Geographic Crime Analysis
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Optimized Community Policing through Locational Analytics By Roger Chin and Jake Campbell
system can help law enforcement analytics alleviate contention between officers and the communities they serve, deter crime and improve accountability and transparency.
Combined with quantitative and qualitative data, locational analytics allows modern policing to help the public report problems and officers to respond. Law enforcement agencies that actively collect crime data must take similar approaches toward community satisfaction surveys. GIS can map the locational origins and distribution of quantitative crime data and incorporate qualitative citizen feedback on police services. The resulting assessment can inform decisionmaking to determine high crime and low favorability neighborhoods where public satisfaction with police services needs improvement. The assessment can influence resource allocation, allow officers to be a proactive presence in high crime areas and help to deter crime.
The continuous process improvement model can map community policing efforts, and analysis of new data can create an ongoing cycle of crime reduction and increased cordial relations with the public by redirecting efforts and retargeting communities. Locational analytics can determine the optimal locations for outreach events—churches, schools, community centers or other sites—that coincide with high crime areas where officers already seek a proactive presence. Ideally, their presence may help deter crime by demonstrating that a particular area is not without enforcement, while simultaneously improving relations, engaging the public and demonstrating that specific neighborhoods are not neglected.
In an increasingly data-centric society, analytics integration has become common in public organizations. As agencies learn to integrate data into their operations, they must have a clear strategy to maximize their efforts; data collection alone does not adhere to analytical best practices. Data must be acquired and integrated into a system of continuous process improvement in which the data analysis shapes decisionmaking and provides a basis for reevaluating existing policy. Yet organizations often face pitfalls when collecting data without analysis, or when the analysis does not influence and guide organizational strategies and policies. A coherent analytical operation can maximize effectiveness and improve the way organizations achieve target outcomes. Geographic information systems (GIS) and the value associated with their integration provide a path forward.
General Shifts in Policing The concept of “traditional policing” has evolved in recent decades. Previously, officers conducted foot patrols and fielded service calls from street- side call boxes. Now, they use cars equipped with 300 horsepower and advanced computers. They are perceived as much more than enforcers of the law, but as social workers, therapists, mediators, community leaders, role models and public relations specialists. These increasing obligations amid declining budgets constantly challenge officials to find innovative solutions. Indeed, some crime prevention programs, though designed to enhance safety, have drawn adverse public reaction. One way to deter crimes is through proactive tactics in high risk areas, rather than training officers to merely react to illegal activities.
Locational Analytics for Modern Policing The use of spatial and temporal data in crime analysis has set a new standard for law enforcement agencies. GIS hardware and software support the collection and analysis of quantitative, qualitative and spatial data for location integrated analytics. When used in a continuous process improvement system, GIS can help agencies reach targeted outcomes and maximize effectiveness. To better understand this comprehensive, iterative approach, we apply the model to community policing. This continued on page 28
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Increase of Support Service Productivity After QA Implementation
QA Program Implemented 2013
The QA project simultaneously brought more hard- to-reach residents into services and increased the frequency that all residents used the program.
The Bottom-Up Experience Three years later, SAHA took a bottom-up approach to QA for family services. SAHA’s data management culture and skill had transformed since the 2013 implementation and management decided to broaden involvement to generate buy-in and improve the program. A working group of 10 direct service staff agreed on family program goals and used them to build a QA program design. Staff tested the guidelines quarterly and reconvened over their performance reports for feedback and revision. No formal performance evaluation ensued; periodic report cards were for staff information only.
The bottom-up approach was well suited to family services as staff need more discretion and flexibility for providing families a wider range of programs and referrals compared to senior communities. Some heads of households need income and employment support; others only want referrals to youth programs, savings tools, recreation, health services or scholarship opportunities. Many families prefer targeted, less frequent contact because they work multiple jobs and provide care to multiple generations.
The bottom-up program is too recent to evaluate turnover impacts and success, but SAHA already detects positive staff morale. Without a high-pressure data accountability regime, service coordinators report new understanding and enjoyment of data and quicker resolution of errors and inconsistencies through peer support. Staff believe more strongly in their purpose and outreach strategies, increasing their efficacy in gaining client participation. Staff now drive innovation around youth programming, expanding their activity offerings and recruiting greater volunteer support at their buildings.
Employee stewardship happens when employees take ownership of their work product and make
excellence their goal, rather than perform to top- down management objectives. SAHA’s bottom- up approach recruited direct service staff in organizational transformation, while transforming the staff into stewards of data-driven change.
Chris Hess, an MPA candidate at Presidio Graduate School in San Francisco, is director of resident services at Satellite Affordable Housing Associates in Berkeley. He can be reached at chris.hess@presidio.edu.
OPTIMIZED COMMUNITY POLICING continued from page 26
The community policing example demonstrates an ideal application of locational analytics and its functions within a system of continuous process improvement. Rather than view their adoption as a hindrance or costly endeavor, administrators should consider the long-term benefits of improved strategic planning and resource allocation. In addition to improving accountability and transparency, locational analytics can support optimized strategies to increase officer productivity, raise morale, enhance safety, refine patrol patterns and improve community satisfaction.
Challenges to Overcome Like all professions, modern policing must adapt to changing times and learn from experiences. Locational analytics must be executed so data analysis and policy review function as part of an ongoing cycle of strategic planning and assessment. The continuous process improvement procedure is not without challenges and is not a panacea for every problem that every law enforcement and public sector agency faces. GIS tools only work if the organization adopting them acknowledges the benefits to be gained. If a department focuses too much on rewarding officers for the number of arrests made, officers may be less inclined to use GIS insights to support community outreach efforts. Additionally, long-term implementation of GIS may help to curb costs, but agencies may face funding constraints, making it difficult to implement technology and hire analysts who can use and interpret the outputs. Yet in overcoming these challenges, locational analytics can help bring community policing and organizational operations into the 21st century.
Roger Chin is a Ph.D. candidate in political science and information systems at Claremont Graduate University and a faculty associate at Arizona State University. He can be reached at roger.chin@cgu.edu.
Jake Campbell is a Ph.D. candidate in political science at Claremont Graduate University and an adjunct professor at California State University, Long Beach. He can be reached at jake.campbell@csulb.edu.
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