System) (De Pietro,2019). At the farm level, the geospatial platform can be used for a variety of analyses, such as biodiversity protection (figure 7.12). At the global level, it can track the achievement of AAA sustainable goals (figure 7.13).

134

Figure 7.12 Nespresso F.A.R.M Biodiversity map displays analysis of landslide potential and tracks the planting of trees to protect biodiversity

(Source: Nespresso)

Figure 7.13 Nespresso's Positive Cup Dashboard displays the F.A.R.M.S GIS visual database that tracks the performance of all 75,000 farms along with a number of sustainability measures

(Source: Nespresso)

This long-term multi-decade commitment to sustainability has led Nespresso to be lauded for its commitment to sustainability and commitment to enhancing local suppliers and communities. Porter and Kramer recognized this when they introduced the concept of shared value in 2011 (Porter & Kramer, 2011). In the article, they called out Nespresso for their

135

community building impact, noting: “Embedded in the Nestlé example is a far broader insight, which is the advantage of buying from capable local suppliers….Buying local includes not only local companies but also local units of national or international companies. When firms buy locally, their suppliers can get stronger, increase their profits, hire more people, and pay better wages—all of which will benefit other businesses in the community. Shared value is created." (Porter & Kramer, 2011, p.10).

Nespresso, as well as the other examples in this chapter confirm the interrelatedness of business and the communities they operate in, and the mutual business and society gains that can be possible. Geospatial platforms enable location analytics that can reveal these patterns, trends, and opportunities.

136

SPATIAL BUSINESS: COMPETING AND LEADING WITH LOCATION ANALYTICS

PART III

137

Chapter 8

Business Management and Leadership

Introduction

This chapter turns to examine the human and behavioral side of spatial business. Mounting great geospatial efforts and design is not likely to succeed without the human factors of management and leadership. In this context, leadership involves skilled management, championing spatial initiatives, continuing training and education of employees, and understanding the human elements that strengthen locational transformation of the organization. It encompasses corporate social responsibility, for which a company considers, in addition to its profits, its full effect on society, including environmental, social and economic impacts. The chapter also includes the management concern for location privacy.

Consider an Asian cruise shipping firm that is seeking to develop locational intelligence because its senior vice president of marketing “gets it” about how locational information can be applied competitively, leading to more profits. The director of global marketing, who is beginning to learn about spatial and is also enthusiastic, is working with an analytics expert who is tasked with pushing forward a powerful spatial marketing and customer system to offices companywide.

Among the firm’s challenges are how to evaluate and tweak a starting enterprise-GIS built by an outsourcer, how to train 25 middle managers and skilled business analysts in seven major business units, how to motivate managers who are trained to move forward in their units with creative use of the new system, and how to gauge progress and measure locational value. A looming challenge is how to break down organizational walls and widen the location analytics initiative to other units such as navigation and supply chain. Also, since building long-term customers is crucial, the leaders and managers of the firm must protect the location privacy of their records.

All the chapter issues are captured in this example: spatial maturity, workforce development, middle and senior management, digital and spatial transformation, ethics and privacy, and, and the pulls and tugs of executive championing of location analytics versus competing internal initiatives.

Spatial Maturity Stages Figure 8.1 shows an organization's stages of spatial maturity, based on a stage model for

analytics in general proposed by Davenport and Harris (2017). Davenport and Harris (2017), who posited the analytics stages upon which Figure 8.1 is based, also describe a progression through analytics stages, which may be adapted to apply to location intelligence and analytics.

138

In stage 1, there is limited locational data, which often lacks quality controls. There are limited workforce skills in location analytics and few if any metrics measuring spatial value and productivity. The trigger that moves to stage 2 is often effort by the original supervisor or low- level sponsor to broaden the support base and try to communicate with senior leaders. Since GIS and location intelligence are relatively new to some business leaders, an important step in the process is to engage them, educate them if necessary, and bring them along in the journey.

In stage 2, sponsorship of spatial initiatives comes from a local departmental or divisional manager (Davenport and Harris 2017; SBI 2018). The stage is typified by testing spatial applications locally and assessing net benefits. The initiative may then be taken up by other departments and their sponsors.

139

Figure 8.1 The five Spatial Maturity stages represent moving organizationally from analytically impaired, to localized analytics, analytical aspirations, analytical company and analytical competitor

(Source: Davenport and Harris, 2017)

Whether location analytics stays long-term in this stage or advances to stage 3 depends on gaining the initial support from senior management for a companywide effort. An example is a leading international commercial real estate firm, in which a highly skilled technical manager succeeded in gaining traction for a spatial intelligence initiative in US business units but had mixed success in persuading overseas units to start local projects, in some cases encountering resistance to a new technology. In this instance, spatial maturity had been temporarily stalled at the local stage overseas.

Stage 3 involves sponsorship by a senior executive and the formation of antecedent structures to launch an enterprise spatial system. Usually, a location analytics project is improved to the point of garnering visible attention companywide. Another crucial development is “defining a set of achievable performance metrics and putting the processes in place to monitor progress” (Davenport and Harris 2017). At the end of a successful stage 3, the C-suite will begin to recognize the importance of GIS, and sufficient capabilities will be in place to implement enterprise-wide.

In stage 4, the senior executive team decides that location analytics will be implemented across the entire company. A centralized and highly skilled GIS team is assembled, bringing in talented location intelligence employees who may have been working in business units, and the team's relationship to the corporate IT group is resolved. A centralized companywide spatial database is also established.

When an enterprise GIS is installed with strong performance and benefits, the C-suite begins to see it as a competitive force. Stage 5 begins with this recognition from senior management, which then adds resources to position the system competitively. At UPS, for example, top management realized that the ORION enterprise routing system was world class and could create significant cost savings and efficiencies. Once a spatial system is enabling a firm to gain on competitors, the challenge becomes to maintain that lead with further improvements such as novel analytics and strategic applications, upgrading of infrastructure, and embedding useful applications of emerging technologies.

This progression across stages is helped by facilitators (Davenport and Harris 2017, SBI, 2018). The two most important facilitators are a perception of the value of location-based insights for business and the availability of world-class spatial technology. Practically, these factors were present for the Walgreens case and will be seen in this chapter’s BP case. The next two factors are C-suite support and clear business strategy. In the case of UPS, covered in chapter 9, location analytics languished as a project for many years with little recognition from the C-suite, until a regional test finally excited the top leaders, resulting in rapid progress in maturity stages. The last factor of articulation of ROI in GIS has less influence and would likely add to progression through later stages. This is seen in results from a survey of 200 businesses (SBI 2018), in which respondents who were asked to indicate the factors that differentiated

140

stages of spatial maturity. (see Table 8.1). Rather than ROI, the most important maturity- facilitation factors were availability of best-in-class location intelligence technology and value of location-based insights to the business and customers.

Success in progressing through the stages benefits by efforts by the GIS team in engaging leaders and managers outside of the location analytics area and collaborating with them in the progression. Among other things, this ensures a sustainable support base for location analytics.

Table 8.1 Perceived Net Facilitating Factors of Differentiation of Spatial Maturity

Facilitator or Inhibitor of Differentiation of Spatial Maturity Net Percentage Facilitating (Facilitator minus Inhibitor)

Value of location-based business and customer insights 45%

Availability of best-in-class GIS and location intelligence technology 45%

C-suite sponsorship and support 30%

Clear and coherent business strategy 29%

Clear articulation of ROI of GIS and location intelligence initiatives 17%

(Source: SBI, 2018)

Management Pathways Since the location analytics team is rarely located in the c-suite, which underscores the

importance of the strong technical managers. As mentioned earlier, such managers can be crucial to obtaining and retaining a sponsor or champion, branching out to other middle managers, leading in improving the quality of data and spearheading technology and software improvements. In addition, a manager has responsibilities in location analytics department planning, day-to-day management of personnel, hiring, and coordinating with the IT department, which tends to be considerably larger than the GIS group.

As appreciation for location analytics improves a direct communication tie-in will need to be made with senior management (Tomlinson, 2013).

Certain steps recommended for effective Location Analytics Management (Somers, 1998; Tomlinson, 2013) are the following:

 Devising an approach that works for developing and implementing projects in a particular firm. The common system development steps for a spatial project include planning, analysis including requirements specification, design and build, implementation and maintenance. However, GIS management needs to be flexible to

141

such factors as scope of project, speed required, human resource capacity to complete the project, and extent of control imposed on the project team. One recommended step for location analytics projects is, early on, to hold a technology seminar, which is a meeting of nearly all the major stakeholders, that has focus on training for the project, awareness of its goals and challenges, and gaining understanding and consensus on the development and oversight roles involved throughout the project (Tomlinson, 2013).

 Long range planning and vision. Location Analytics teams need to work on and gain consensus on a plan for the location analytics. The plan should have a vision of what a desired long-term outcome is of location analytics in the organization (Tomlinson, 2013, Somers, 1998). The plan can serve as a unified series of steps building up to important goals. It should be subject to modification as the planning period unfolds, and it needs to be aligned with plans for IT and for the business as a whole.

 Coordinating team members with users. As is standard in technology projects, location analytics projects need to have users involved in designing and building systems, as well as evaluating systems that are in active use (Pick 2008, Tomlinson, 2013). Location analytics is a resource that has multiple potential users and its outputs are encouraged to be shared, as much as proprietary or security constraints allow. Users can discover beneficial applications that were not planned for, so need to be added.

 Communicating with stakeholders. Location Analytics managers need to communicate with multiple stakeholders that include technical team members, users, vendors, the IT manager or CIO, and often including senior management. Communications must be proactive, timely and appropriate to the level and interests of the other party or parties (Somers, 1998). For instance, a short phone call with the VP of marketing would differ from an intensive video-conference review of performance with 3 members of the GIS team. Each exchange is tailored to the time, knowledge base, and objective of the communication exchange.

To sum it up, as stated by Roger Tomlinson (2013), a “GIS manager must not only have a firm grasp of GIS technology and capabilities, but also be a meticulous organizer, a strong leader, and an effective communicator.”

Case Example: CoServ As an example, consider the role of GIS middle management at CoServ, a Texas utility

cooperative. CoServ is an electric and natural gas distribution company founded in 1937, serving over 250,000 electric meters in six counties north of Dallas, Texas (CoServ, 2020). The firm also offers solar renewable energy. The company started in a region of rural farmland where a group of residents formed the nonprofit cooperative to provide them with energy. Today, to the north and northeast of Dallas, former farmland has increasingly been converted into corporate headquarters and other facilities, but CoServ’s western area still functions, for now, as a rural cooperative.

142

CoServ originally started its location analytics unit to upgrade small orange map books of its electrical systems, which were manually copied for use in the field. Today CoServ deploys an enterprise system, web mapping for business units, and full access to the system by field personnel on their mobile devices. It also provides corporate-level enterprise support to business-unit GIS teams in electric utilities, gas utilities, and engineering.

The GIS manager of over a decade has developed workforce, set project goals, established working relationships with the business divisions, collaborated on a workable structure for an integrated IT/GIS department, established strong relationships with vendors and outsourcers, and developed visibility for GIS across the company as well as in the senior leadership.

The IT/GIS group works together well with the understanding that the IT department is in charge of configuring systems, servers, databases, and networks, but that the GIS team populates the databases with data, installs the GIS software and portal, and runs the administrative accounts. The GIS middle manager has also worked out a productive relationship with the spatial teams in engineering, electric, and gas. Engineering, for example, has specialized utility design and operations software, for which the GIS department serves only as a consultant if needed. Likewise, gas and electric GIS groups use SCADA software to monitor transmission and pipeline flows, which central GIS installs and supports as needed.

The largest and most challenging project has been developing the enterprise location intelligence. The GIS manager and team realized this would take many months of effort, not just technical effort but also coordinating end users, scoping the steps of the project, going through iterations of testing, changing time-worn processes, and training users. The system succeeded with electric utilities and well along the process with gas. The web-map portal built on top of the enterprise base has been popular with end users since they can customize spatial applications within hours or days, rather than waiting weeks.

Overall, the CoServ story exemplifies many of the key responsibilities of location analytics management: developing an approach that works in a particular firm -- long-range planning, coordinating the GIS team with users, and effective communication.

Applying management principles to spatial transformation Digital transformation is a process of applying digital technology to creatively and

fundamentally change a business, including its existing culture, organization, and business processes (Tabrizi et al. 2019). Since GIS and location intelligence are becoming increasingly digital, organizations are concurrently undergoing spatial transformation, which we define as the part of digital transformation that involves locational processes and cross-organizational and cultural changes. If a business is reimagined to have its geospatial information and processes in digital form, based on such features as web maps, portals, cloud computing, broadband internet, 3D or 4D visualization both inside and externally facing, and if this changes the way business is conducted, then spatial transformation is underway. As part of spatial transformation, people also change in their job roles, skills, productivity, and decision-making.

143

A business that is spatially transformed is usually in the 4th or 5th maturity stage, so that spatial applications have permeated the organization and may already be a competitive force. For instance, the BP case at the end of the chapter exemplifies location transformation in the 4th maturity stage.

A practical view of spatial transformation posits a series of steps (McGrath and McManus 2020, Harvard Business Analytic Services 2020):

 Define and communicate the underlying business objectives.

 Define the spatial operation experience (McGrath and McManus 2020), i.e., indicate which locational elements or tools will be digitized, beyond the state they are in now. An example would be changing from a disjointed set of maps showing each step in a supply chain to having an integrated digital display of the entire supply chain from raw material to customer.

 Invest in personnel to support and maintain the spatial operating experience. Although technically trained people are required, there is equal need for investing in people with soft skills who are creative, adaptable, and flexible (Frankiewicz and Chamorro-Premuzic 2020).

 Focus on specific location-based problems and use metrics. For instance, in the State of Connecticut, truck routing displays were transformed from 2D to 3D to solve the problem of trucks being too large for safe passage on routes with bridges, overpasses, and tunnels. 3D mapping enabled precise measurement of the maximum allowable dimensions for truck transit.

 Emphasize data needs. It is essential to maintain focus on having extensive, high-quality data upon which to base the spatial transformation.

 Look for platforms and ecosystem implications. Encourage the user to arrive at or create solutions that can reside on top of a stable platform. An ecosystem implication refers to the interfacing of a robust enterprise spatial platform with the platforms of other collaborating businesses or organizations. For example, the Arizona Republic newspaper derived competitive advantage by applying a GIS system to select geographic areas suitable to a particular advertiser and collaborating with advertiser systems that supported decisions on what goods and services to advertise (Pick 2008).

 Drive spatial transformation from the top. Spatial transformation changes the business profoundly, to the extent that senior management becomes the driver (Frankiewicz and Chamorro-Premuzic 2020).

Many companies seek digital transformation, but fewer succeed. In a 2020 poll of 700 executives, 95% indicated that digital transformation had grown in importance over the past two years, and 70% pointed to digital transformation as significant, yet only 20% evaluated their own firm’s digital transformation efforts as effective (Harvard Business Analytic Services 2020). These findings point to the difficulty of succeeding in digital transformation but also to

144

the competitive opportunity for the firm that does succeed. The same study emphasized that cultural change may be a barrier to overcome. In the instance of spatial transformation, culture that is set in its use of legacy approaches to GIS can be transformed by top management setting clear business goals, communicating those goals, and using indicators to check performance in reaching them (Harvard Business Analytic Services 2020).

Leadership and championing Leadership is crucial in spatial business. A leader provides vision to an organization,

communicates the vision to the people he oversees, motivates and inspires them to work toward the vision, and enables this effort among the relevant people in the organization. The leader or champion of GIS and location intelligence in a business has even more challenge because spatial and GIS are frequently unfamiliar concepts that require persistence and patience to “educate” personnel and stakeholders about what GIS is and why it is important for business. This added challenge is evident in some cases, such as the long delay at UPS for spatial to be recognized by upper management or by the continual challenge and only mixed success at one of the leading global commercial real estate companies in educating and persuading country business units outside the U.S. about GIS and why it can be significant.

Among the qualities of spatial leadership that are recommended are the following:

 Act as a role model. The leader must set an example with her behavior and beliefs, on which others in the organization or unit can model their behavior.

 Inspire a shared vision (Kouzes and Posner, 2017). Leaders think up and present a vision of the business. It is essential that the vision be shared throughout the organization or portions of it overseen by the leader. This can best be achieved by inspiring subordinates to “buy off” on the vision, rather than by coercion.

 Encourage and counsel others to act. The leader depends on others to do most of the work, so needs to support their efforts. This effort is often done in teams. The leader builds trust in subordinates and teams and he needs to continue to stay engaged with the work as it is carried out, sometimes over considerable time.

 Be a friend and guide. Show concern (Kousez and Posner, 2017). The effective leader must reach out to seek to establish friendship with subordinates and stakeholders. She should offer guidance and genuinely be concerned about the people who are doing the work and carrying the projects forward.

Specific approaches recommended for leaders of geospatial and location intelligence in business (Kantor 2018; SBI 2018) include setting priorities, which lead in turn to strategies. This can be seen in Figure 8.2, which defines how to get to vision by setting priorities, leading to strategies, and turn strategies into implementation (SBI 2018).

145

Figure 8.2 An illustration summarizes the components of the three major steps in achieving location strategy of establishing priorities, determining strategy, and implementing the strategy

(Source: SBI, 2018)

As an example of setting priorities that lead to strategy, the location analytics leader may set as a top priority the development and implementation of location analytics with big data to provide for improved logistics throughout North America. Further priority areas that the spatial leader might address are the following (Kantor 2018):

 Analysis of the locational aspects of supply chains.

 Understanding how location analytics can be combined with social media sentiment analysis to reveal changes in attitudes across geographies.

 Advancing depth of knowledge of the customer through locational tagging of customer ordering patterns in time and space using mobile apps.

Other dimensions of spatial leadership are corporate social responsibility and the awareness of the privacy and ethical implications of location intelligence.

Examples of ethical spatial leadership occurred in the 2020 COVID-19 pandemic, during which leaders of several software vendor companies put profit motives aside and provided free spatial software and services to help nonprofit organizations, academia, research institutions, businesses, and governments monitor the geographical spread of the virus, optimize the delivery of needed supplies, and track down the contacts of infectious individuals through geo- referenced social media apps while preserving the privacy of those individuals.

146

Privacy and Ethics in Spatial Business Notwithstanding the tide of expanding benefits, efficiencies, and innovation from spatial

business applications, location intelligence also presents companies and their spatial leaders with ethical dilemmas. Location has its own set of ethical issues associated with it. Often a decision has to be made that must balance risks between locational value at one end of the scale and harm at the other end—unintended or intentional harm to customers, employees, or the general public.

Here, we raise privacy, one of these ethical concerns, to highlight the breadth of challenges, and suggest several ways to consider approaching them. Location privacy is defined as control over the locations of people and their associated personal information and over the primary and secondary uses of this information. This ethical issue has grown to widespread prominence through the proliferation of methods to determine the location of people and assets, and the increasing accuracy of these methods.

The escalating use of cell phones worldwide means that the locations of billions of people can be tracked over long periods without their knowledge. At the same time, satellite imagery of the planet is available from dozens of providers at varying scales, resolutions, spectral wavelength, and frequencies of collection. Imagery can be scanned rapidly by machine learning to identify features and at very high resolution to recognize vehicles, people who are outdoors, and other identifiers. People's locations can also be collected from fixed video cameras and wearables.

A location-tracking industry has sprung up that is unregulated in the US and sells detailed tracking information about people and assets to other companies and organizations. The ethical issues of location privacy are relevant to purveyors of geo-referenced data, but also to executive decision-makers in the location-tracking industry and to companies that purchase and make use of the location information.

An example of the ethical issues for personal privacy can be seen in a database from a location information firm that was provided anonymously to a research team at the New York Times (Thompson and Warzel 2019). The database, with 50 billion location pings from cell phones of over 12 million people in the US, reveals the ordinary daily-life mobility patterns of individuals—but also unusual patterns, including visits to drug rehabilitation facilities, doctor offices, or more worrisome places. The individuals have consented to reveal their location by clicking “yes” to the legal notices that pop up with many applications. Some software apps include little pieces of code called SDKs that provide normal location information for the app, but also can be collected by location information firms (Thompson and Warzel 2019).

Given the growing threat of intentional or unintentional misuses of personal data and many other situations where location information and analytics have the potential for harm, what can the individual do to protect herself? What can the company and its leaders do to protect workers and customers and ensure that spatial business adheres to ethical standards?

147

Measures that can help to reduce this threat include the privacy policies of businesses and government regulation, as well as tools for anonymizing information and data obfuscation (Duckham 2013). Companies can set high standards that reject misuse of personal information, including not allowing purchase of unconstrained location files, and can require GIS, IT, and business managers to weigh the ethical balance in any decision presenting potential for locational harm. In addition, regulation needs to come from federal and state legislation, which so far in the US has not sufficiently regulated private-sector data controls outside of some industries such as healthcare and banking. The US Constitution does support some aspects of locational privacy rights through the Fourth Amendment, although more legal tests are needed to clarify these rights (Litt and Brill 2018). In Europe, the General Data Protection Act of the European Union has constrained use of personal information without permission but does not include any substantive regulation of location privacy. On the individual level, anonymizing and obfuscating data can be effective, up to a point, but is often difficult for the individual to control, especially with large spatial datasets.

Developing Spatial Business Workforce This chapter concludes with a critical management and leadership concern, which is

how to recruit and develop and a location intelligence workforce, in a market that has limited supply and growing demand. The concern extends beyond internal training within businesses and must consider the opportunities and constraints in society on how geospatial workforce is being educated and trained.

Since skilled team members and managers at different levels are crucial to supporting business use of location analytics across the spectrum of stages, spatial workforce development is essential. This topic has received attention from varied stakeholders including universities, governments, professional groups, and businesses. Several key questions arise, among them: What mix of geospatial knowledge and skills is needed for the challenges of working in industry? How can universities best prepare future workers in spatial business? What is the market in the US for geospatial in business?

In terms of knowledge and skills needed, a general reference point is the Geospatial Technology Competency Model (GTCM), that was developed through a collaboration between the U.S. Department of Labor Employment and Training Administration and GeoTech Center (DiBiase et al., 2010, GeoTechCenter.org, 2020) (See Figure 8.3).

148

Figure 8.3 A pyramidal diagram of the Geospatial Technology Competency Model includes personal effectiveness at the base; academic, workforce, and industry competencies in the middle; and management competencies and specific occupational requirements at the top

(Source: U.S. Department of Labor, 2020)

In this model, multidisciplinary academic competencies and workforce competencies form a foundation for industry competencies. The GTCM includes broad human competencies, such as interpersonal skills, combined with knowledge of geography, science, engineering, math, computing, critical thinking, and communications. The workplace competencies include business fundamentals, problem solving, planning and organizing, and teamwork. The upper half of the pyramid emphasizes management competencies, as well as technical competencies, management competencies, and occupation-specific requirements.

The level of knowledge and skills depends on the overall responsibilities of the position. Table 8.2 outlines these spatial business GTCM related competencies at the entry level (e.g., analyst), mid-level (e.g., manager), and senior level (e.g., director). As such it also serves as a model pathway for a spatial business career. To that end, a sample of existing position titles are provided across a range of industry sectors.

149

Table 8.2 Spatial Business Competencies

Spatial Business Competencies

Spatial Business Career Pathways

Entry: Analyst Mid: Manager Senior: Director

Academic Required undergraduate degree, preferably with business and GIS elements.

Certificates can substitute for business and GIS elements.

Preferred advanced degree (at Master’s level) with business and GIS elements.

Certificates can substitute for location analytics in degree.

Required advanced degree (at Master’s level) with business and GIS elements

Certificates can substitute for location analytics in degree.

Workplace Preferred 1-3 years of experience in workplace environment involving GI.S

Required 3-5 years of experience in workplace environment involving GIS.

Required 5-10 years of experience in workplace environment involving GIS.

Industry Wide - Sector

Industry sector experience preferred but not required.

Ability to manage technical and business aspects of selected industry.

Ability to lead location analytics enterprise to achieve business goals and strategies.

Management Management experience not required.

Required 1-3 years supervisory experience.

Required 3-5 years management experience.

Sample Current Job Titles

Senior GIS Analyst (Silicon Valley wireless services start up).

Business Systems (GIS) Analyst (regional utility company).

Geospatial Data Analyst (national security company.)

Senior Product Manager, Location Intelligence (global business data consulting firm).

Geospatial Strategy and Analytics Manager (regional e-commerce company).

Manager of GIS Analytics and Insights (large regional grocery company).

Executive Director, Enterprise Location Intelligence (global pharmacy company).

Director Geospatial Data Science (global life insurance company)

Associate Director, Geospatial Services (national non-profit)

Universities play an important role in preparing a spatial business workforce at each level of knowledge, skills and responsibilities noted above. What is needed for this model to work well in practice is coordination between academic preparation, workplace training, and

150

mentoring from experienced geospatial professionals. Ideally, there would be a spectrum of programs that integrate business, geography, GIS, location analytics into a unified curriculum, with practicums of students networking with industry people in communities of practice.

A starting point concerns the status of university spatial-related programs and whether they recognize and include suitable preparation for business careers (Marble 2006, Dibiase et al., 2010; Tate and Jarvis, 2017). To date, education that integrates critical areas of business and geography/GIS are relatively rare.

For example, Geography departments have been viewed as the most important source of geospatial workers and with good reason, since GIS’s original base disciplines in the 1960s and 1970s were geography and cartography (Solem, 2017). However, the discipline of geography is becoming more multidisciplinary (Tate and Jarvis 2017). Correspondingly, while business schools have grown Business Analytics components in the curriculum, many have lagged in integrating location analytics into their curriculum (Sarkar et al., 2020).

While more integration will undoubtably occur as the needs for these knowledge and skill continue to grow, professional certificates can also serve as useful complement to traditional academic training. For business degrees, this can be a certificate in Location Analytics. For GIS degrees, this can be a certificate in a business area such as marketing and supply chain management. Another suggestion for broadening GeoSpatial education (Solem, 2017) is to encourage education complementing standard GIS technical courses with courses on holistic topics of ethics, human welfare, citizenship, social relations, and well-being (Solem, 2017). The goal is “to promote individual autonomy and freedom through imagination.” This approach can provide broader viewpoints of societal dimensions that could be addressed through location analytics.

Overall, the value of spatial business academic and professional training is high. Although the US federal government does not have separate job categories for Geospatial managers, analysts, or geospatial specialists, 2020 US Bureau of Labor Statistics data for related categories such as cartographers, geoscientists, statisticians, and software developers show high rates of growth, pointing to demand-driven job markets for spatially-educated people entering the workforce in the 2020s.

Communities of Practice Several studies of Geospatial education in geography have called for broadening

Geospatial education considerably beyond traditional academic topics by introducing communities of practice and broad capabilities of thinking about life (Tate and Jarvis, 2017; Solem, 2017). Communities of practice are proposed as a bridge between academia and industry. As seen in Figure 8.4, the GIS community of practice includes current and newly graduated GIS students along with two types of experienced professionals, those with a graduate degree in GIS and those without a formal degree (Tate and Jarvis, 2017). The community is regarded as complementing formal courses. It is suitable to virtual form. It provides an informal way to bridge the barrier between active business workforce and

151

students, with information exchange going both ways, since the business practitioners confront real projects without disciplinary boundaries and recent and current students bring insights in technologies, platforms, concepts that increase lifelong learning and awareness even among business professionals.

Figure 8.4 A graphic illustrates the GIS community of practice in the middle, which consists of and unites current students, recent student alumni, business GIS professionals with GIS degrees, and other GIS professional schooled on the job

(Modified from Tate and Jarvis, 2017)

An example of job preparation and transition to industry is the career progression of Beth Rogers, shown in figure 8.5, who went from an undergraduate education in biology to a MS in geoscience and then to a first business job as an intermediate software developer at Fruit of the Loom, a major underwear and clothing company that is part of Berkshire Hathaway (Kantor 2020). She joined the firm’s analytics group, which has access to a vast database of company clothing data. Although the environment and problems were very different from her graduate school study of the geography of fish, she applied her spatial background to develop algorithms that saved shipping costs in supply chains and relocated products to alternative distribution centers. On the job, she received mentoring from the CIO and others and was so successful that Beth was advanced in several steps to senior manager of data science (Kantor 2020). This story illustrates how a solid and broad science education can be converted, with mentoring, into an intensive and rapid-moving sequence of spatial business positions.

152

Figure 8.5 Beth Rogers, a senior manager of data science at Fruit of the Loom is pictured in a photo

(Source: Esri, 2020)

Concluding Case Study: BP BP (formerly British Petroleum) is the world’s seventh largest petroleum company, with

2017 revenues of $223 billion and 70,000 employees. In the mid-2010s, a decision was made by Senior BP management to overcome mounting IT glitches with its legacy system by initiating companywide digital transformation (Jacobs 2019; Venables 2019). A major transition from maturity stage 3 to stage 4 was affected by rolling out an enterprise architecture, which the Geospatial Lead and team named “One Map.”

The One Map enterprise platform replaced the existing decentralized silos with an integrated system that includes big data across the enterprise, IoT, access for any authorized BP employee, inclusion of all static, real-time, and historic data, and storage in regional clouds for more rapid response. The platform includes systems of record for field assets, as well as spatial analysis and location analytics tools (Boulmay 2020). Infrastructure is regional, consisting of in- country server installations, each with enterprise servers, portal, and full geospatial software. With this modern platform, any user anywhere worldwide, with permission, can access the complete set of BP map layers and information. Monthly, there are over 2,100 active spatial portal users and over 4 million map service requests. For backbone applications, automation of widely repeated workflows is emphasized, which simplifies use and maintenance (Boulmay 2018).

153

BP’s small Geospatial team was originally tasked a decade earlier to support four siloed US BP business units spread over four states. BP made the decision to leave critical specialist systems inherited from the silo period in place, but now they also are linked to the enterprise backbone, which supports a common enterprise-wide centralized database that contains over 40,000 datasets (Boulmay 2019). This allows robust storage of data accessible across the company’s matrix of regions, portable devices, and functional teams. Through collaborative conversations and organizational networking, the GIS manager ascertained that specialized business units preferred to develop their own dashboards and visualizations, which put those analytics applications in the hands of the specialists who understood pipeline design or raw material supply chain routing. Specialist analytics were handed over to special teams known as “citizen developers” (PESA News 2020).

The Geospatial Lead exemplified spatial leadership in other ways as well. He rolled out continual training and emphasized marketing and justification of GIS at maturity stages along the way. He also realized that, after building standardized software, infrastructure, and data, he needed to lead by shifting attention to the data services that were being used by the most user departments and emphasizing the high-use applications. An example was leading in supporting the mapping and spatial analysis required for the COVID-19 pandemic. The standardized central data could be analyzed in new ways, and quickly, allowing valuable reports to be produced within days.

The lessons from the case are reflected in the facilitating factors of spatial maturity, as seen earlier in Figure 8.1. All along the way, it was important to educate internal "customers" i.e. managers of peer groups and, later on, higher level executives in the value of location intelligence. The geospatial platform was carefully chosen as best in class. As it grew and matured, the spatial unit had a consistent strategy of providing a platform while letting the users develop applications. The C-suite became supportive and directly involved as part of the location-intelligence enterprise roll out. Specific lessons at BP are summarized in Table 8.3.

Table 8.3 Digital Transformation at BP – 8 Lessons Learned

Lesson 1. Embrace the enterprise platform

Decide which part of the organization should take ownership of geospatial capabilities. Do not try to do everything.

Lesson 2. Find the right home. For BP, the original home was in the subsurface business under the Upstream segment of the firm.

Lesson 3. Set your data free

Make data across the entire organization as freely available as possible, subject to security and privacy constraints.

Lesson 4. Let business users extend the platform

Provide tools that are as user friendly as possible, delegating to business users the extension and upgrading of applications in their areas.

154

Lesson 5. Tailor user support to the new paradigm

Foster collaboration between the location analytics team, IT, and the business units, which leads to support.

Lesson 6. Automate

If technically feasible, automate a workflow early if it is likely it will be repetitive.

Lesson 7. Mind your marketing

Market concertedly the locational transformation internally and externally.

Lesson 8. Measure success

Evaluate the locational transformation at steps along the way. Afterwards review of the steps can provide invaluable feedback and lead to necessary tweaks and improvements.

Lesson 9. Build and maintain teamwork and appreciation for the players.

Give attention to support and appreciation of the team members.

Lesson 10. Communicate clearly and widely and repeat the message as needed

Keep a focus on communicating activities and accomplishments.

(Source: modified from Boulmay 2018)

As this system rolled out, the small team shifted from reporting to a small-scale business unit, to reporting to corporate IT, and later to the leaders in core business divisions served in a major way by the system, since they had the deep business knowledge of value and workflows (Boulmay 2018). Subsequently, senior management came to understand that geospatial transformation was a key part of the corporation’s digital transformation, which increased their interest and support. In the latest reorganization of the company with the arrival of a new CEO in early 2020, the Geospatial team joined a new high-level group, innovation and engineering, a locus for the people leading BP’s corporate digital transformation, a key goal of the CEO.

155

CHAPTER 9

Strategies and Competitiveness

Introduction Geospatial strategy begins with justification of the importance of location analytics

weighed against multiple other uses of organizational resources. Decision-makers need to ask how strategizing will pay off, and how, where, and why location value can be realized. Then, if strategic planning is to occur, what are the formal steps? How can policy be turned into action and how can it be kept current?

For information systems, a well-known success factor is to achieve alignment of IT strategy with business strategy (Peppard and Ward, 2016). This means that parts of IT strategy complement and strengthen the corresponding parts of the business strategy. For instance, if the business strategy seeks to offer spatial decision-making to an organization’s field workers, the IT strategy seeks to provide decision software on a cloud-based delivery platform that connects the mobile devices of the workforce worldwide. The importance of alignment likewise applies to Location Analytics strategy (Lewin, 2021).

Geospatial strategic planning has both external and internal elements. The external element focuses on how location analytics can be used to strengthen the firm’s competitive position, or modify forces affecting competition, such as customer relationships or new products. Internal planning emphasizes improving the firm’s own GIS infrastructure and processes. The internal element focuses on the alignment with business needs, technological capacity, and human resource requirements to achieve desired location and business value.

Several themes have arisen in this book that provide strategic themes for organizations to consider. These include:

 Identify and Enhance Location Value Chain

 Enable Spatial Maturity Pathway

 Match Analytical Approach to the Business Needs

 Build a Spatial Business Architecture

 Use Market and Customer Intelligence to Drive Business Growth

 Measure, Manage, and Monitor the Operation

 Mitigate the Risk and Drive the Resiliency

 Enhance Corporate Social Responsibility

 Inspire Management to Capture Vision and Deliver Impacts

156

 Solidify Spatial Leadership for Sustainable Advantage

This chapter considers these themes within the context of strategic planning and the next chapter elaborates on their implication for practice.

Geospatial Strategic Planning Strategic planning is standard practice for middle-sized and larger organizations,

whether a business, university, or governmental unit, and strategic plans are often required by the senior management or the board overseeing the organization (Hitt et al. 2016, Hitt, 2017). Likewise, IT planning has become commonplace in medium/large organizations (Peppard and Ward 2016). Formal Location Analytics planning is growing and shares many concepts with IT planning, but has some unique features examined in this chapter (Pick 2008; Lodge 2016). Also, there are many organizations where a informal geo spatial strategy is developed even if not performed as a formal “plan’.

Steps to Development of a Geospatial Strategy

1. Initiate the plan with self-assessment and identify the business issue(s) or opportunity(s) that location analytics is intended to address.

Getting started on a geospatial strategy is challenging, especially is it takes resources away from short-term projects and ongoing operations. For that reason, the strategic effort should start with the highest-level senior manager or executive who is responsible for the overall outcomes of the strategy. The self-assessment needs to consider the capacities of the firm’s human resources, IT infrastructure, finance, and management, and how they compare to the demands of the location analytics strategy (Peppard and Ward 2016; Piccoli and Pigni 2022).

2. Identify the current Spatial Business Architecture components of the company.

They include people, infrastructure, data, applications, users, business unit emphases on location intelligence, and governance. This step gives an overall picture of the present state of GIS throughout the enterprise and the current capabilities.

3. Determine what the value-add benefits of the geospatial strategy are to the corporation.

Evaluate the value benefits for a new or enhanced location analytics capacity indicate how it helps the firm’s business objectives. Tangible benefits are measurable, such as the benefit of faster delivery times, greater value-added productivity, and measurable increase in customer satisfaction. Intangible benefits include such non-monetized advantages as high- quality executive decision-making, improvement in company brand image, and improved readiness to cope with supply chain interruption.

Costs must also be appraised, including hard costs such as personnel, spatial software, data, virtual infrastructure, servers, and facilities, as well as the soft costs of insurance, security,

157

and consulting. The net location value is the difference of location analytics value benefits minus costs.

In considering value, look across the relevant business functions, and well as appreciate social, community and environmental dimensions that may be appropriate. Such an approach reflects corporate social responsibility aspect of the geospatial strategy.

4. Assess markets that new or substitute products and services can be released into.

The Porter (2008) competitive forces model can be applied to assess the competitiveness of spatially- enabled products being released into a market. As outlined below, new and substitute products and services can alter the existing dynamics of competition. Other competitive forces that can alter the market are changes in relationships of a firm with buyers and suppliers. The geospatial strategy needs to assess the competitive impacts for spatially- enabled innovations.

5. Determine the vision and mission location analytics as a component of the company.

The geospatial vision is a picture of what the company’s location intelligence will be in the future. It will serve as a guide for the organization to reach its imagined role for location analytics in the future and how location intelligence will operate. It will reflect a full realization of value to a variety of customers. This vision will be complementary to the firm’s business vision.

The geospatial mission is a statement of the key long-term broad purpose of location analytics in the organization. For instance, the mission might be to achieve world leadership in applying location intelligence in evaluating insurance risks. Or the mission might be to have the most accurate geospatial prediction for siting new car dealerships in Brazil. The mission is often brief so that it is understandable and can have wide adoption throughout an organization. It serves to unite the firm’s stakeholders around a primary goal.

A leadership team should include both those with technical and business expertise to ensure the vision and mission capture both the business strategy and role of location analytics in achieving the business strategy. They should be included as participants in the vibrant back- and-forth of discussion, argument, and eventually consensus on mission and vision. Sometimes this participation has been reduced, but as seen in the review of location maturity stages, gaining a voice in upper management decisions is a key factor to succeed in attaining stages 4 and 5. Geospatial strategy progression has floundered without both business and technical expertise being included in formulating vision and mission. For example, in a global commercial real estate company, the leader of its geospatial initiative was excluded from corporate strategy-setting and GIS became a “hard sell” around the company, falling way short of its potential.

6. Define the scope of infrastructure to achieve the strategic objectives.

What technical steps need to be accomplished? These might include building software applications, outsourcing to a cloud platform, providing intensive spatial training for new

158

managerial users, prototyping innovation in emerging spatial technologies, or strengthening the physical geospatial infrastructure (Lodge 2016). Scope can be examined in terms of departments and regions, or in terms of internal and external stakeholders.

7. Assess risks.

The strategy being proposed should consider risks of financial losses, lowered quality of outcomes, underperforming personnel, poor management decision-making, reputational damage, and adverse events in the external environment (Peppard and Ward 2016). For example, in a global commercial real estate company, the leader of its geospatial initiative was excluded from corporate strategy-setting and GIS became a “hard sell” around the company, falling way short of its potential.

Some of the other risks include:

 Information for operational efficiency or management decisions is missing.

 Investment in strategic development of GIS is out of alignment with IT and business strategies.

 GIS infrastructure and related IT infrastructure are insufficient to support the GIS improvements called for in the strategic plan.

 Location value is underestimated because intangible value is not recognized.

 Strategic spatial applications and solutions are implements with very short life cycles, so the benefits are reduced by the need for constant redesign.

 The management priorities change in unexpected ways, requiring major revision of the GIS strategy.

If carefully conceived, a geospatial strategy can serve as a touchstone over several years. Smaller activities and projects can be measured against the plan in approving them for funding. This approach has been shown to steadily move an organization towards achieving its strategic goals. However, the external environment and markets may change more rapidly than the pace of the strategic plan. An example is the COVID-19 pandemic, which impacted many firms in their spatial deployments, depending on industry. Consider, for instance, the map- intensive sharing economy for on-demand ridesharing. In ridesharing, the GIS benefits of locational intelligence in Uber and Lyft cars were overshadowed by a sharply reduced customer base. On the other hand, the pharmaceutical industry benefitted by urgent need to apply predictive location analytics to optimize vaccine supply chains.

Building a robust and complete Geospatial Strategic Plan that is updated regularly serves as a reference point for all the location-value contribution that a company should strive for, yet the extent of strategic planning tends to vary by the spatial maturity stage of the firm (Peppard and Ward 2016).

159

When a geospatial strategy is undertaken as an effort within a nascent department (stage 2 from chapter 1), the spatial strategic focus can be limited to finding the immediate opportunities, obtaining sufficient technology and determining the value-added of the endeavor (e.g., Steps 1-3).

As spatial maturity progresses to stage 3, the focus of planning typically shifts to incorporate strategic goals. At stage 4 of enterprise platform, most of the recommended strategy planning steps are in effect. Since spatial awareness is now firmly implanted throughout the organization, coordination of location strategy with the company’s business strategies might now be initiated by members of the senior executive team. Finally, in maturity stage 5, the spatial strategy has matured to include research on competitors, risk analysis, and the appraisal of innovative spatial technologies.

An example of making the shift to more comprehensive strategic planning is seen for British Petroleum, discussed in chapter 4. BP jumped, in a transformative single year, from planning on a multi-departmental performance basis to enterprise-wide planning keyed to strengthened connections to senior management. Maturation of spatial strategic planning paralleled the jump to a firm-wide, enterprise locational approach for competitive advantage. Shifts of strategy as location intelligence has progressed in maturity also is seen in Hess Corporation (see Appendix).

Case Example: United Parcel Service (UPS) UPS is the world’s largest package and delivery company, with 481,000 employees,

performing over 2.3 billion route optimizations annually and serving over 2,000 facilities in 220 countries and territories (Westberg 2015; Perez 2017; CFRA, 2021). Its revenue in 2020 was $84.6 billion (CFRA 2021). It represents a case of long-term geospatial investments that have come to be at the core of the corporation, but not without some setbacks.

In 1991, the firm introduced the first Delivery Information Acquisition Device (DIAD). At the time, the DIAD was an innovative mobile device for the UPS driver that provided updated delivery information and allowed drivers to electronically capture information throughout their route. Today, the latest DIAD includes these features, plus scanning of bar codes, tallying of cash on delivery, a programmed personalized map route that can be modified during the day with corresponding route stops and timecard information, and many other features.

UPS’s ORION delivery system is one of the world’s most sophisticated and powerful locational optimization systems (Westberg 2015, Chiappinelli, 2017). It minimizes the driver’s daily route based on advanced optimization models, data from planning systems, and customized map data (Westberg 2015). By optimizing throughout the day, the UPS delivery truck might take surprising and unexpected paths. For example, for the next delivery, the truck might drive past four delivery points without stopping, on the way to a more distant fifth point. Though this seems puzzling, the counterintuitive reason is that it would optimally save travel time, gasoline, and money to proceed directly to the fifth point and return later to the missed

160

points. This was a giant step forward from UPS’s previous routing routines, which did not optimize beyond the next delivery point.

The GIS strategic planning had a slow start. Jack Levis, formerly UPS’s Senior Director of Process Management, started at the firm in 1975 and took over a project in 2002 to provide spatial optimization for UPS delivery routing. He and his small team produced extraordinary innovation in mapping optimization for the UPS fleet but lacked a strategic plan and management support to operationalize the mapping software until 2012. In that year, senior UPS management finally gave him the go-ahead to prototype it for one region. The results were startling, showing high ROI and driver satisfaction. The ORION software quickly became a UPS- wide strategic initiative, and still remains at the core of UPS’s integrated data infrastructure, now with additional regulatory and service components (Perez 2017). The contemporary ORION includes dashboards for control in the local delivery office. In Figure 9.1 UPS workers are viewing a map to plan daily ORION deliveries on a monitor, along with a handheld DIAD.

The lesson of the decade-long, low-profile ORION testing and the subsequent decade- plus years of strategic use, highlights several points. An important point is that, in retrospect, the development period was too long, given the subsequent competitive strength of ORION. During the ten years of R&D, this project had not been included in the strategic business plan or IT plan of the company. Due to this misalignment, the R&D innovation was somewhat siloed until its practical importance was suddenly realized (Levis 2017). After 2012, ORION turned out to be the force of direct competition, a “secret weapon” developed internally; the system is currently generating over $400 million in annual cost savings and avoidance (Gray 2017).

161

Figure 9.1 Two UPS logistics and delivery employees are looking at a map from the company’s mammoth ORION delivery system in contemplating routing, with one employee holding a DIAD mobile device that can access ORION on-the-spot in deliveries

(Source: Pittsburgh Post-Gazette, 2020)

UPS now places strategic emphasis on improving ORION further, developing a real-time and dynamic updated version with even more complete global coverage. This real-time version has a much shorter development cycle of two or three years (Gray 2017). For the future, CIO Juan Perez foresees the “dispatch [of] a fleet of autonomous package cars each morning that are guided by a real-time version of ORION” (Perez 2017). Another service under R&D is NPT (Network Planning Tools), which builds on ORION with a mixture of artificial intelligence, advanced analytics, and optimization, a set of tools that can yield better efficiencies (Perez 2017). ORION and its successors are now part of UPS’s strategic business plan, aligned, prioritized by senior management, and highly competitive.

Location Analytics Strategy in Small Business Location Analytics strategy development for a small business follows the seven steps

given earlier in the chapter, but has some distinguishing features that arise from the limited resources available and shifts in the challenges of competition and collaboration. They include the following.

 Self-assessment of spatial capabilities may be more challenging due to a small-firm senior management’s lesser awareness of location intelligence. It may encourage more proportionate use of outside spatial consultants.

 Alignment of location analytics goals with corporate strategy is essential. For example, a small blinds and draperies firm adopted web-based single-user business mapping software to compete effectively with a larger number and more numerous and varied range of competitors over a large geographic market. Accordingly, the GIS strategic goal to understand the expanded geographical patterns of competition was aligned to the business strategy.

 In identifying its internal location intelligence components, the small business can more quickly survey its people, resources, software, data, etc. Typically, in a small firm, resources are scarce and hence it may be they have to adjust the strategy to make the most use of internal and external talent.

 Calculating net benefits of location intelligence may be regarded as too difficult and time consuming for a small staff.

 Based on Porter’s five forces model, the small company may face asymmetrical competitive forces from large, incumbent firms.

 In a small firm, establishing the GIS vision and mission may diluted in the “haste to get to market” (Gans et al., 2018).

162

 Assessing risk might be more difficult for the small business, since firm is often innovating into an unfamiliar markets and environments. For instance, the small blinds and draperies firm felt it lacked its own capability to determine its GIS risk, so turned to US Small Business Administration, which in turn referred the company to a nearby university to help in determining risk.

On the plus side, innovation may benefit by the small firms’ flexibility.

In summary, the same seven steps in formulating GIS strategy apply also for small enterprises, but with less geospatial internal workforce capability, time pressure from leadership to move rapidly to implement, and heightened competitive forces from much larger players. Consequently, locational intelligence strategy may be cast aside in the rush to get to market.

Location Analytics Strategy in a Small Business: RapidSOS RapidSOS is a small, growing private firm founded a decade ago, that developed and

offers an innovative approach to enhance the readiness of response to 911 distress calls, by sourcing and organizing location intelligence and other enriched digital information to accompany the distress calls sent to 911 emergency centers, which are relayed to first responders. The business and societal importance of providing fuller and more accurate information to expedite emergency response has been accentuated in the covid-19 pandemic which at its peak contended with issues of an overload of critical 911 calls.

The enriched information that RapidSOS sends, along with the call, to emergency communication centers (ECCs) includes the accurate location of the caller, information on her real-time health, vehicle crash indicators, the caller’s personal profile, and building security features at or nearby the emergency location (RapidSOS, 2021). The enriched location information can also be passed directly to government emergency dispatch centers, known as PSAPs (Public Safety Answering Points) (see Figure 9.2). A first responder who is dispatched from an ECC or PSAP with the enriched information is quicker to arrive at the emergency, more prepared, and more knowledgeable in addressing the emergency situation. For example, in a boating accident in Florida, a retiree fell overboard and was tangled in lines in freezing water in mortal danger. He reached for his cellphone in a waterproofed pouch, and phoned 911. The RapidSOS-enhanced information informed a helicopter of his exact point location, including considerable background information on him, that led to his rescue and resuscitation (RapidSOS, 2021).

163

Figure 9.2 The RapidSOS emergency management system is illustrated in a diagram that shows sensors of emergencies that register an emergency situation and feed information into the firm’s 911 Clearinghouse of data that are processed and sent to government 911 Emergency Centers, first responders, and emergencies contacts

(Source: RapidSOS, in FinSMEs, 2018)

Location intelligence is at the cornerstone of RapidSOS’s business model. The model is based on smartphones which can determine location in multiple ways, including by GPS, triangulation with multiple cell towers, Wi-Fi connections, and signatures of neighboring signals. This yields very accurate location identification of callers’ mobile phones, including indoor location, and, in some circumstances, even emerging ways to identify 3D location. The latter has the potential to locate the emergency caller in a certain floor and room in a multistory building. Analytics are also crucial to the RapidSOS model in being able, during the time of an emergency, to data-mine large amounts of big data on health, social media, and business, and extract what is relevant for a particular emergency situation.

Although the US federal government seeks to enhance 911 calls with more contemporary enhancements, it is doing so gradually. The NextGen 911 initiative, supported by the National Telecommunications and Information Administration (NTIA) and National Highway Traffic Safety Administration (NHTSA), is on a slow track to work with states to make more contemporary information available to first responders (NTIA, 2021). However, this program would not match the extent of data provision that RapidSOS is seeking, nor does it include providing portals to ECCs and PSAPs.

The spatial strategies of RapidGIS evolved from Phase 1, which centered on innovation, to Phase 2, in which the firm pivoted its spatial strategy to “go to market,” while formalizing collaboration with Esri, GeoComm, and several other spatially-oriented businesses (RapidSOS, 2020; GeoComm, 2021). In Phase 1, RapidSOS developed its emergency support platform, which has geospatial data at its foundation. The heightened accuracy enables precise locating of a caller in 2 or 3 dimensions. That translates into first responders getting to victims crucial minutes earlier.

164

The firm also innovated in building strong, long-term relationships with leading real- time digital data providers such as Uber, Apple, and Google, in order to utilize their extensive real-time data to provide the first responder with enhanced knowledge of the emergency caller/victim, the emergency site’s built infrastructure, and a socioeconomic profile of the geographic area around the incident. RapidSOS additionally sought out ties with hundreds of PSAPs and ECCs, explaining what the firm could offer each of them.

In Phase 2, to get fast acceptance by PSAPs, the firm offered its portal product to PSAPs for free. RapidSOS astutely realized that, since a PSAP would be wary of the risk of holding its own extensive digital information, a free offer of RapidSOS’s geospatially-enabled portal could overcome the concern.

In the rush to market with the free portal product, how could RapidSOS generate revenues and assure profit in the long term? The answer is that RapidSOS mainly relies on payments from its data vendors, including Uber, Apple and others, which benefit in turn by having their data in use in the 911 space. In addition, RapidSOS garners revenue from companies which pay to add their apps to its platform installed in the emergency communications and dispatch centers -- an example of an ecosystem approach.

RapidSOS’s capabilities are evolving to offer analytic modeling of the likelihood of different types of emergency problems at the caller’s location. For instance, an emergency call arrives at an emergency call center at 8:05am from a smart phone caller at an exact location near a small Wyoming city, but the caller is incoherent and unable to reveal what the emergency problem is. Using the enriched locational data, RapidSOS can generate utilize predictive analytics to generate the most likely emergency problem to be a heart attack, with 45% certainty. On the other hand, RapidSOS must overcome concerns about data privacy and meet the challenge of providing consistently accurate data to over 20,000 emergency dispatch locations nationwide. Data violations can occur when the victim’s personal information informs first responders, constituting a tradeoff between emergency relief to the victim and his privacy.

This case supports the steps for forging a GIS strategic plan by a small, startup enterprise, while also demonstrating the challenges of achieving success. Of the GIS strategic planning steps from earlier in the chapter, RapidSOS included the vision and mission for GIS, intended GIS solution, and most other steps. What was not present in the startup’s GIS plan was to determine the net benefits for the firm and to assess risks. Phase 1 involved a disruptive strategy (Gans et al., 2018), which is a typical option for startups. Since there was innovation and upsetting of value chains, the emphasis was on rapid market growth without sufficient time or capability to assess risks and determine net benefits. Phase 2 focused on collaboration with major players (Gans et al., 2018). It allowed more clarity on net benefits. Risk was reduced by the cooperative tripartite agreement with GeoComm and Esri. Throughout Phases 1 and 2, location intelligence remained at the heart of corporate strategy and was integrated from the start with corporate strategic planning. As the company matures, we foresee that the firm will also need strategically to raise the quality of its data and to serve many thousands of emergency centers with consistently high reliability. Also, as emphasized in

165

chapter 7, we suggest the firm needs to include corporate social responsibility in its strategic plan, including assuring a standard of data privacy as well as equity and inclusion in providing services across cities and municipalities nationally.

For location intelligence business startups, two essential strategic decisions are encountered: (1) whether to compete or collaborate and (2) whether to be defensive, protecting products and technological advances, or to focus on rapid growth, development, and experimentation/risk-taking in the marketplace (Gans, Scott, and Stern, 2018). In both decisions, RapidSOS chose the latter option.

Geospatial Competitiveness Value-Added The Geospatial strategy needs to correspond to the company’s mission and vision. The

necessity for this is well known in studies of information systems (Peppard and Ward, 2016; Piccolo and Pigni, 2019;) and is noted for GIS (Pick, 2008, Carnow, 2019; Lewin, 2021). A business that has achieved consonance between its geospatial strategy and the strategic direction and mission of the firm has alignment, which has been shown to improve performance in the long term. Since the GIS leader is increasingly being invited in as part of the firm’s strategic planning process, strategic spatial alignment is becoming the norm. The more active the GIS manager can be in the planning effort the better. The manager may need to assert to senior management the importance of GIS by pointing to its role in addressing and mitigating pain points for the business, while indicating that the aligned GIS strategy can contribute to mission and result in tangible benefits for the company (Dangermond, 2019).

Sustaining the alignment of geospatial strategy and business strategy over time requires continuing effort, since technologies are changing rapidly and the outside environment is altering. Maintaining this alignment are often referred to as co-evolution (Peppard and Ward, 2016). It is important over time to continually adjust the geospatial strategy with the business strategy, in response to changes in one or both. An example of co-evolution in chapter 2’s Walgreens case study is the shift from an earlier strategy that emphasized US regional management decision-making utilizing GIS centered on regional management decision making, to an international strategy that encompasses the expansion of Walgreens through the Boots acquisition into a global firm, with an enlarged GIS strategy that seeks to provide spatial value to the Boots division.

Business value can be increased by prioritization of the initiatives in a geospatial strategic plan. In assessing priorities, the following considerations apply – (a) to determine what is the most important of the strategic initiatives based on expected value of benefits, (b) what is the enterprise’s capacity to undertake an initiative, based on the resources present, and (c) can an initiative succeed based on benefits and risks (Peppard and Ward, 2016). In assessing benefits and the management risks for GIS, it is useful to categorize the certainty of benefits compared to the risk of the challenges (Carnow, 2019), as pictured Figure 9.3.

166

Figure 9.3 A two-by-two table shows high and low geospatial risks and challenges crossed-categorized with low and high benefits, leading to four risk approaches, namely avoid, cautiously embrace, experiment and aggressively embrace

(Source: Carnow, 2019)

Using this matrix, the highest priority should go to initiatives with high benefit and low, management risk. These might be considered the “low hanging fruit.” A very risky initiative with high benefit should be regarded cautiously, while one with low benefit and low risk should be considered an experiment for testing. A high risk, low benefit initiative is given low priority. Prioritization is evident In the UPS case in this chapter, in which the ORION system initiative for years was judged as experimental, before being raised to the level at which senior management aggressively backed it.

Competitiveness Location Analytics can serve to expand competitiveness in a firm, leading to lower cost

of goods and services, to differentiating products and services, and to entry into specialized market niches (Porter 2008). For UPS, the powerful proprietary ORION software optimized daily routing of trucks, leading to significantly lower average cost of deliveries at scale. In another example, a small firm, GIS Consulting Inc. (anonymous name), was able to establish a strong niche in high-end location analytics software for sophisticated federal government agencies that deploy the applications in real-time, spatially dynamic environments. For instance, it designed a standardized spatial surveillance system for government vehicles and personnel that provided support for a US Presidential inauguration.

According to the “Porter 5-Forces Model” (Porter 2008), direct competition is a primary force a firm has to confront (see Figure 9.4). There are four additional forces that can disrupt and impact the competitive arena: new market entrants, substitute products, firm-buyer

167

relationships, and firm-supplier relationships (Porter 2008). GIS and location analytics can influence all four of these forces, offering strategic advantage.

 A new and successful market entrant can divert economic benefits from a company and its direct competitors—for instance, autonomous self-navigating vacuum cleaners, which use spatial algorithms with input from locational sensors to provide hands-free cleaning, have disrupted the competitive vacuum market.

Figure 9.4 Michael Porter’s model, relevant to formulating geospatial strategy, has the five competitive forces of direct competition as its central force, impacted by a competing firm’s forces of its supplier relationships and buyer relationships, and by new market entrants and substitute products

(Source: Porter, 2008)

 Substitute location-based products or services can usurp the benefits of an existing product or service by providing competitive market benefits. In the sharing economy, Uber or Lyft service substitutes for traditional taxi service by outfitting their cars with interactive mapping that displays customer and vehicle locations, as well as pick-up and transport routes in real time.

 Location analytics can have a positive influence on the firm-customer relationship in various industries and markets. For instance, at a large midwestern insurance company, the speed of response to impacted customers after a destructive event is improved through a GIS model that dispatches a team immediately to the large disaster site by spatially modeling it, according to the amount and type of loss by location. This rapidity of response, in turn, boosts customer satisfaction and loyalty.

168

 The last competitive force, the firm-supplier relationship, depends on the positioning of the supplier in the market, i.e. whether the supplier or the firm holds costing clout over the market. Government regulation can also be a somewhat unpredictable variable that affects supply. An example is the supply of coffee plants from hundreds of farmers in the Nespresso case study in chapter 7. The geography of the suppliers is crucial to costing, e.g. a large supplier in an optimal coffee growing area may have costing clout, enabling it to push Nespresso for higher pricing. The geography of Nespresso’s growers can be studied by location intelligence and decisions made to achieve more costing evenness between suppliers, reducing pricing power.

Collaboration Geospatial strategies should include an exploration of potential benefits of

collaboration. In contrast to competitiveness, collaboration involves mutual benefit to partner firms, so each partner gains in market position, value, strength of products and services, profits, and brand recognition. Geospatial collaboration ranges from informal sharing of ideas by individuals, to large-scale pooling of technological knowledge by firms, to formal collaboration on product development, marketing research, joint ventures, and co-branding.

Collaboration has been spurred to higher levels by digitization (Kiron, 2017), which can facilitate informal networks. An early study of GIS and networks of collaboration (Harvey, 2001), based on 212 interviews of GIS employees, found that collaboration of GIS employees at firms in Switzerland tended to involve multiple interpersonal networks that are frequently formed and dissolved. Groups remain stable if the perspectives of group members remain consistent over time. The groups included private-firm employees with a focus on collaboration with data providers at the level of cantons, governmental entities comparable to US states (Harvey, 2001). The cantons benefited the firms by providing a variety of spatial data including demographic and cadastral information.

A more recent example is RapidSOS, discussed in chapter 2, a growing smaller company that collaborates with big-tech companies for data provision, with local public emergency services to enhance real-time information flow to emergency workers, and with several large GIS firms on development of services.

Ecosystems constitute another form of collaboration that has been hastened by rapid digital transformation sweeping through industry sectors. An ecosystem is an orchestrated network of collaboration that extends across many industry sectors (Jacobides, 2019). Companies in the ecosystem have many common standards and share common services, software, and platforms. An example is the iPhone, which opened up to an ecosystem with other companies to share apps in its iStore. In that case the platform is shared collaboratively, even though the app firms may be in competition in some other business areas. In the case of RapidSOS, that firm’s portal is strategically set up, so it can serve as an ecosystem to other firms in the emergency response sector. The post covid-19 business world is likely to verge even more strongly towards digitization, which will stimulate strategies that encompass geospatial

169

ecosystems. A geospatial corporate hub might attract sufficient external interactive participants to be considered as an ecosystem.

Sustainable Advantage Companies are increasingly being held accountable for the impacts beyond the

“bottom-line.” The triple bottom line adds accountability for social and environmental net benefit. Some companies’ business and GIS strategies go beyond the goal of location monetary value-added to formalize the social and environmental goals.

In the case of Nespresso, Chapter 7 described how deeply it is engrained into their strategy and practices. Figure 9.5 illustrates the extent to which Nespresso embodies that broad strategy for sustainable advantage. The priorities under the “Positive Cup Framework,” Nespresso focuses long-term sustainable coffee supplies, analytics to support farmers, transparent communication to customers, and responsible practices in communities. These priorities are executed via two dashboards: F.A.R.M.S to analyze and management farm activities and the AAA Sustainability dashboard to manage and track sustainable practices and key performance indicators. Finally, in terms of implementation, the focus in on impacts in terms of increased efficiencies in coffee production, progress toward achieving 100% of supplies from AAA compliant farms, and progress in achieving 11 (of the 17) United Nations 2030 goals. As part of the sustainability strategy, GIS and location analytic strategy is to apply mapping to farm coffee more sustainably.

Figure 9.5 The Nespresso case study from Chapter 7 is illustrated with respect to its particular strategic components of setting priorities, determining strategy, and implementing the strategy

(Source: Author)

Request this Figure to be re-drawn.

170

Concluding Case Study: Kentucky Fried Chicken This case introduces locational strategies in the context of KFC, a global fast-food

company that has a distributed, franchised organizational structure. This structure contrasts with UPS’s more centralized management structure. The challenge for strategy shifts from centrally optimizing a routing and logistics system to supporting a variety of spatial needs across numerous franchises with varied degrees of autonomy.

Kentucky Fried Chicken (KFC) is a global brand that represents over half the unit business of Yum! Brands Inc., a firm that also includes Taco Bell as about one third of Yum! units, while Pizza Hut accounts for a sixth. Yum! is basically the franchisor but now operates only about 2 percent of its franchises. KFC has hundreds of franchise owners worldwide, except for in China, where it was spun off as a separate company. Franchise owners own between 1 to over 800 franchises. They are often independent in their thinking and strategies, although constrained by the terms of the franchise agreements. The thrust of the past decade has been for KFC to eliminate its ownership of franchises, so Yum!’s revenue shrank to $5.9 billion in 2018, but the company’s profitability has increased.

Until 2016, KFC did not have in-house GIS, although Yum! Brands did have GIS globally. However, KFC was siloed from Taco Bell and Pizza Hut, so when the corporate manager of GIS was hired in 2016, he was told, “We’re not going to run you from the top; you’re going to run yourself” (Joseph 2019). The GIS manager has a staff of two, a GIS analyst and business analyst. The GIS group interfaces with a much larger IT group, which has vast responsibility in supporting the back of the house for over 4,000 franchisee restaurants as well as supporting corporate functions.

The focus of the GIS group is to provide competitive spatial tools to the thousands of franchises worldwide. That includes customized applications, data, and training. Although the GIS group has access to franchisee data, it is careful not to share the data among franchisees, since they sometimes compete with each other.

The GIS group has key technology partners in Esri, Maptitude, and Intalytics, now a part of Kallibrate, which produces its branded SiteIntel platform. The SiteIntel platform provides location-based data visualization, forecasting, and reporting for real estate and marketing, based on a wide variety of current data sources. The GIS group adopted SiteIntel and has customized its platform in different versions for internal uses versus external uses that are franchisee-facing. This software indicates, at a large scale, i.e., in great detail of mapping, where all the KFCs are located, where all competitors are, the location of important generators of customers, transportation routes, traffic volumes, and demographics. The spatial tool has functionality for the user to add and superimpose layers, run reports, and perform some spatial analysis. Additionally, the GIS analyst on the team receives numerous general requests for spatial analysis, location analytics, and professional mapping.

The GIS manager classifies KFC’s spatial maturity as about a third of the way along, so there is plenty of opportunity to elevate the GIS to a higher maturity level. Several of the key GIS accomplishments to date are building the GIS capabilities into important parts of the

171

franchisee’s marketing plan and then loading the visualization of promising locations for new KPC restaurants. These can be viewed internally but also, through SiteIntel, by franchisees seeking growth areas. The potential locations on a map for high growth are termed “market calls” and the software enables them to be prioritized. Recently KFC has acquired massive mobile-device data, over which to overlay customer sentiment from social media.

The Intalytics models also provide analytical tools based on the competitive details in the immediate neighborhood of a proposed or existing KFC location, including locations of competitors in nearby blocks, signage, traffic flows, vegetation blocking view lines, and other detailed neighborhood features. For larger geographies, important work is being done in trade area analysis, using demographics and geo-segmentation. Sometimes Intalytics sends a team of surveyors to a restaurant to ask questions, understand its location, and perceive where the restaurant is heading. This can be helpful in helping a franchisee avoid choosing an underperforming location or treading on another franchise’s territory and customers. This is an area in which social-media spatial big data could be helpful in the future.

So far, KFC’s spatial strategy has been tactical and very practical, which would correspond to a moderate spatial maturity level. The KFC strategic planning team can observe other Yum! brands to find out what works spatially for their restaurants in the US or overseas. The strategy is to focus on development of GIS applications rather than outcomes, because KFC-corporate has reduced power over the spatial decision-making, especially in overseas franchises. Part of the spatial strategy is instilling franchisees with knowledge of their markets, of locational opportunities, and where they can build or be looking to build. Ultimately the spatial strategy is focused on the franchisees’ priorities.

In sum, GIS within KFC has been restricted to being a support entity within a franchisor, concentrating its spatial group to support the hundreds of separately owned franchises and 4,000 plus franchisee restaurants. The small GIS team has aligned its spatial strategy with the corporate business strategy and with IT, and it has reached out to collaborate with the other internal units as well as with franchisees worldwide. With GIS planning maturity at a moderate stage, there is still room to provide enterprise-wide, competitive location analytics to the corporation in the future.

172

Chapter 10

Themes and Implications for Practice

Introduction The book has provided a wide-ranging examination of Spatial Business with the aim of

providing a contemporary foundation for understanding the business and locational knowledge base for integrating location analytics into the functions and goals of business. As noted at the outset, our approach was first to consider the Fundamentals of Special Business (Part I), then focus on Achieving Business and Societal value (Part II), and finally consider managing and leading Toward Spatial Excellence (Part III). This concluding chapter summarizes key themes at each phase and outlines a set of 10 Implications for Practice that are derived from these themes (See Figure 10.1).

Figure 10.1 Spatial Business: Themes and Implications, showing ten themes and implications for practice associated with three sections of the book (lessons in light blue boxes and the sections of the book in dark blue boxes)

Source: Author

Request this Figure to be re-drawn.

173

Fundamentals of Spatial Business: Themes and Implications

1) Identify and Enhance the Location Value Chain The book has emphasized the location value chain as an important conceptual lens for

examining location analytics across a business enterprise. Growing the value chain can elevate an organization from a spatial novice, when GIS initiatives are localized in a particular function, to spatially mature, when GIS deployment, driven by business needs and strategy, spans enterprise-wide across multiple functions.

Location value underpins the location value chain. Not every function in a business is rooted in location. While not exhaustive, common functions found in a location value chain include:

 Research and Development

 Business Development and Sales

 Supply Chain Management

 Logistics

 Real Estate Strategy

 Marketing

 Operations

 Risk Management

 Community

 Environment

Organizations can use location analytics to heighten the value contribution of individual business units or combinations of business units. This in turn enhances the customer experience, creates opportunities for business expansion and competitive separation and generates benefits for employees and the wider community.

174

Implications for Practice:

 Use the “spatial decision cycle" to engage business managers and cultivate internal champions. The book outlined a spatial decision cycle that provides a rubric to enable spatial thinking by considering key business goals, locational elements of that goal, location analytics that can be used to contribute to that goal, and the data needed to achieve desired produce valid and meaningful results. With a firmly identified location value-added that can be achieved, business managers can work with both technical experts and senior management to execute analytical processes that can contribute to gains in key parts of the business value chain.

 Identify business functions with greatest tangible value from location intelligence. Location analytics can help achieve important strategic goals such as customer growth, effective operational management, and risk mitigation. This may occur within the context of a given function (such as marketing) , but ideally would evolve through connections to related value chain domains (such as R&D and supply chain), providing an avenue to achieve strategies and metrics not viable by other means.

 Whether the company is developing a service approach, developing joint marketing strategies, or growing supplier diversity, there is no replacement for joint visibility and measured action planning. For example, in the area of revenue generation, location analytics can used to identify and market to high performing locations. In the area of regulatory compliance, location analytics can demonstrate compliance. In operations and risk management, examples such CSX and Walgreens demonstrated the value of tangibly demonstrating the capabilities of location analytics to important business goals, especially filling both an immediate need and a strategic opportunity.

Consider the benefits of shared value where data and insights from one function may be valuable to another. The Shopping Center Group (TSCG), a leading national retail-only service provider has four main service lines: tenant representations, project leasing, retail property sales, and property management of those retail properties. They consider location analytics as core to each one. While not so streamlined for every company, TSCG executives consider the use of location analytics to be a key contributor to their 30 percent growth and its emergence as a commercial retail and information company.

2) Enable Spatial Maturity Pathway The spatial maturity ladder is wide-ranging among contemporary companies,

progressing from novice to enterprise-wide, competitive status. The book outlines the concept of spatial business maturity, drawing upon the analytics maturity model proposed by Davenport and Harris (2017). Factors that distinguish mature location analytics competitors from the others are:

 Value of location-based business and customer insights

 Sponsorship and support of location analytics initiatives from senior leadership

175

 Clear and coherent business strategy

 Clear articulation of ROI of GIS and location analytics initiatives

 Availability of best-in-class GIS and location intelligence technology

The spatial maturity model can offer a pathway for organizations that may currently have location analytics siloed within a specific function or department but aspire to deploy location intelligence as a competitive force and as a driver of spatial transformation.

Implications for Practice:

 Provide opportunities for location value to gain broad acceptance. As shared in the John Deere case study, opportunities will come from all levels of the company as benefits are experienced. Such value will get expanded upon to benefit other areas of the business, and soon, like with John Deere, location intelligence will be poised to catalyze innovation in every part of the company's value chain impacting farmers, dealers, and consumers while transforming the company to a techno-centric agribusiness. A company will be confident that profitability is maximized, current resources are optimized, risk is mitigated or avoided, and market share is captured. The tools used in the “business of modern-day farming” echo that of other business development and operations leaders alike, helping them improve yield, increase productivity, lower costs, and achieve more precision ahead of the trend.

 Move toward deeper analytical uses. Maps and interactive business analytics dashboards and applications provide immense value to understanding market potential, current status, plans, and the relationship between all of them. Location analytics adds significant value by quantifying and enriching data - calculating, measuring, and assigning - using real-world constraints and context with real-time feedback. Companies such as Travelers Insurance now employ a full range of location analytics to assist a range of business-critical functions (Travelers Insurance, 2021). These include predicting the location of natural disasters (for underwriting purposes), analyzing damage locations (for claim purposes), and identifying high-priority locational impacts (for disaster response). These tools have been used with remarkable success for recent hurricanes on the east coast and wildfires on the west coast (Claims Journal, 2019).

3) Match Location Analytics Approach to Business Goals and Needs By using analytics to make better decisions, organizations generate value that manifests

in the form of business benefits, both tangible and intangible. These include cost savings, revenue growth via an increased share of wallet, uncovering business opportunities and untapped markets, increase in productivity, process improvements resulting in improved asset efficiency, enhanced brand recognition, increase in customer satisfaction, and benefits to the environment and society. A critical element in achieving this success to match the right analytical approach to the business goal or need being addressed.

176

While there is no right place to begin, the methods of location analytics often parallel the spatial maturity of an organization. The descriptive approach and visualization may gain initial attention and develop links on the location value chain, while the prescriptive and predictive approaches will garner the greatest executive visibility as they result in more directive findings to support the future growth, competitive advantage, and collaborative potential for the company.

Hot spot mapping, route optimization, demand coverage, cannibalization examination, and relocation strategies are examples of spatial modeling approaches. They support a company’s ability to best understand its position and orient its product or service to its customers while optimizing resources. In cases like these, location analytics provided awareness, spurred enhancement of an existing process or the creation of a new process, and then evolved into supporting strategic plan development. In digitally mature organizations, like John Deere, digital technologies are not just an add-on to existing processes and practices; rather, they prompt such organizations to rethink how they do business. Of these technologies, business analytics—the paradigm of fact- and data-driven decision-making—has been rated by business leaders as the main driver of digital transformation in their organizations, surpassing AI, IoT, mobile, social media, robotic process automation, manufacturing robots, and VR (Kane et al. 2017).

Implications for Practice:

 Engage audiences through descriptive visualization. Smart Maps and visualizations have always provided keen descriptive insights that help decision-makers understand what happened (in the past) or what is happening (in the present) in their stores, sales territories, and service areas, out in the field, or at a supplier location thousands of miles away. Today, powerful dashboards elevate the descriptive power of location analytics and broaden its reach. Dashboards composed of smart maps can be used in any part of the location value chain to provide situational intelligence in real-time and facilitate reporting, which is crucial for decision-makers. Depending on business needs, they may be internal- or external-facing.

 Consider long-term business decisions. The predictive power of location analytics is becoming increasingly important as businesses navigate an uncertain world. The cost of business disruption can be tremendous, as was evident during the COVID-19 pandemic. Prescriptive analytics is tied directly to support of decision-making--for strategic planning or within an established system’s process. It reconciles a broad array of factors and criteria that may sometimes be conflicting. This provides a foundation for decisions and actions that enable process-, department-, and/or enterprise-wide optimization, ensuring business success, improving resiliency, and enhancing competitiveness in both the short- and long-term.

 Work toward location intelligence. Businesses ultimately seek the insight of location to help achieve their mission and adjust to the unanticipated activities of the world. They seek the simplicity in sorting through the business, social, economic, infrastructural, and

177

climate insights which impact them today and aim to adjust in order to achieve future goals. Through the results of location analysis and data visualization, companies can gain insight and solidify understanding. They achieve location intelligence which helps them define their circumstances and set measurable strategic and tactical courses for what to do next.

4) Build a Data-Driven Spatial Business Architecture The “Spatial Business Architecture” outlined in the book provides a framework for

building a technical foundation for conducting effective location analytics. The architecture begins with the business goals and needs, then business users who have responsibilities for addressing these business goals and using location analytics to do so. The architecture continues with a series of location analytics tools, tools that depend on various forms of location data. Underlying all of these functions are the various platforms that host spatial business processes, such as the cloud, the enterprise, or mobile services. The final component is the net consequence in term of location intelligence that can be used in provide insights, inform decisions, and have an impact on business performance relative to identified business goals and needs.

The style and extensiveness of the Spatial Business Architecture developed by business can range from a relatively “light’ style, with online or desktop use of internal customer data, to a broader enterprise platform with diverse applications, data-sets and user stakeholders. As the enacted architecture grows, a mature digital business requires a mature data strategy to set up the proper architecture, flow, and governance of data. Without one, masses of jumbled data streams—a “data hairball”—can complicate product development and fail the primary insights they are intended to support. The development of mature data strategies is designed to quickly cut through the data clutter and align data streams with business initiatives and priorities and dictate the proper use of the data.

Today, location data is ubiquitous. With the increasing use of smartphones, geotagged sensors, and other IoT devices, and the rapid diffusion of unmanned aerial systems (UASs), the availability of location data is at an all-time high. Mobile geolocation data has spawned an entire industry of providers of this data that companies are just beginning to discover. Also prevalent is geodemographic segmentation data which helps companies draw generalized conclusions about customers and markets.

As companies become voracious consumers of mobile location data, the ethical use of data remains an urgent consideration (Thompson and Warzel 2019). However, with ethical standards of data aggregation in place, with a heightened awareness of implicit bias, and proper internal governance policies companies can continue to derive valuable intelligence using location analytics.

Implications for Practice:

178

 Gain competitive advantage through location-savvy information products. For location forward companies like Travelers who are spatially mature, managing, leveraging, and planning for new location data gives them a competitive advantage. Their science originated in underwriting where the use of third-party data was prevalent and location intelligence was at the core of their business. Their third-party data is part of the data strategy. They have built their systems and algorithms with location value at the forefront. Because of this they can quickly take advantage of new data in the market and continue to provide premium services to their customers.

 Use well-crafted location analytics to strengthen partnerships. As location analytics becomes embedded in an organization, the resulting business benefits can grow to impact numerous goals and stakeholders. Building trust through insightful and high- quality data products yields new customers, new revenue streams, and closer partnerships internally and externally. Trust builds relationships with customers through achieving a competitive advantage like Travelers, Natura, and Nespresso. Similarly, trust builds relationships with suppliers and partners in having a tangible and measurable way to work better together.

 Develop spatial data as part of the location analytics strategy. Many companies are considering how their data can be utilized to create a new revenue stream or to make services available for outside use. Central data organizations with C-suite participation and a Chief Data Officer to support the switch from data for business unit reporting to a more strategic level of use. For third-party data, the imagery sector is enriching spatial business by providing base maps, point clouds, space-time imagery, AI-enhanced map imagery, and raster analytics and modeling (Dangermond, 2021). Satellites, planes, and drones tend to have digital image collectors, including radar collectors, lidar, multi- spectral collectors, digital cameras, and GPS. Various modes of collection should be considered for appropriateness for certain business applications, as each mode has pluses and minuses (Sarlitto, 2020). Social media with location features has become commonplace for individual “social” users, and increasingly analytics teams in businesses are employing the power of location-based social media to better serve their customers.

 GeoAI can support decisions at scale. GeoAI models are likely to play a central role in producing location intelligence at scale to predict demand spikes, identify high-margin prospects, anticipate disruptions in the supply chain, and speed up logistics delivery services (Raad 2017). Already, in the insurance industry, deep-learning-based models are expediting decision-making in damage assessment, claims processing, and fraud detection. This can be used to forecast sales, manage inventory levels, prescribe location-specific target marketing campaigns along with individualized engagement and retention strategies that minimize the risk of attrition (Davenport, 2018).

Achieving Business and Society Value: Themes and Implications

179

5) Use Market and Consumer Intelligence to Drive Business Growth Location intelligence is used competitively in global, local, and hyperlocal markets. Rich

geographic information increases competitiveness, fosters new products and services, and provides new ways to strengthen ties with suppliers and buyers. Business expansion, marketing, sales, and service have firm location value as a core element to decision making in short- and long-term growth opportunity assessment.

Marketing is strengthened by location intelligence because of the ability to target, measure, and communicate. The location of customers, company stores, storage facilities, competitor facilities, transportation routing, geodemographic and social media metrics and intensities, all contribute to the total picture. Depending on the nature of the question asked, location intelligence can be made available at different unit scales, such as county, zip code, city, census tract, and individual. For instance, marketing data from major social media vendors are utilized by RapidSOS to improve the accuracy and timeliness of the emergency response of its local and regional governmental clients.

New operations growth, new service revenue growth, and business expansion are all uniquely benefited by location thinking as models can predict market share, overall sales, revenue, and profit potential of trade areas which will drive service and optimization. It is the ability to measure and target spatially that allows companies to see their results in satisfied customers, higher market penetration, a higher net promoter score, trusted service relationship with suppliers, and improved company engagement at each venue and online.

Implications for Practice:

 Understand and predict consumer behavior. For Business Development and Sales, companies are increasingly combining proprietary customer data (for example, website, mobile, CRM) with data from social media feeds and household virtual assistants to generate insights about a customer's mindset and a holistic 360˚ persona. This can be used to forecast sales, manage inventory levels, prescribe location-specific target marketing campaigns along with individualized engagement and retention strategies that minimize the risk of attrition (Davenport, 2018). Increasingly, CRM systems are beginning to add in spatial data access, and visualization as omnichannel engagement becomes widespread. This gives fuel to GeoAI models which are being used to provide rich, dynamic insights into customer behavior.

 Bridge digital and physical to meet customers where they are. Applying this insight into the physical world, this data in combination with new data from third parties, like anonymized human movement data are coupled with more traditional socio- demographics and consumer spending data to become the foundation for decision- support prescriptive modeling and ongoing monitoring. As a classic example, in the Kentucky Fried Chicken case study, location intelligence furnished the firm and its franchisees with rich and varied social media and demographic information at the micro level for decision-making on competitive locations of retail units. Such information is utilized to support real estate investments and lease agreements in most retailers and

180

QSRs, as well as in an ongoing way to set up and evaluate KPIs associated with each unit in its trade area classification or cluster. Stronger correlations can be drawn to improve the connection between digital and physical interaction to achieve higher customer conversion metrics, improve the experience for the customer, and find savings in optimized product assortments.

 Apply across business functions to win priority markets. In marketing, companies consistently measure their opportunity and learn about their priority markets to engage the best go-to-market course of action. A location-based approach draws the connection to the P’s of marketing through sales, investment, and planning. This was illustrated in the Fresh Direct case study. The output of activities from one function flows to the next from marketing through sourcing, distribution, and delivery. It allows for the coming together of disparate data and analysis to inform the complete process, avoiding disjointed work, and providing a manner to bring functions together for better planning and nimble response. In this case, there was a unique beneficial by-product for FreshDirect. They were able to optimally invest in trucks, some with refrigeration and some without because they knew they would be able to maintain safety standards with their planned delivery windows.

 Use service area analytics to gain a competitive advantage. As businesses are increasingly focused on the customer experience, the service areas play an equally critical role and benefit both the happiness of the customer as well as cost and operations efficiencies internally. Like with the FreshDirect delivery trucks and CIDIU’s environmental services, it is the ability to measure, monitor, and target that drives the business initiatives. By integrating IoT, GIS, and optimization modeling and using location-aware sensors, CIDIU S.p.A. was able to completely eliminate the third shift for the entire service area. In addition, the number of vehicles used during a test period decreased by 33% reducing waste-collection operational costs and augmenting the company’s competitiveness (Fadda et al, 2018). A third benefit is showcased in the Cisco case study where premium service areas were developed and sold without incurring additional operating expense resulting in near total profit.

 Analyze locations for achieving long-term market growth. For long-term growth, the ability to evaluate underpenetrated and underserved areas helps a business weigh its future investments through partnerships or acquisitions. Location analytics helps set up the desired outcome through thorough evaluation of multiple variables like risk, country development stage, competitive reach, transportation, utility resourcing, compliance, and climate variables. The results of which may lead to initial sales testing or operations research to get the feel for greenfield growth, potential acquisitions, and sales potential even 10-20 years ahead of planned investment. With location intelligence, businesses can conduct granular or hyper-segmented analyses of business development opportunities or reorient supply chains for significant savings like in the Proctor & Gamble example.

6) Measure, Manage, and Monitor the Operation

181

This is a highly varied, highly process-oriented part of the organization and historically the center of value. It spans from situational awareness to facilities, to supply chain and logistics. Quality, consistency, cost, and continuous improvement needs are high in these functions, and depending on the industry, so are capital investment and sustainability efforts. Increasing profitability with optimal resourcing has location value chain linkages in service delivery, business growth planning, vendor selection, asset management, distribution, and sustainable development.

In manufacturing, for example, production cannot happen without safe buildings, well- functioning machines, reliable services to make them run, and transportation to get them to market. Hundreds of millions of dollars in capital investment are spent on new plants and the tools to make products. Factory automation is continuously advancing and there is an ongoing interest to make products to order. These two needs alone illustrate the need for location- based asset management and distribution network planning as a necessity. Additionally, building automation, energy optimization, and predictive maintenance are also focus areas for companies maintaining physical space, offices, or leasable space. Spatial data about facilities and assets serves as a solid foundation for each of these and provides the framework to see them all in an integrated matter. At a minimum, this helps an organization boost data accuracy, reduce errors, adopt automated workflows, and improve efficiency.

With the improved facility and process monitoring a company will see higher product yields, decreased expenses, and satisfied customers through more timely delivery of quality products. Companies will have a unique ability to nimbly respond to significant pressures like COVID-19 and disruptions with far greater speed and ease as well as open doors to new revenue streams. The combination of facility, supply chain, and asset data in various permutations with a location-driven approach will very quickly alleviate the pressures such as was the case in the aftermath of the Fukushima incident in Japan.

Implications for Practice:

 Use real-time tracking to achieve operational benefits. Transcending industry verticals, the real-time tracking and monitoring of asset location and condition can improve productivity, prevent breakdowns, ensure safety, and reduce costs. The book provides examples of GIS coupled with other technologies and data such as IoT-based sensors, drones, augmented reality, mixed reality, radio-frequency identification (RFID), and machine learning to provide sophisticated real-time geotagged data and locational insights of considerable business value for operational as well as tactical decision- making in near-real-time. This was exemplified the Tampa broadband service dashboard that monitored critical dimensions of broadband service delivery. Location intelligence can also guide the dynamic navigation of field assets (people and vehicles), reducing travel time and ensuring that service time windows are honored. In the event of breakdowns or emergencies, location intelligence can reroute drivers and vehicles, ensure safety, and maintain the timeliness of operations.

182

 Consider indoor analytics for improved profitability. While outdoor and mobile assets are more commonplace to monitor, indoor spatial relationships are increasingly developed, measured, and tracked. As another cross-industry function, manufacturers, retailers, real estate companies, and public facilities benefit in unique ways. In manufacturing, efficient layouts can increase the productivity of production lines. In retail, customer experience is paramount, so companies are improving their layouts to avoid congestion, aligning basket analysis with product placement in-store, as well as engaging their loyal customers with a premier experience. This helps firms design the layout of facilities to increase shopper traffic as well as average transaction values and profitability (Hwangbo, Kim, Lee, and Kim 2017).

 Use location analytics to enhance supply chain visibility. Facilities themselves are also a critical node in the supply chain, the network of facilities for a company is a balance of cost, risk, and regulation. Digitizing and mapping a supply chain as part of organizational digital transformation can have several benefits. For a global business, it can provide a reliable operating picture of how the supply network chain performs and where it might be failing. In addition, a digital, fully mapped supply chain can deliver valuable situational awareness powered by real-time alerts and notifications, asset tracking, and monitoring. It can also identify geographic stress points that may be prone to risks. This can help accelerate a company’s response time during the normal course of business and enable it to plan, prepare and respond in an emergency.

 Consider new technologies to provide holistic views of operations. Real-time tracking and monitoring of asset location and condition can improve productivity, maintain uptime, ensure safety and reduce costs. In addition to IoT, GIS technology is also being coupled with the concept of a digital twin—a virtual replica or electronic counterpart of physical assets, processes, or systems (Tao, Zhang, Liu, and Nee 2019). GIS mapping provides a holistic overview of system performance - people, assets, sensors, devices, and other services on the day-to-day but also in relation to disruptions. It allows for the change in condition or disruption to trigger notifications, maintenance, or necessary interventions to assure a product’s safety, prevent costly damage or loss, and disruptions to customers.

 Use platform to integrate disparate operational data. Th book details how managing a supply chain effectively starts with combining internal organizational data with external data from varied sources. As these disparate data streams reside in individual silos, their business user needs, whether upstream- or downstream-facing, are equally siloed. However, as an interface, a Geospatial platform acts as an integrative platform between the siloed data streams and use cases. The platform enables users across the enterprise with different business needs to collaborate, drawing upon descriptive visualization of georeferenced datasets, which provides the foundation for predictions, decisions, and informed actions across the network.

183

7) Mitigate the Risk and Drive Toward Resiliency Imagine a company’s asset igniting a fire due to poorly managed maintenance. Perhaps

the asset starts a wildfire that ignites thousands of homes or a plant. The fines, product shortages, or bad press could financially damage or even bankrupt a company. Or perhaps an uncharacteristic freezing weather event or a hurricane halts power distribution and stops an entire material supply chain and creates housing challenges for company associates, servicers, and customers alike. Imagine also a washout from severe weather rendered a railroad track unusable as occurred with CSX railroad in 2018. While such events can’t be predicted far in advance, scenario planning, action planning, and ongoing monitoring provide a more proactive environment for risk response. Every aspect of a company can be affected by risk yet there is not one function that holds ultimate responsibility.

In confronting external risks such as geopolitical crises and natural disasters, the lack of a visible plan can render CEOs and their organizations vulnerable. Recent surveys have shown that during such times, two out of three CEOs feel concerned about their ability to gather information quickly and communicate accurately with internal and external stakeholders (Pricewaterhouse 2020). This was proven accurate upon the start of the COVID 19- pandemic where every company wanted to know how their operations were going to suffer and what regions may be more greatly affected. Hurricanes and uncharacteristically bad weather make such needs come alive as well.

Risk means different things to different companies and to different people. Travelers Insurance, Bass Pro Shops, Mid-South Synergy’s Electrical Cooperative, and CSX manage their risk and resiliency in different ways with location at its core. Travelers has a competitive advantage by pricing more accurately and processing claims quickly for improved customer service. Bass Pro Shops’ dashboard of store and associate status provided much-needed visibility in the changing climate of COVID-19 spread. Both the Mid-South and CSX are asset- based companies and need to have awareness of the status of their assets to provide service to their customer.

Implications for Practice:

 Reduce business risk and improve resiliency. Risk and resiliency preparations concern physical disruption, brand image, competitive advantage, and impact on communities and the environment. Using location analytics, companies can gain a new way to measure and initiate action ahead of time, gaining the advantage of being proactive instead of reactive. With improved visibility there is potential to adjust to events and have decreased operational downtime, safe teams, measured climate action, increased customer service, as well as partner and stakeholder engagement.

 Use platform to achieve greater supply chain visibility. Beyond man-made or natural disaster disruption, compliance and sustainability issues are also prevalent. The need to understand a supplier or plant’s location in context to labor practices, water, and energy resources, is paramount to sustainability. Companies are typically aware of the primary address for their Tier 1 suppliers but have little insight into the network of facilities

184

supporting the Tier 1 and their suppliers farther upstream. Gathering data to improve location understanding allows for companies to set baseline metrics and initiate plans to improve and or maintain standards across their supply chain and in line with their commitment to sustainable practices and ethical standards.

8) Enhance Corporate Social Responsibility In 2019, leaders of almost 200 major corporations in the United States redefined the

purpose of a corporation to promote an economy that works for all. These Business Roundtable leaders recognized that the success of a business enterprise can no longer be viewed solely through the lens of corporate profits and shareholder value. Instead, they affirmed that businesses play an essential role in improving social conditions, both locally and globally. In many ways, this is a rediscovery of centuries-old business traditions where successful Japanese traders and merchants understood that to build an enduring, successful trade, every transaction must benefit buyers, sellers, and local communities (Kantor and Peters 2020).

This expansive role of the business to address social, racial, economic, health, and educational inequities has been exacerbated worldwide by the COVID-19 pandemic. As corporate leaders navigate their businesses through increasingly uncertain business and geopolitical environments in the post-COVID world and are pressured to achieve growth, they are also at the forefront of shaping their organizations' role in confronting and addressing these inequities. At the same time, they confront an equally urgent challenge in the climate and sustainability crises. In each of these areas—whether it is building a racially diverse and equitable workforce, committing to supply chain transparency, engaging in sustainable business practices and environmental stewardship, or going above and beyond regulatory requirements to be true agents of change—location analytics and intelligence can play a role. Managers and leaders of the 21st-century businesses are organizing in areas such as racial justice, CSR, sustainability, and shared-value. All these issues have locational components.

Implications for Practice:

 Measure progress on sustainability goals. Historically, there were few ways to measure business impact on society. With the ability to digitally model a business in space and time and bring together the extensive global data sets available it is now possible to examine the current state as a baseline and begin to measure changes to be able to set goals and monitor progress for the community, environment, and human rights to build even greater global resilience. Nike, Nespresso, and VF Corporation are leading the way in getting deep into their supply chains and making the product origins and impact on local communities known.

 Inform end consumer about supply chain performance. The reputational risk and cost of engaging with suppliers who unethically source raw materials or manufacturing partners who do not hire locally, pay fair wages, or engage in child labor can be immense. More and more companies are providing interactive mapping to showcase their global supply chains to engage with a variety of stakeholders. USA-based Nike and UK-based Marks & Spencer were both early adopters in making their brands visible and

185

offering insights about individual factories (Bateman and Bonanni, 2019). VF Corp. took this a step further and created traceability maps of its brand name products that communicate to consumers exactly where raw materials for a particular product was sourced, where it was manufactured, assembled, and shipped for distribution, and how components of the product flow between different facilities in the supply chain.

 Deliver insights that balance stakeholder needs. The Natura case study showcases how a popular line of beauty products launched in the early 2000s with ingredients native to the Amazon rainforest in Brazil maintained its commitments to biodiversity and environmental stewardship while generating sustainable competitive advantages for the company (Esri 2015). After solving the initial difficulties of sourcing logistics in the rain forest, building strong supplier relationships (Boehe, Pongeluppe, and Lazzarini 2014), and building out the supply chain network the company’s Geospatial platform now gathers location intelligence to make decisions about sourcing, pricing, and distribution addressing the needs of all stakeholders - farming cooperatives, consumers, and shareholders. By using a "quadruple bottom line" approach that balances financial, environmental, social, and human objectives, Natura has continued to diversify its product offerings using an expanded array of supplier communities and bio-ingredients while simultaneously protecting the Amazon and committing to the ethical sourcing of biodiverse ingredients (Natura, 2020).

 Build climate resilient infrastructures. Companies are addressing resilience as related to climate. AT&T is addressing consumers’ safety and security by actively researching at- risk zones and adjusting their fiber and cell networks to improve coverage. Through their Climate Resiliency initiative, the company is building a Climate Change Analysis Tool. Using data analysis, predictive modeling, and visualization, this tool enables AT&T to react to climate changes by making the adaptations necessary to help increase safety, service, and connectivity for its employees, customers, and communities (AT&T, 2021)

 Guide community investment. In the case of JP Morgan Chase, one of its exemplary CSR projects has been a long-term program to help small businesses thrive in the city of Detroit, Michigan which had been economically declining for many decades with small businesses bankrupt. JPMorgan decided to foster a campaign named “Invested in Detroit” across the downturned neighborhoods to seek to rebuild the city and its economy. As the company was a signatory of the Business Roundtable statements on CSR and has embedded CSR as part of its culture, the bank discovered it could help these businesses most effectively by using loans which are keyed to low-income areas to help disadvantaged small enterprises and nonprofits. The bank succeeded with loans to three initial micro-districts and will scale scaled up to a dozen more while rolling out this CSR approach to depressed areas of other American cities. JPMorgan is also realizing eventual financial benefit since in Detroit it holds $20 billion in deposits. Hence, the "Invested in Detroit" project can stimulate long-term business growth and conversion of many new and renewed businesses into bankable entities adding to JPMorgan's market share of deposits and loans (Heimer, 2017).

186

 Create dashboards that support global collaboration. At present, the world remains engaged in the COVID-19 pandemic dashboards, like the one published by Johns Hopkins University, where all industry, government, and health leaders are using location intelligence to come together to take their role in providing visibility and metrics of spread and hospitalizations, developing accurate spatiotemporal forecasts of disease transmission, identifying specific on the ground needs, and coordinating with targeted relief all over the globe with supplies, equipment, and personnel. Direct Relief and the Facebook AI Research (FAIR) team collaborated to forecast the spread of COVID- 19 in the US combining reliable first- and third-party data on a wide variety of important factors such as confirmed cases, the prevalence of COVID-like symptoms from self- reported surveys, human movement trends and changes across different categories of places, doctor visits, COVID testing, and local weather patterns. (Le, Ibrahim, Sagun, Lacroix, and Nickel 2020).

Toward Spatial Excellence: Themes and Implications

9) Develop a Spatial Strategy and Capacity A coherent spatial strategy and the capacity to execute this strategy are both essential

for location analytics to make a significant impact. The strategy brings together the business goals and needs with the ability of location analytics to contribute to those goals and needs. Typical elements of a spatial strategy include the following:

 Determination of the vision and mission of location analytics as a component of the company.

 Identification of the business issues, opportunities, and needs that location analytics is intended to address.

 Analysis of locational dimensions to current and potential company business lines and operations.

 Estimation of tangible and intangible benefits expected from the spatial strategy, including costs and risks associated with pursuing these benefits.

 Identification the current Spatial Business Architecture components and determination of architecture elements needed to achieve the strategic objectives.

 An operational plan to implement the spatial strategy and key performance indicators to be measured and tracked.

A leadership team for spatial strategy should include both those with technical and business expertise to ensure the vision and mission capture both the business strategy and role of location analytics in achieving the business strategy. They should be included as participants

187

in the vibrant back-and-forth of discussion, argument, and eventually consensus on mission and vision.

Implications for Practice:

 Engage senior management in strategy development. Location data and location analytics increases the agility of the business, challenges the status quo, connects dots across the enterprise, drives profitable growth, and increases competitiveness. Spatial transformation changes the business profoundly, to the extent that senior management becomes the driver (Frankiewicz and Chamorro-Premuzic 2020). There is an opportunity for varied leadership of this discipline. The spatial strategy should be closely aligned with the digital transformation office, the project management office, and the offices of the CSO, CIO, CMO, and COO. These are the offices where the funding streams will ultimately stem and where the cross-functional needs ultimately come together. These offices will fund the programs and product teams aligned to drive planning and operational work.

 Gear strategy to benchmarks of spatial maturity. Realizing the level of digital, analytical, and spatial maturity of a company will aid in establishing a frame of reference for ongoing development and benchmarking to lead with vision like that of BP, Walgreens, or Nespresso. With only 20% of organizations deeming their Digital Transformation successful (Harvard Business Analytic Services 2020), the considerations of readiness, resourcing, and talent are paramount.

 Use strategy to educate stakeholders to systems-thinking approach. Devising a spatial strategy provides an opportunity to view the company across the location value chain, that is, as a system. This can provide the framework to take into consideration new business opportunities and cross-company collaborations. It helps break down the organizational walls and widens the range of impact. It also provides greater awareness of potential risks across functions.

10) Provide Spatial Leadership for Sustainable Advantage The transition from a strategic plan to a successful and impactful strategy is where the

“rubber meets the road”. Senior leadership can provide the vision and critical support for integrating location analytics into business planning and operations, including as part of the firm's overall business and IT strategies. As noted above, this is best conducted in conjunction with the location analytics unit or team. In the case of UPS, although there was an extended time period for the testing and retesting of truck routing and delivery algorithms when company senior leadership saw the clear benefit of location analytics, this leant support, funding, and encouragement of middle management to extend location analytics across the entire company and soon geospatial was added to the corporate vision. In the case of Nespresso, there became a critical mass of location value across their sustainability value chain. The Nespresso geospatial platforms supported the stakeholders in various capacities resulting in award winning achievements. They used the tools in daily operations, spatial analysis, risk

188

analysis communication and in the development of new partnerships to grow the AAA sustainable development program for coffee.

Implications for Practice:

 Provide leadership in executing strategic plan. Ideally, this would be a collaborative leadership with representation from relevant business and technical teams. This would enable working relationships, improved connections to strategy, and potentially new revenue-generating opportunities that arise. In assessing priorities, the following considerations apply – (1) to determine what is the most important of the strategic initiatives based on the expected value of benefits, (2) what is the enterprise’s capacity to undertake an initiative, based on the resources present, and (3) can an initiative succeed based on benefits and risks (Peppard and Ward, 2016). With proper relationships with leaders, teams will be more effective and more engaged. With proper geospatial leadership, product teams can emerge from silos and become

 Contribute to sustainable advantage. Companies are one of the strongest influencers in local and well as global affairs. Companies can measure their commitment to communities and the environment, stewardship giving them a competitive advantage in the eyes of their consumers now and the eyes of all the stakeholders for the future. Location intelligence plays a key role in supporting and advancing such initiatives by providing location-specific data on communities, workforce, and producing data and information products for different stakeholders. With such visibility, companies can more confidently confirm their global businesses and suppliers’ movement and actions toward improving resource utilization, physical environment, treatment of labor, and stewardship to the community. Companies that invest in location intelligence are well- positioned to develop and successfully execute ESG plans and commitments.

Concluding Thoughts Location information has never been more important to businesses. Spatially mature

businesses deploy location intelligence to fully realize the power of location data to better understand and serve their customers and responsibly grow the business for the benefit of the enterprise and its employees, to manage risk for the sake of all stakeholders. The location value chain model provides aspiring organizations with a pathway for spatial transformation. The use of location analytics in such organizations is driven by well-defined business needs and usually produces high impact and value. However, without well-defined business needs and a spatial strategy, GIS and location analytics may end up as just another piece of an organization’s analytics/technology stack.

In closing, we reiterate the important role of management and leadership in contemporary spatial businesses. Several of the case studies have shown that a key facilitator of spatial maturity is sponsorship and support by the senior leadership of business projects involving location intelligence. From a management standpoint, this book emphasizes collaboration, risk tolerance, overcoming resistance, and a consistent, value-driven focus on

189

business and societal gains. With advances in technology, location intelligence and analytics, use cases will continue to evolve. However, we hope that the principles of leadership, management, spatial strategy, and decision-making highlighted in this book will not only endure but will also provide inspiration for businesses to compete and lead with location intelligence.

190

References

Aggarwal, S., and Srivastava, M. (2016). Nissan: Recovering Supply Chain Operations, Harvard Case Study, Harvard Business Publishing.

Anderson, J. C., Narus, J. A., & van Rossum, W. (2006). Customer value propositions in business markets. Harvard Business Review, 84(3), 90–149.

Angwin, J., and Valentino-Devries, J. 2011. Apple, Google collect user data. The Wall Street Journal, April 22. Retrieved at www.wsj.com.

Applebaum, W. 1966. Methods for Determining Store Trade Areas, Market Penetration, and Potential Sales. Journal of Marketing Research, 3(2), 127-141.

ArcWatch, GIS and Research: The Heartbeat of The Shopping Center Group, February, 2017. https://www.esri.com/about/newsroom/arcwatch/gis-and-research-the-heartbeat-of- the-shopping-center-group/.

AT&T. (2019). Road to climate resiliency: The AT&T story. Retrieved from https://about.att.com/content/dam/csr/PDFs/RoadToClimateResiliency.pdf.

Bateman, A., and Bonanni, L. (2019). What Supply Chain Transparency Really Means, Harvard Business Review. Retrieved from https://hbr.org/2019/08/what-supply-chain- transparency-really-means.

Boehe D.M., Pongeluppe L.S., Lazzarini S.G. (2014). Natura and the Development of a Sustainable Supply Chain in the Amazon Region. In: Liberman L., Garcilazo S., Stal E. (eds) Multinationals in Latin America. The AIB-LAT Book Series. Palgrave Macmillan, London. https://doi.org/10.1057/9781137024107_13

Boshell, P.M. 2019. The power of place: Geolocation tracking and privacy. Chicago: American Bar Association. Retrieved from https://www.americanbar.org/groups/business_law/publications/blt/2019/04/geolocation/

Boulmay, B. 2018. BP shares eight lessons on digital transformation. WhereNext, November 12. Retrieved from www.esri.com .

Boulmay, B. 2019. Enterprising geospatial: Acceleration, integration, innovation. Presentation. Australian Esri User Conference - OZRI 2019, November 20. Brisbane, Australia.

Boulmay, B. 2020. One Map for all. Position, February/March, 32-34. Retrieved from www.spatialsource.com.au.

Boyle, M., and Giammona, C. 2018. Walmart and Amazon clash with FreshDirect in New York food fight.

Business Roundtable, Business Roundtable Redefines the Purpose of a Corporation to Promote ‘An Economy That Serves All Americans’, August 19, 2019.

191

https://www.businessroundtable.org/business-roundtable-redefines-the-purpose-of-a- corporation-to-promote-an-economy-that-serves-all-americans.

CACI, 2020. Acorn user guide. Arlington, VA: CACI International. CACI. Retrieved from https://www.caci.co.uk/sites/default/files/resources/Acorn%20User%20Guide%202020.pdf

Cairncross, F. The Death of Distance: How the Communications Revolution Will Change Our Lives. Boston, Mass: Harvard Business School Press, 1997.

Carnow, A. 2019. Enterprise GIS: strategic planning for success. Esri Events, April 8. Redlands, CA: Esri Inc. Retrieved from https://proceedings.esri.com/library/userconf/proc18/tech-workshops/tw_2494-121.pdf

CFRA. 2021. United Parcel Service Inc. MarketScope Advisor. New York, NY: Center for Financial Research and Analysis.

Cheng, J. (2021). Analysis of Integrated Report Adoption for Natura Cosmeticos. Open Journal of Business and Management, 9(2), 489-495.

Chiappinelli, C. 2017. Buzzwords, hidden dimensions, and innovation: a UPS story. WhereNext, September 7, 2017. Retrieved from https://www.esri.com/about/newsroom/publications/wherenext/buzzwords-hidden- dimensions-and-innovation-a-ups-story/

Chiappinelli, C., 2018, The Business Value of Sustainability, WhereNext, December 11, 2018. Retrieved at: https://www.esri.com/about/newsroom/publications/wherenext/sustainability-and-location- intelligence/

Chiappinelli, C. (2020). Think Tank: How to Reopen the Workplace during COVID-19. WhereNext, Esri. Retrieved from https://www.esri.com/about/newsroom/publications/wherenext/reopening-the-workplace/.

Chostner, B. (2017). See & Spray: The Next Generation of Weed Control, Resource, 24(4), 4-5.

Church, R. L., & Murray, A. T. (2009). Business site selection, location analysis, and GIS. Hoboken, NJ: John Wiley & Sons.

CityEngine. (2016). Urban planning scenario, fictive redevelopment of a city block in Philadelphia. Retrieved from https://www.arcgis.com/apps/CEWebViewer/viewer.html?3dWebScene=86f88285788a4c53bd 3d5dde6b315dfe#.

City of Santa Rosa. (n.d.). Fire Aerial Photo Comparison App. Retrieved from https://santarosa.maps.arcgis.com/apps/PublicInformation/index.html?appid=478994a6534e4 86db5fb2e6313fe213c.

192

Claims Journal, After a Disaster, Imagery Gives Insurance Companies a Clear Picture. July 25, 2019. Available at: https://www.claimsjournal.com/news/national/2019/07/25/292162.htm

Cluster Mapping. (2021). U.S. Cluster Mapping Project. Retrieved from https://www.isc.hbs.edu/about-michael-porter/affiliated-organizations-institutions/Pages/us- cluster-mapping-project.aspx.

Collis, D., and Rukstad, M. (2008). Can you say what your strategy is?, Harvard Business Review, 86(4), 82-90.

Cooper, L. (1963). Location-allocation problems. Operations Research, 11(3), 331-343.

CoServ. 2020. CoServ corporate website. Corinth, TX: CoServ. Retrieved from www.coserv.com.

Council of Economic Advisors. (2020). The Impact of Opportunity Zones: An Initial Assessment. Report retrieved from https://trumpwhitehouse.archives.gov/wp- content/uploads/2020/08/The-Impact-of-Opportunity-Zones-An-Initial-Assessment.pdf.

COVID-19 Mobility Data Network. (2020). Movement Trends. Retrieved from https://visualization.covid19mobility.org/?date=2020-12-12&dates=2020-09-12_2020-12- 12&region=55.

Dalton, C.M., and Thatcher, J. 2015. Inflated granularity: spatial “big data” and geodemographics. Big Data and Society, 2(2): 1-15.

Dangermond, J. 2019. Comments in Carnow, A., Enterprise GIS: strategic planning for success. Esri Events, April 8. Video. Redlands, CA: Esri Inc. Retrieved https://www.esri.com/videos/watch?videoid=xaF6yBEvMj4&title=enterprise-gis-strategic- planning-for-success

Dangermond, J. 2021. GIS – creating a sustainable future. Keynote presentation. Esri User Conference 2021. July 12. Redlands, CA: Esri Inc. Retrieved from https://www.youtube.com/watch?v=1rtC_ZK74H0 .

Davenport, T. H. (2018). From analytics to artificial intelligence. Journal of Business Analytics, 1(2), 73-80.

Davenport, T., & Harris, J. (2017). Competing on analytics: Updated, with a new introduction: The new science of winning. Harvard Business Press.

De Pietro, Y. 2019, Nespresso: The Business Value of Sustainability through GIS

Geo Business Seminar, March 19, 2019, Amsterdam, NE

De Souza, R., and Iyer, L. (2019). Health Care and the Competitive Advantage of Racial Equity: How advancing racial equity can create business value. PolicyLink. Retrieved from

193

https://www.policylink.org/sites/default/files/Health%20Care%20and%20the%20Competitive% 20Advantage%20of%20Racial%20Equity.pdf.

Deere, 2020, Sustainability Report, Retrieved from: https://www.deere.com/en/our- company/sustainability/sustainability-report/

Deere 2021, Annual Report, 2021 retrieved from: https://s22.q4cdn.com/253594569/files/doc_financials/2021/ar/Deere-Co_Annual-Report- 2021.pdf

Deere, 2022a, Innovation & Technology. Retrieved from: https://www.deere.com/en/our-company/innovation/

Deere, 2022b Deer Operations Center, Retrieved from: https://www.deere.com/en/technology-products/precision-ag-technology/data- management/operations-center/

DiBiase, D., Corbin, T., Fox, T., Francisca, J., Green, Kl, Jackson, J., Jeffress, G., Jones, B., Mennis, J., Schuckman, K., Smith, C., and Van Sickle, J. 2010. The new geospatial technology competency model: Bringing workforce needs into focus. URISA Special GIS Education Issue, 22(2):55-72.

Direct Relief. (2020). About Direct Relief. Retrieved from https://www.directrelief.org/about/.

Direct Relief. (2020). Facebook AI Research (FAIR) Model of COVID-19 Spread. Retrieved fromhttps://directrelief.maps.arcgis.com/apps/dashboards/2b329f0ef76246568511292df89fc2 ac, on December 28, 2020.

Dong, E., Du, H., & Gardner, L. (2020). An interactive web-based dashboard to track COVID-19 in real time. The Lancet Infectious Diseases, 20(5), 533-534. doi: 10.1016/S1473- 3099(20)30120-1.

Dresner. (2019). Wisdom of Crowds Business Intelligence Market Study, Tenth Anniversary Edition, Dresner Advisory Services, LLC.

Duckham. 2013. Location privacy. In Information Resources Management Association, Geographic Information Systems: Concepts, Methods, Tools, and Applications, Volume 1, Chapter 3. Hershey, PA: IGI Global, pp. 24-29.

Economic Innovation Group. (2021). EIG Opportunity Zones Activity Map. Retrieved from https://eig.org/oz-activity-map.

Economic Innovation Group. (n.d.). EIG Opportunity Zones Activity Map. Retrieved from https://eig.org/oz-activity-map.

Edward Jones, History of Edward Jones, 2021 Available at: https://www.edwardjones.com/us-en/why-edward-jones/about-us/our-history.

194

Elliott, C. 2019. Finding the confidence to grow a business. WhereNext, March 5. Retrieved from https://www.esri.com/about/newsroom/publications/wherenext/confidence- in-new-markets/.

Elliott, C., and Nickola, C. (2021). Spotting New Business Opportunities in Consumer Data. WhereNext, Esri. Retrieved from https://www.esri.com/about/newsroom/publications/wherenext/spotting-new-business- opportunities/.

Endsley, M. R. (1988). Design and evaluation for situation awareness enhancement. In Proceedings of the Human Factors Society Annual Meeting (Vol. 32, No. 2, pp. 97-101). Los Angeles, CA: Sage Publications.

Esri, 2015. Savvy businesses share a secret. ArcWatch September. Redlands, CA: Esri. Retrieved from https://www.esri.com/about/newsroom/arcwatch/savvy-businesses-share-a- secret/

Esri. (2015). Supporting Sustainability through the Supply Chain. Retrieved from https://www.esri.com/library/casestudies/natura.pdf.

Esri. (2016). The new ArcGIS: it’s all about the portal. Spring. Redlands, CA: Esri. Retrieved from https://www.esri.com/about/newsroom/arcnews/the-new-arcgis-its-all-about- the-portal/

Esri. (2018). John Deere: Data Science and the Future of Agriculture [Audio podcast]. Retrieved from https://www.esri.com/about/newsroom/podcast/john-deere-data-science-and- the-future-of-agriculture/.

Esri. (2019). ArcGIS The Foundation of Digital Twins for Utilities. Video (min. 20). Retrieved from https://www.esri.com/videos/watch?videoid=jzn_u6uBRi4&title=arcgis-the- foundation-of-digital-twins-for-utilities.

Esri. (2020, May 31). Spatial Analytics and Data Diminish Tree-Related Outages 60 Percent. https://www.esri.com/en-us/industries/electric-gas-utilities/segments/electric/mid- south-synergy-removes-tree-hazards.

Esri. (2020). John Deere: How Data Science Drives Business Growth [Audio podcast]. Retrieved from https://www.esri.com/about/newsroom/podcast/john-deere-how-data- science-drives-business-growth/.

Esri. (2021a). Sustainable Development Report 2021. Retrieved from https://storymaps.arcgis.com/stories/5387a3d2041c4773b4b1d70d9f981b65.

Esri. (2021b). ArcGIS Hub. Redlands, CA: Esri. Retrieved from https://www.esri.com/en- us/arcgis/products/arcgis-hub/overview.

Esri. (2021c). Sephora Web Map. Retrieved from https://arcg.is/1riTv8.

195

Esri. (2021d). ArcGIS The Foundation of Digital Twins for Utilities. Redlands, CA: Esri. Retrieved from https://www.esri.com/videos/watch?videoid=jzn_u6uBRi4&title=arcgis-the- foundation-of-digital-twins-for-utilities.

Esri. (2022). Node Capacity Analysis Node Utilization Dashboard. Retrieved from https://insights.arcgis.com/index.html#/view/2e009ad4a92c4b4b810c80d17f589728.

Fadda, E., Gobbato, L., Perboli, G., Rosano, M., & Tadei, R. (2018). Waste collection in urban areas: A case study. INFORMS Journal on Applied Analytics, 48(4), 307-322.

Fekete, E. 2018. Foursquare in the city of fountains: using Kansas City as a case study for combining demographic and social media data. Chapter 7 in Thatcher, J., Eckert, J., and Shears, A. Thinking Big Data in Geography, Lincoln, NE, University of Nebraska Press, pp. 145.166.

FEMSA. 2018. Annual Report, FEMSA. Retrieved from https://femsa.gcs- web.com/financial-reports.

FinSMEs. 2018. RapidSOS raises $30 million in Series B Funding. London, England: FinSMs. Retrieved from https://www.finsmes.com/2018/11/rapidsos-raises-30m-in-series-b- funding.html

Frankiewicz, B., and Chamorro-Premuzic, T. 2020. Digital Transformation is about talent, not technology. May 6. Harvard Business Review.

Fu, P. (2020). Getting to Know Web GIS. 4th Edition. Redlands, CA: Esri Press.

Gans, J., Scott, E.L., and Stern, S. (2018). Strategy for start-ups. Harvard Business Review, May/June, pp. 44-51.

Gardner, H. (2006). Multiple Intelligences: New Horizons. Basic Books, New York: Perseus Group.

GeoComm. 2021. Putting the right location data, on the right map, for the right people, at the right time. Introducing Public Safety Location Intelligence. St. Cloud, MN: GeoComm. Retrieved from GeoComm.com.

GeoTechCenter.org. 2020. Geospatial Technology Industry Competency Model.GeoTech Center.org. Retrieved from https://www.careeronestop.org/competencymodel/competency- models/geospatial- technology.aspx.

GMC. 2019. GeoBuiz: Geospatial Industry Outlook and Readiness Index. Geospatial Media and Communications. Noida, India: Geospatial Media and Communications.

Government of Ireland. 2018. The changing patterns of unemployment and poverty in Ireland, 2011-2018. StoryMap. Dublin: Government of Ireland. Retrieved from https://irelandsdg.geohive.ie/apps/the-changing-patterns-of-unemployment-and-poverty-in- ireland-2011-2018/explore

196

GrandView Research. 2020. Location Based Advertising Market Size, Share & Trends Analysis Report By Type (Push, Pull), By Content (Text, Multimedia), By Application (Retail Outlets, Airports, Public Spaces), By Region, And Segment Forecasts, 2020 – 2027. Report GVR- 4-68038-869-4. July. 109 pgs. San Francisco, CA: Grandview Research.

Gray, M. 2017. Future of ORION, our powerful route optimization system. Address at UPS investor’s conference. Atlanta, GA: UPS.

Green, A. 2020. Complete Guide to Privacy Laws in the US. New York, NY: Varonis Systems Inc. Retrieved at https://www.varonis.com/blog/us-privacy-laws/ .

Grieves, M., and Vickers, J. (2017). In Kahlen, F.-J. et al. (Eds.) Transdisciplinary Perspectives on Complex Systems, New York, NY: Springer International Publishing, pp. 85-113.

Handly, B. 2019. Getting started with location-based marketing. Forbes Technology Council Post. Retrieved from https://www.forbes.com/sites/forbestechcouncil/2019/06/27/getting-started-with-location- based-marketing/?sh=36c2b78657ba

Harvard Business Review Analytic Services. 2020. Rethinking digital transformation: New data examines the culture and process change imperative in 2020. Pulse Survey. Cambridge, MA: Harvard Business Review.

Harvey, F. (2001). Constructing GIS: Actor networks of collaboration. URISA Journal, 13(1):29-37.

Heimer, M. (2017, September 7). How JPMorgan Chase Is Fueling Detroit’s Revival, Fortune. Retrieved from https://fortune.com/2017/09/07/jp-morgan-chase-detroit-revival/.

Hitt, A. 2017. Making room for innovation with GIS strategic planning. Spring. 4pp. Retrieved from https://www.esri.com/about/newsroom/arcnews/making-room-for-innovation- with- gis-strategic-planning/

Hitt, M., Ireland, R.D.., and Hoskisson, R.E. (2016). Strategic Management: Concepts and Cases. Mason, Ohio: South-Western Publishing Company.

Horner, P. (2016). ‘ORION’ delivers success for UPS. ORMS Today, INFORMS, Retrieved from https://pubsonline.informs.org/do/10.1287/orms.2016.03.10/full/.

Huff, D. L. (1964). Defining and estimating a trading area. Journal of Marketing, 28(3), 34-38.

HUD. (n.d.). Map of Opportunity Zones. Retrieved from https://opportunityzones.hud.gov/resources/map.

Hwangbo, H., Kim, J., Lee, Z., & Kim, S. (2017). Store layout optimization using indoor positioning system. International Journal of Distributed Sensor Networks, 13(2), 1-13.

197

Investopedia. 2019. The 4 Ps. Investopedia. Retrieved at www.investopedia.com.

Jacobides, J.G. 2019. In the ecosystem economy, what’s your strategy? Harvard Business Review, September-October, 129-137.

Jacobs, T. 2019. Digital transformation at BP is starting to add up to billions. Journal of Petroleum Technology, May 16. Retrieved from https://pubs.spe.org/en/jpt/jpt-article- detail/?art=5495

Johns Hopkins University. (2020). COVID-19 Dashboard. Retrieved from https://coronavirus.jhu.edu/map.html, on December 28, 2020.

Joseph, L. 2019. Interview, January 24. Redlands: University of Redlands.

Kane, G. C., Palmer, D., Nguyen-Phillips, A., Kiron, D., & Buckley, N. (2017). Achieving digital maturity. MIT Sloan Management Review, 59(1).

Kantor, M. 2018. Business advantage through location intelligence: The CEO’ guide. WhereNext. November 6. Retrieved from https://www.esri.com/about/newsroom/publications/wherenext/ceo-guide-to-location- intelligence/.

Kantor, M. 2018a. Burgers for a penny, and the power of location intelligence. WhereNext, December 10. Retrieved from https://www.esri.com/about/newsroom/publications/wherenext/burger-king-marketing- campaign/.

Kantor, M. 2018b. How social media could improve targeted marketing. WhereNext. February 6. Retrieved from https://www.esri.com/about/newsroom/publications/wherenext/social-media-and-targeted- advertising/.

Kantor, M. 2020. A lifelong learner brings a data science edge to Fruit of the Loom. WhereNext, January 21. Retrieved from https://www.esri.com/about/newsroom/publications/wherenext/a-lifelong-learner-brings-a- data-science-edge-to-fruit-of-the-loom/.

Kantor, M., & van der Schaaf, F. (2019). How Data-Driven John Deere Wins the Market, WhereNext, Esri.

Kantor, M., and Peters, J. (2020). A Blueprint for the New Era of Corporate Social Responsibility. WhereNext, Esri, Retrieved from https://www.esri.com/about/newsroom/publications/wherenext/csr-and-location- intelligence/.

Kaplan, A. 2018. Social media, in Warf, B. (Ed.) The SAGE Encyclopedia of the Internet. Los Angeles: SAGE Reference, pp. 809-813.

198

Kapur, M., Dawar, S., and Ahuja, V.R. 2014. Unlocking the wealth in rural markets. Harvard Business Review, June, 113-117.

Kazemi, Y. (2018). How GM Maps and Manages Supply Chain Risk, WhereNext, Esri, Retrieved from https://www.esri.com/about/newsroom/publications/wherenext/gm-maps- supply-chain-risk/.

Kazemi, Y., & Szmerekovsky, J. (2016). An optimisation model for downstream petroleum supply chain incorporating geographic information system (GIS). International Journal of Integrated Supply Management, 10(2), 151-172.

Kiron, D. 2017. Why your company needs more collaboration. MIT Sloan Management Review, 59(1):17-19.

Kouzes, J.M., and Posner, B.Z. 2017. The Leadership Challenge. 6th Ed. Hoboken, NJ: John Wiley and Sons.

KPMG, The Time Has Come: The KPMG Survey of Sustainability Reporting. December 2020. Retrieved from https://home.kpmg/sustainabilityreporting.

Kumar, R. 2019. Environmental scanning. SlideShare. Retrieved at https://www.slideshare.net/rajworship/environmental-scanning-8870811.

Lai, J., Cheng, T., and Lansley, G. 2017. Improved targeted outdoor advertising based on geotagged social media data. Annals of GIS, 23(4):237-250.

LAWA. (2019). 2019 LAWA Design & Construction Handbook. Retrieved from https://www.lawa.org/lawa-businesses/lawa-documents-and-guidelines/lawa-design-and- construction-handbook/archives/2019-design-and-construction-handbook.

Le, M., Ibrahim, M., Sagun, L., Lacroix, T. and Nickel, M. (2020). Neural Relational Autoregression for High-Resolution COVID-19 Forecasting. Retrieved from https://ai.facebook.com/research/publications/neural-relational-autoregression-for-high- resolution-covid-19-forecasting.

Leventhal, Barry. 2016. Geodemographics for Marketers: Using Location Analysis for Research and Marketing. London: Kogan Press.

Levis, J. 2017. Interview of Jack Levis by Marianna Kantor. Podcast. Redlands, CA: Esri. Retrieved from https://www.esri.com/about/newsroom/podcast/how-ups-strengthens- customer-connection-with-spatial-analytics/

Lewin, M. 2021. Strategy for GIS: the three essential steps to creating enterprise value. Toronto, Canada: Esri Canada. Retrieved from https://resources.esri.ca/news-and- updates/strategy-for-gis-the-three-essential-steps-to-creating-enterprise-value .

199

Lim, K.Y.H., Zheng, P., and Chen, C.-H. (2019). A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives. Journal of Intelligent Manufacturing, 31:1313-1337.

Litt, R. S., & Brill, S. M. 2018. Location information is protected by the 4th Amendment, SCOTUS rules. Socially Aware. San Francisco, CA: Morrison Foerster.

Lodge, A. 2016. What is a GIS strategic plan and template. San Francisco, CA: Farallon Geographics. August 30. Retrieved from https://fargeo.com/blog/what-is-gis-strategic-plan/

Longley, P. A., Goodchild, M. F., Maguire, D. J., & Rhind, D. W. (2015). Geographic information science and systems. New York, NY: John Wiley & Sons.

Lowther, A.M., and Tarolli, D. (2020). Cushman & Wakefield Clients Get 3D Tours and Market Insight, WhereNext, Esri.

Macys. (2020). Annual Report, Macys, available at https://www.macysinc.com/investors/sec-filings/annual-reports.

Marble, D.F. 2006. Who are we? Defining the geospatial workforce. Geospatial Solutions. May. Retrieved from www.geospatial-online.com .

Marks and Spencer. (2022). M&S Interactive Map. Retrieved from https://interactivemap.marksandspencer.com/?sectionPID=56c359428b0c1e3d3ccdf022 on March 7.

Marr, B. 2017. What is digital twin technology – An why is it so important? Forbes, March 6.

Marshall, P. 2016. LA GeoHub: a model for ‘datafying’ communities. Government Computer News 35(2):47.

McGrath, R.G., and McManus, R. 2020. Discovery-driven digital transformation. Harvard Business Review, May/June.

McKinsey & Company, Adapting to the next normal in retail: The customer experience imperative, May 14, 2020.

MIT (2021). Amazon last mile routing: Research challenge. Retrieved from https://routingchallenge.mit.edu/.

MMA Global. 2019. Heineken: Heineken@WhereNext. New York, NY: Mobile Marketing Association. Retrieved from www.mmaglobal.com .

Morningstar Inc. 2021. Walgreens Boots Alliance Inc. Chicago, IL: Morningstar Inc.

200

Munnich, L., Fried, T., Cho, J., and Horan. T. (2021). Assessment of Spatial Location and Air Transport Patterns of Minnesota’s Medical Device Industry Cluster. Journal of Strategic Innovation and Sustainability, 16(2), 106-118.

National Research Council. Learning to Think Spatially. Washington, DC: The National Academies Press, 2006. https://doi.org/10.17226/11019.

Natura. (2020). Annual Report - 2020. Retrieved from https://www.naturaeco.com/en/.

Nelson, M. 2021. Outsmarting fraudsters with advanced analytics. San Francisco: Visa. Retrieved from https://usa.visa.com/visa-everywhere/security/outsmarting-fraudsters-with- advanced-analytics.html .

Nespresso, Our Business Principles, 2021 Retrieved at: https://nestle- nespresso.com/our_business_principles. Accessed on August 8, 2021.

Nespresso. (2021). The positive cup. Retrieved from https://www.sustainability.nespresso.com/.

Nespresso, 2022, Facts and Figures, retrieved at: https://nestle- nespresso.com/our_company/facts_figures

O’Sullivan, D., & Unwin, D. (2014). Geographic information analysis. John Wiley & Sons.

Pavate, V. 2021. Why warehouses are becoming spatially intelligent. Material Handling and Logistics, January 6. Retrieved from https://www.mhlnews.com/technology- automation/article/21151717/why-warehouses-are-becoming-spatially-intelligent .

Peppard, J., and Ward. J. 2016. The Strategic Management of Information Systems: Building a Digital Strategy. 4th Edition. Chichester, United Kingdom: John Wiley and Sons.

Perez, J. 2017. Address at UPS investor’s conference. Atlanta, GA: UPS.

PESA. 2020. PESA delegates get insights into BP’s digital data transformation. PESA News, First Quarter, 31-33. Perth, Australia: Petroleum Exploration Society of Australia.

Peters, A. (2017). How John Deere’s New AI Lab Is Designing Farm Equipment For A More Sustainable Future, Fast Company, Retrieved from https://www.fastcompany.com/40464024/how-john-deeres-new-ai-lab-is-designing-farm- equipment-for-more-sustainable-future, May 15, 2020.

Piccoli, G. and Pigni, F. 2022. Information Systems for Managers in the Digital Age. 5th edition. Burlington, VT: Prospect Press.

Pick, J.B. 2008. Geo-business: GIS in the Digital Organizations. New York, NY: John Wiley and Sons.

201

Porter, M. and Kramer, M. (2016). “The Ecosystem of Shared Value,” Harvard Business Review, December. Retrieved from https://hbr.org/2016/10/the-ecosystem-of-shared-value.

Porter, M. and Kramer, M. (2011). Creating Shared Value, Harvard Business Review, 89(1,2), 2-17.

Porter, M. E. (1998). Clusters and the new economics of competition. Harvard Business Review, 76(6), 77-90.

Porter, M. E. (1998). The Competitive Advantage: Creating and Sustaining Superior Performance. New York NY: Free Press, 1998 (Second Edition).

Porter, M. E. (1998). The Competitive Advantage of Nations. San Francisco, CA: The Free Press.

Porter, M.E. (2021). Cluster Mapping. Cambridge MA: Harvard Business School.

Porter, M.F. (2008). The five competitive forces that shape strategy. Harvard Business Review, 86(1):78-93.

PriceWaterHouse. (2020). Welcome to the crisis era. Are you ready? Retrieved from https://www.pwc.com/gx/en/ceo-agenda/pulse/crisis.html on May 1, 2020.

Raad, M. (2017). A New Business Intelligence Emerges: Geo.AI, WhereNext, Esri, Retrieved from https://www.esri.com/about/newsroom/publications/wherenext/new- business-intelligence-emerges-geo-ai/.

Radke, S., Johnson, R. and Baranyi, J. (2013). Enabling Comprehensive Situational Awareness, Esri Press: Redlands CA.

Rainforest Alliance. (2021). The birth of the AAA sustainable quality program. Retrieved from https://www.sustainability.nespresso.com/rainforest-alliance-insights.

RapidSOS. 2020. Esri, GeoComm, and RapidSOS partner to improve first responder situational awareness. New York, NY: RapidSOS. Retrieved from rapidsos.com.

RapidSOS. 2021. The ultimate guide to integrating your business with public safety. New York, NY: RapidSOS. Retrieved from https://rapidsos.com/ultimate-guide-integrating-business- with-911-ebook/

Reid, A. 2021. SecureWatch and artificial intelligence fuel safer, more effective monitoring of real estate development during the Covid-19 pandemic. Blog April 7. Westminster, CO: MAXAR Corporation. Retrieved from https://blog.maxar.com/earth- intelligence/2021/securewatch-and-artificial-intelligence-fuel-safer-more-effective-monitoring- of-real-estate-development-during-the-covid-19-pandemic

Ricker, B. 2018. Location-based services. In Warf, B. (Ed.), The SAGE Encyclopedia of the Internet, Thousand Oaks, CA: SAGE Publications Inc. pp. 613-618.

202

San Diego Regional Economic Development Corporation (n.d.). Building San Diego's Talent Pipeline. Retrieved from https://sd-regional edc.maps.arcgis.com/apps/Cascade/index.html?appid=97fc15fd9df04152aa41d009a87ed8eb.

Sandino, T., Cavazos, G.P., and Lobb. A. (2017). Oxxo’s turf ware against Extra (B), Harvard Business School Case 117-022, Harvard Business Publishing: Boston, MA.

Sankary, G. (2020). Inside Bass Pro Shops’ Path to Business Continuity during COVID-19, WhereNext, Esri, Retrieved from https://www.esri.com/about/newsroom/publications/wherenext/inside-bass-pro-shops-path- to-business-continuity-during-covid-19/.

Sarlitto, D.J. (2020. Evolution of earth observation. In Shekhar, S., Hui, X., and Xun, Z. 2020. Encyclopedia of GIS, Springer International Publishing.

Schroeder, A. (2017). Humanitarian Aid and Spatial Technologies in Crisis. Presentation at the University of Redlands, Redlands CA. Retrieved from https://www.redlands.edu/csb- speakers/.

Semprebon, A. 2021, Our Favorites Stories of 2021, December 19.

Shah, Agam. 2021. Walgreens brings 122 apps to the cloud. CIO Journal. Wall Street Journal. August 11, 2021.

Sharda, R., Delen, D., and Turban, E. 2018. Business Intelligence, Analytics, and Data Science. New York, NY: Pearson.

Shipt. 2020. About us. Retrieved from shipt.com.

Simchi-Levi, D., Kaminsky, P., & Simchi-Levi, E. (2004). Managing the Supply Chain: Definitive Guide. McGraw-Hill.

Singh, C. 2017. Nike Segment Analysis. Case Study, April 25, Essays 24. Retrieved from https://www.essays24.com/essay/Nike-Segment Analysis/75704.html#:~:text=Nike%20is%20unique%20in%20the,%E2%80%9D(Nike.com).&text =Thus%20segmenting%20to%20more%20precisely%20define%20various%20market%20segme nts%20is%20necessary.

Smith, N. (2020). AI Predicts Highest Risk U.S. Counties During Covid-19 Surge. Retrieved from https://www.directrelief.org/2020/11/ai-predicts-highest-risk-u-s-counties-during-covid- 19-surge/.

Solem, M. 2017. Geography education, workforce trends, twenty-first-century skills, and geographical capabilities. In Richardson, D. et al. (Eds), The International Encyclopedia of Geography, Chichester, England: John Wiley and Sons Ltd., pp. 2739-2747.

Somers, R. 1998. Developing GIS management strategies for an organization. Journal of Housing Research, 9(1):137-178.

203

Spatial Business Initiative. (2018). Charting Spatial Business Transformation. University of Redlands, Redlands, CA: Author.

Sreedhar, B. and Bhatnagar. S. (2019). Location analytics market: Global forecast to 2024. MarketsandMarkets.

Stanley, J. (2017). Space, Time and Groceries, Retrieved from https://tech.instacart.com/space-time-and-groceries-a315925acf3a.

Statista. 2021. Statistics on big data. Retrieved from www.statista.com

Stenmark, J. (2016). Indoor mobile mapping takes off at LAX, Retrieved from https://www.geospatialworld.net/article/indoor-mobile-mapping-lax/.

Swiss Re. (2021). The economics of climate change: no action not an option. Available at https://www.swissre.com/dam/jcr:e73ee7c3-7f83-4c17-a2b8-8ef23a8d3312/swiss-re-institute- expertise-publication-economics-of-climate-change.pdf.

Tabrizi, B., Lam, E., Girard, K., and Irvin, V. (2019). Digital transformation is not about technology. Harvard Business Review, March 13.

Tang, W. and Selwood, J. 2005. Spatial portals: Gateways to Geographic Information. Redlands, CA: Esri Press.

Tao, F., Zhang, H., Liu, A., & Nee, A. Y. (2018). Digital twin in industry: State-of-the- art. IEEE Transactions on Industrial Informatics, 15(4), 2405-2415.

Tate, N.J., and Jarvis, C.H. 2017. Changing the face of GIS education with communities of practice. Journal of Geography in Higher Education, 14(3), 327-340.

The Shopping Center Group, About Us. Available at: https://www.theshoppingcentergroup.com/about/, Retrieved on August 8, 2021

Thompson, S.A., and Warzel, C.W. (2019). Twelve million phones, one dataset, zero privacy. NY Times. Retrieved from https://www.nytimes.com/interactive/2019/12/19/opinion/location-tracking-cell-phone.html.

Tobler, W. R. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46(sup1), 234-240.

Tomlinson, R. 2013. Thinking About GIS: Geographic Information System Planning for Managers. 5th Ed. Redlands, CA: Esri Press.

Travelers Insurance, 2021. Geospatial Intelligence Informs Safer, Smarter Business Decisions. Available at: https://www.travelers.com/resources/business-topics/internet-of- things/geospatial-intelligence.

204

Travelers. (2019). The Travelers Companies, Inc, 2019 Annual Report. Retrieved from http://investor.travelers.com/Annual-Reports.

Turner, A. (2018). The business case for racial equity: A strategy from growth. W.K. Kellogg Foundation Report. Retrieved from https://wkkf.issuelab.org/resource/business-case- for-racial-equity.html.

United Health Foundation. (2021). America’s Health Rankings: Health Disparities Report 2021. Retrieved from https://assets.americashealthrankings.org/app/uploads/2021_ahr_health-disparities- comprehensive-report_final.pdf.

United Nations. (2022). Sustainable Development Goals Communications Materials. Retrieved from https://www.un.org/sustainabledevelopment/news/communications-material/.

Valentino-DeVries, J. 2018. Five ways Facebook shared your data. New York Times, December 19. Retrieved from www.nytimes.com.

Valentino-DeVries, J., Singer, N., Keller, M.H., and Krolik, A. 2018. Your apps know where you were last night, and they’re not keeping it secret. New York Times, December 10.

Van der Heijden, K., R. Bradfield, G. Burt, G. Cairns, and G. Wright. 2002. The sixth sense: accelerating organizational learning with scenarios. John Wiley & Sons Inc.

Venables, M. 2019. Change of culture reaps rewards for BP’s digital transformation. Forbes, January 31. Retrieved from https://www.forbes.com/sites/markvenables/2019/01/31/change-of-culture-reaps-rewards- for-bps-digital-transformation/?sh=27fa36306199.

VF Corp. (2018). We are made for change: Sustainability and Responsibility Report 2018. Available at https://d1io3yog0oux5.cloudfront.net/vfc/files/documents/Sustainability/Resources/VF+2018+ Made+for+Change+report.pdf.

VF Corp. (2020). Traceability Maps. Retrieved from https://www.vfc.com/responsibility/product/traceability-maps.

Walgreens. 2018. Interview of Walgreens. October 5, 2018.

Walgreens. 2021. Interview of Walgreens. July 23,2021.

Wells, J. 2018. FreshDirect takes aim at Amazon and Walmart with new fulfillment center. Dive Brief. July 23. Washington DC: Retaildive.com. Retrieved from https://www.retaildive.com/news/freshdirect-takes-aim-at-amazon-and-walmart-with-new- fulfillment-center/528069/

205

Westberg, T. 2015. UPS presentation. 2015 Broadband Tech Summit. Retrieved from https://www.slideshare.net/UtahBroadband/2015-broadband-tech-summit-todd-westberg- ups-presentation .

Woodward, J.R. 2020. Enterprise GIS: Concepts and Applications. Boca Raton, FL: CRC Press.

Yaffe-Bellany, D., & Corkery, M. (2020, May 5). A Wendy’s With No Burgers as Meat Production Is Hit, NY Times. https://www.nytimes.com/2020/05/05/business/coronavirus- meat-shortages.html?auth=login-email&login=email

Yunes, T. H., Napolitano, D., Scheller-Wolf, A., & Tayur, S. (2007). Building efficient product portfolios at John Deere and Company. Operations Research, 55(4), 615-629.

Zlatanova, S., and Isikdag, 2017. 3D indoor models and their applications, In Shekhar, S., et al. (Eds.), Encyclopedia of GIS, 2nd edition, New York: Springer.

206

  • 0 Latest Cover PDF Format.pdf
  • 1 Copyright.pdf
  • 2 Spatial Business Book TOC_3_15_22.pdf
    • Table of Contents
  • 3 Intro singlespace 4_19_22.pdf
    • Technology and Location
    • Spatial Business Organization
      • Spatial Business Fundamentals
      • Achieving Business and Societal Value
      • Toward Spatial Excellence
  • 4 Acknowledgements single-spaced_4_19_2022.pdf
  • 5 Spatial Business Book PART I_3_15_22.pdf
    • SPATIAL BUSINESS: COMPETING AND LEADING WITH LOCATION ANALYTICS
  • 6 CHAPTER 1 singlespace 4_19_22.pdf
    • Introduction
      • Creating Value
      • Sustainable Value
    • Spatial Decision Cycle
      • Case Example: The Shopping Center Group
    • Location Value Chain
      • Pandemic Influences on Location Value Chain
    • Drivers of Spatial Maturity
    • Global Location Analytics Outlook
  • 7 CHAPTER 2 singlespaced 4_19_22.pdf
    • Introduction
      • Spatial Visualization and Hotspots
      • Indoor Analytics
      • StoryMaps
      • GEO-AI
      • Digital Twins
      • Business Data - Enterprise
      • Business Data – Commercial
      • Community and Environmental Data – Public/Open
      • Imagery and Remote Sensing Data
      • Cloud
      • Enterprise
      • Desktop
      • Portals
      • Hubs
    • Closing Case Study: Walgreens
  • 8 CHAPTER 3 singlespaced 4_19_22.pdf
    • Introduction
    • Principles of Business Location Analytics
      • Location Proximity and Relatedness
      • Location Differences, Linkages, and Contexts
    • Hierarchy of Location Analytics
      • Descriptive Location Analytics
        • Illustration of Descriptive Location Analytics
      • Predictive Location Analytics
        • Illustration of Predictive Location Analytics
      • Prescriptive Location Analytics
        • Illustration of Prescriptive Location Analytics
    • Location Analytics Across the Value Chain
      • Research and Development
      • Marketing and Sales
      • Real Estate and Site Selection
      • Operations
      • Corporate Social Responsibility
    • Closing Case Study: John Deere
      • Location Intelligence for R&D in Precision Farming
      • Location intelligence for business development and sales
      • Location intelligence for real estate strategy and store operations
      • Environmental and Societal Elements
  • 9 Spatial Business Book PART II_3_15_22.pdf
    • SPATIAL BUSINESS: COMPETING AND LEADING WITH LOCATION ANALYTICS
  • 10 CHAPTER 4 singlespaced 4_19_22.pdf
    • Introduction
    • Understanding Business Markets
    • Environmental Scanning
    • Trade Area Analysis
    • Growing Customers
      • Market Segmentation: Geodemographics
    • Location Analytics Across the 7-Ps
    • FreshDirect in New York City
    • Location-Based Marketing
    • Benefit of location-based marketing
    • Location Based Social Media Marketing
      • Heineken’s use of social media for a marketing campaign
    • Privacy Issues Related to Markets and Customers
    • Closing Case Study: Oxxo Mexico
      • Understanding markets and customers
      • Location differences drive a differentiated retail approach
      • Location intelligence drives business expansion
      • Location intelligence at Oxxo: The future
  • 11 CHAPTER 5 singlespaced 4_19_22.pdf
    • Introduction
    • Real-time Situational Awareness
    • Real-time situational awareness at an Electric Utility
    • Monitoring operations KPIs using Dashboards
    • Distribution System Design
      • Facilities Location
      • Routing Optimization
    • Facilities Layout
      • Indoor GIS for Facilities Layout Design and Management
        • Indoor GIS at Los Angeles International Airport
    • Supply Chain Management and Logistics
      • Spatial Technologies for Supply Chain Management
    • Concluding Case Study: Cisco
  • 12 Chapter 6 singlespaced 4_19_22.pdf
    • Introduction
    • Location Analytics for Risk Management
      • Location Analytics for Supply Chain Risk Management at General Motors (GM)
    • Real Estate Risk Management
      • The Rise of 3D
    • Business risk mitigation and building business resilience
      • Business Continuity: The Case for Dashboards
      • Business Recovery: Real Time Monitoring
      • Risk Resilience: Predictive modeling
      • Business Risk Management: Predictive Big Data Modeling
    • Concluding Case Study: Geospatial Innovation at Travelers Insurance
      • Pay what you owe
      • Improve customer experience using innovative predictive models
      • Increase efficiency and productivity
  • 13 Chapter 7 singlespaced 4_19_22.pdf
    • Introduction
    • Environment, Society and Governance
      • Business Implementation of ESG
    • Sustainable Supply Chains
    • Preserving Biodiversity
    • Climate Resiliency
      • COVID-19 Pandemic Dashboard
      • Predictive Modeling of Pandemic
    • Diversity, Equity, and Inclusion (DEI)
    • Community Development
    • Closing Case Study: Nespresso
  • 14 Spatial Business Book PART III_3_15_22.pdf
    • SPATIAL BUSINESS: COMPETING AND LEADING WITH LOCATION ANALYTICS
  • 15 Chapter 8 singlespaced 4_19_22.pdf
    • Introduction
    • Spatial Maturity Stages
    • Management Pathways
    • Case Example: CoServ
    • Applying management principles to spatial transformation
    • Leadership and championing
    • Privacy and Ethics in Spatial Business
    • Developing Spatial Business Workforce
    • Communities of Practice
    • Concluding Case Study: BP
  • 16 CHAPTER 9 singlespaced 4_19_22.pdf
    • Introduction
    • Geospatial Strategic Planning
      • Steps to Development of a Geospatial Strategy
    • Case Example: United Parcel Service (UPS)
    • Location Analytics Strategy in Small Business
    • Location Analytics Strategy in a Small Business: RapidSOS
    • Geospatial Competitiveness Value-Added
    • Competitiveness
    • Collaboration
    • Sustainable Advantage
    • Concluding Case Study: Kentucky Fried Chicken
  • 17 Chapter 10 singlespaced 4_19_22.pdf
    • Introduction
    • Fundamentals of Spatial Business: Themes and Implications
      • 1) Identify and Enhance the Location Value Chain
      • 2) Enable Spatial Maturity Pathway
      • 3) Match Location Analytics Approach to Business Goals and Needs
      • 4) Build a Data-Driven Spatial Business Architecture
    • Achieving Business and Society Value: Themes and Implications
      • 5) Use Market and Consumer Intelligence to Drive Business Growth
      • 6) Measure, Manage, and Monitor the Operation
      • 7) Mitigate the Risk and Drive Toward Resiliency
      • 8) Enhance Corporate Social Responsibility
    • Toward Spatial Excellence: Themes and Implications
      • 9) Develop a Spatial Strategy and Capacity
      • 10) Provide Spatial Leadership for Sustainable Advantage
    • Concluding Thoughts
  • 18 references_singlespaced 4_19_22.pdf