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Regional Studies

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Knowledge Neighbourhoods: Urban Form and Evolutionary Economic Geography

Gregory M. Spencer

To cite this article: Gregory M. Spencer (2015) Knowledge Neighbourhoods: Urban Form and Evolutionary Economic Geography, Regional Studies, 49:5, 883-898, DOI: 10.1080/00343404.2015.1019846

To link to this article: http://dx.doi.org/10.1080/00343404.2015.1019846

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Knowledge Neighbourhoods: Urban Form and Evolutionary Economic Geography

GREGORY M. SPENCER Munk School of Global Affairs, University of Toronto, 1 Devonshire Place, Toronto, ON, Canada M5S 3K7.

Email: [email protected]

(Received October 2012; in revised form January 2015)

SPENCER G. M. Knowledge neighbourhoods: urban form and evolutionary economic geography, Regional Studies. This paper examines connections between the urban form of neighbourhoods in relation to the evolutionary economic geography of knowledge-intensive industries. The data presented show that firms in ‘creative’ industries tend to be located in dense, mixed-use neighbourhoods near the city core, while ‘science-based’ industries tend to be concentrated in low-density, single-use neighbourhoods in the suburbs. It is argued that these spatial patterns are related to the fact that inter-firm networks are more important in the ‘creative’ industries, while ‘science-based’ industries rely more heavily on intra-firm interactions and learning.

Urban form Evolution Diversity Creativity Science Neighbourhood

SPENCER G. M. 知识的邻里：城市形态与演化经济地理学，区域研究。本文检视邻里的城市形态与知识密集产业的演 化经济地理之间的连结。本文呈现的数据显示，“创意”产业中的企业，倾向座落于临近市中心的高密度、混合使用的 邻里，而“以科技为基础”的产业，则倾向聚集于郊区中低密度、单一使用的邻里。本文主张，这些空间形式，与企业 间的网络对“创意”产业更为重要、“以科技为基础”的产业则更大量倚赖企业内部的互动与学习的事实相关。

城市形态 演化 多样性 创造力 科学 邻里

SPENCER G. M. Les quartiers de la connaissance: la forme urbaine et la géographie économique évolutionniste, Regional Studies. Ce présent article cherche à examiner les liens entre la forme urbaine des quartiers par rapport à la géographie économique évolutionniste des industries à haute intensité de connaissance. Les données présentées montrent que les entreprises des industries ‘créatives’ ont tendance à s’implanter dans les quartiers très peuplés à usages mixtes situés près du noyau urbain, alors que les industries ‘à vocation scientifique’ tendent à se concentrer dans les quartiers de banlieue à population faible et à usage unique. On affirme que ces configurations spatiales se rapportent au fait que les réseaux interentreprises s’avèrent plus importants dans les industries ‘créatives’, tandis que les industries ‘à vocation scientifique’ sont fortement tributaries des interactions intraentreprises et de l’apprentissage.

Forme urbaine Évolution Diversité Créativité Sciences Quartier

SPENCER G. M. Nachbarschaften des Wissens: urbane Form und evolutionäre Wirtschaftsgeografie, Regional Studies. In diesem Beitrag werden die Verbindungen zwischen der urbanen Form von Nachbarschaften in Beziehung auf die evolutionäre Wirtschaftsgeografie von wissensintensiven Branchen untersucht. Aus den gezeigten Daten geht hervor, dass sich Firmen in ’kreativen’ Branchen tendenziell eher in dicht besiedelten Nachbarschaften mit gemischter Nutzung in der Nähe des Stadtkerns ansiedeln, während sich ’wissenschaftsbasierte’ Branchen eher in dünn besiedelten Nachbarschaften mit einzelner Nutzung in den Vorstädten konzentrieren. Es wird argumentiert, dass diese räumlichen Muster mit der Tatsache zusammenhängen, dass Netzwerke zwischen Firmen in den ’kreativen’ Branchen wichtiger sind, während ’wissenschaftsbasierte’ Branchen stärker auf Wechselwirkungen und Lernprozesse innerhalb der Firma angewiesen sind.

Urbane Form Evolution Vielfalt Kreativität Wissenschaft Nachbarschaft

SPENCER G. M. Barrios de conocimiento: forma urbana y geografía económica evolutiva, Regional Studies. En este artículo se analizan las conexiones entre la forma urbana de los barrios con relación a la geografía económica evolutiva de las industrias de conocimiento intensivo. Los datos presentados indican que las empresas en industrias ‘creativas’ tienden a ubicase en barrios densamente poblados de uso mixto cerca de centros urbanos, mientras que las industrias ‘basadas en la ciencia’ suelen estar concentradas en barrios con una baja densidad de población de uso individual en los suburbios. Se argumenta que estos patrones espaciales están relacionados con el hecho de que las redes entre empresas son más importantes en las industrias

Regional Studies, 2015

Vol. 49, No. 5, 883–898, http://dx.doi.org/10.1080/00343404.2015.1019846

© 2015 Regional Studies Association http://www.regionalstudies.org

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‘creativas’, mientras que las industrias ‘basadas en la ciencia’ confían más en las interacciones y el aprendizaje dentro de cada empresa.

Forma urbana Evolución Diversidad Creatividad Ciencia Barrio

JEL classifications: O1, O31, R11, R14

INTRODUCTION

Diversity is a central principle of evolutionary theory. Specifically, it is a central component of branching and recombination processes that tend to reinforce one another over time. Jane Jacobs (JACOBS, 1961, 1969, 2000) is often credited with making explicit the connec- tions between evolutionary economic processes and local diversity arguing that sustained urban vitality depends on a constant churn of people and ideas. This view is often contrasted with what has become known as the Mar- shall–Arrow–Romer (MAR) view (MARSHALL, 1890; ARROW, 1962; ROMER, 1986) that local specialization is essential for sustained growth. Research from the emerging field of evolutionary economic geography has directly entered the Jacobs–MAR debate particularly with an effort to find a middle ground with the concept of ‘related variety’ (FRENKEN et al., 2007). Many studies have attempted to address this debate empirically but without achieving any semblance of consensus (BEAUDRY and SCHIFFAUEROVA, 2009). The majority of such research involves using structural indicators of industrial diversity/specialization at the regional scale in order to find statistical relationships with growth. This type of analysis, however, represents only half of the for- mulation presented by Jacobs who was at least as, if not more, concerned with how specific urban environments have a direct impact on social interaction and ultimately the production of knowledge. This paper takes a closer look at the evolutionary social processes of knowledge production by taking a closer look at the urban form of the neighbourhoods in which particular knowledge- intensive industries tend to be spatially concentrated.

Specifically, detailed firm-level data are mapped onto three city regions (Montreal, Toronto and Vancouver) in order to demonstrate stark differences in sub-regional spatial patterns of industrial location. The results show that creative firms are heavily concentrated in central areas that are highly congruent with Jacobs’s notion of vibrant and diverse urban environments that she con- tends play a direct and important role in evolutionary socio-economic processes. Conversely, science-based firms tend to locate in more homogeneous suburban neighbourhoods that Jacobs suggests are detrimental to the same knowledge-based processes of social inter- action and learning. This provokes some important questions pertaining to the further development of evol- utionary economic geography:

. How might urban form impact evolutionary econ- omic and learning processes? Are certain industries influenced by urban form to a greater degree?

. How might one detect the impact of urban form on the evolutionary and learning processes of various industries? Does one need to consider sub-regional scales?

. Can/should planners and designers work with econ- omic development practitioners to create urban environments that facilitate these processes?

These questions are addressed from a theoretical perspective by drawing connections between the evol- utionary economic geography literature with emerging work on different knowledge bases as well as sub- regional explorations of knowledge-intensive industries. From an empirical perspective these connections are investigated using address-level firm data in conjunction with detailed contextual data on various aspects of urban form in order to highlight the differences in local environments between ‘creative neighbourhoods’ and ‘science neighbourhoods’. This paper presents each of these in turn before discussing the possible implications to the development of evolutionary economic geogra- phy theory and future research.

EVOLUTIONARY PROCESSES AND ECONOMIC GEOGRAPHY

A basic and central question to economic geography is ‘why does economic activity occur where it does?’. Putting neo-classical economic theories into spatial context is one of the traditional approaches that is widely practised. Specifically, models of the increasing returns to agglomeration are a major contribution to economic geography. Institutional approaches also have a long history of offering explanations to uneven spatial development particularly in conjunction with the notion that economic activity is embedded with social relations (GRANOVETTER, 1985) and local cul- tures (GERTLER, 1995). More recently, these dominant paradigms have been increasingly questioned on the basis that they are not able to account fully for the complex path-dependent processes that (re)produce the spatial distribution of economic activities (MARTIN and SUNLEY, 2007). To this point BOSCHMA and FRENKEN (2006) propose that an evol- utionary science approach to economic geography is warranted on the basis that it combines formal model- ling (neo-classical) with the recognition of heterogen- eity (institutional) while directly addressing processes such as search (MASKELL and MALMBERG, 2007), recombination (WEITZMAN, 1998; DESROCHERS, 2001), and branching (FRENKEN and BOSCHMA,

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2007) that have the potential to provide detailed expla- nations of economic activities and their geographies.

In a general sense, the recent attempts to develop a more formal evolutionary economic geography approach needs to be placed in context of the wider movement that explores the role of knowledge and its production in economic growth and regional develop- ment. SCHUMPETER (1942) in particular, initiated a great deal of thought and research on technological innovation and its impact on economic growth. One of the key aspects of this work is the recognition that innovation is both path dependent and, at times, disrup- tive. There are a great deal of similarities between DARWIN’S (1859) evolutionary processes and inno- vation processes such as recombination and branching. JACOBS (1969) also seized on these ideas in her appli- cation of these processes to diversity within urban con- texts due to divisions of labour based on increasing specializations. With each of these examples a central notion is that the evolutionary processes are fundamen- tally concerned with social interaction, and specifically the collision of differentiated knowledge. Such notions have a longstanding tradition in urban sociology, exem- plarily represented by Wirth’s understanding that the city has

been the melting-pot of races, people, and cultures, and a most favorable breeding-ground of new biological and cultural hybrids. It has not only tolerated but rewarded individual differences. It has brought together people from the ends of the earth because they are different and thus useful to one another, rather than because they are homogeneous and like-minded.

(WIRTH, 1938, p. 10)

This is also the point at which geography becomes an essential component of the equation in that social inter- action is not a-spatial. Despite advances in information and communications technologies, face-to-face contact (STORPER and VENABLES, 2004) and proxi- mity (BOSCHMA, 2005) remain vital to processes of social interaction and learning. In short, the geography of knowledge is both persistent and changing.

BOSCHMA and FRENKEN (2006) propose that this geography is generated across a variety of scales, from the micro/firm level at which routines are constructed, to the meso-level of industrial sectors (related firms) and networks (relationships), to the macro-level geography of the spatial system. This model provides a useful fra- mework to explain where this paper seeks to make a contribution. The contention in this paper is that at the meso-level there are significant differences in the knowledge bases that constitute the different industrial sectors. Furthermore, there are key differences in the social processes (and network structures) that produce each type of knowledge. This in turn suggests that the role of the firm may vary depending on the specific type of knowledge, and that ultimately there are impor- tant implications for the overall spatial system.

KNOWLEDGE TYPES AND SOCIAL PROCESSES

There are a number of literatures that explore the differ- ences between types of knowledge. While there is no specific model that can integrate each perspective, they do generally conform to the notion of a spectrum between artistic/cultural knowledge and scientific knowledge. There is also general agreement that the underlying social process vary somewhat across this spectrum. These differences are also reflected semanti- cally as ‘creativity’ is most often associated with the arts, ‘innovation’ with technology, and ‘discovery’ is most often used in conjunction with science. The psy- chology literature on creativity offers some detailed insights into how the production of artistic-based knowledge may vary from the production of science- based knowledge. STERNBERG and LUBART (1996) make a distinction between ‘open’ and ‘closed’ problem solving whereby ‘closed’ problems have specific answers while ‘open’ ones do not. AMABILE (1996) makes a similar distinction between heuristic knowledge production and algorithmic knowledge pro- duction, whereby the former can only be truly con- sidered ‘creativity’. The key difference is that heuristic knowledge production is based on the subjective values of both the creator and the evaluators, while algo- rithmic knowledge production is observable and repea- table by others who should come to the same conclusions. Little if any knowledge production can be purely subjective or objective, but these two ideal types can prove useful as ends of a conceptual spectrum of knowledge production. For example, FIEST (1999) employs such a framework when examining the differ- ences in the personality traits of highly respected artists and scientists.

The economic geography literature recognizes these distinctions in similar ways. SANTAGATA (2004), for instance, claims that creativity is ‘non-utilitarian’ as opposed to innovation which is motivated by specific and measurable improvement. Furthermore, he suggests that creativity is less path dependent than scientific endeavours. Different types of knowledge have also been directly applied to classifying industries. A leading example of this uses three distinct ‘knowledge bases’ (symbolic, synthetic and analytic) (ASHEIM and GERTLER, 2005; ASHEIM and HANSEN, 2009) as its organizing framework. Symbolic knowledge is ascribed to cultural and creative industries as it involves a high level of aesthetic value. Analytic knowledge is associated with science-intensive industries in that it requires a firmly rational, deductive approach. As its name suggests, synthetic knowledge is driven by making new combinations of existing knowledge in new and valuable ways and is mainly connected to industries with a technological focus. SPENCER (2011) proposes a similar classification system that emphasizes variations in knowledge production processes. He proposes a

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spectrum of processes from (artistic) creativity which is based on human experience and one’s understanding of it to (scientific) discovery which is based on the material world and one’s ability to explain it. Situated between these poles is (technological) innovation which is based on applying scientific knowledge in order to somehow improve human experience. The key message of these literatures is that ‘knowledge’ needs to be conceived in a pluralistic manner. Further- more, this has important implications for understanding how knowledge is produced by a variety of processes.

CONVERGENT VERSUS DIVERGENT THINKING, NETWORKS AND FIRMS

While there are a number of ways to segment types of knowledge perhaps the most important to the main argument being made in this paper is the degree to which types of knowledge require divergent thinking versus convergent thinking. Divergent thinking is typi- cally associated with the creative process whereby there is no single answer to a problem and so many possible solutions are sought. With convergent thinking there is a single optimal way to solve a problem and which involves bringing together existing knowledge in a novel way. If one can agree to the stylized fact that creative and cultural industries rely more heavily on divergent thinking and science and technology indus- tries depend more on convergent thinking then this has potential implications for both networks and the role of the firm. From a network perspective this suggests that it may be more of an imperative for people in creative and cultural industries to cast a wider net for knowledge and information. To this end, more weak ties (GRANOVETTER, 1973) and brid- ging of structural holes in networks (BURT, 1992) would be considered more important. In this case it would be crucial for firms to place themselves in a pos- ition to maximize exposure to as wide a range of influ- ences as possible. From a spatial system perspective this would mean being situated in a highly diverse environ- ment. For science and technology firms the key impetus may be to locate in a region where the range of specialized skills. In this case the role of the firm is to access and organize these skills in order to solve specific problems. Indeed, BROWN and DUGUID (2000) suggest that within Silicon Valley, one of the most important roles of the firm is to organize and connect skilled people that would other- wise not likely interact. Diversity at the regional level may be important to both creative and science-based industries, but they may differ significantly in terms of how they access and organize it.

There is a modest amount of evidence that suggests real and significant structural differences between crea- tive industries and science-based industries that map onto the three-scale model of evolutionary processes

proposed by BOSCHMA and FRENKEN (2006). At the network level the 2008 Canadian General Social Survey shows that individuals working in creative and cultural occupations (N = 277) have the largest social networks of any occupational group. On average these individuals report maintaining 60 relationships with family, friends, and acquaintances while those working in science and technology occupations (N = 732) have on average only 46 connections. Furthermore, the data shows that this disparity is almost entirely accounted for by the number of local (same region) acquaintances that each group maintains relationships with. In network language this can be interpreted as meaning that creative and cultural workers require much larger weak-tie networks than science-based workers. The second key difference detected in the data is that science-based firms are on average 2.5 times larger than firms in creative industries. Data from Dun & Brad- street indicate that creative1 firms2 have on average seven employees while the average for science-based firms is 17 employees. They key implication of these two basic pieces of data is that there are likely significant differences in the scale at which social processes of inter- action and learning tend to occur. Creative industries are characterized by larger social networks but smaller firms while science-based industries tend to involve smaller social networks but larger firms. The main point here is that a greater amount of social interaction/learning is more likely to occur between firms in the case of crea- tive industries and conversely social interaction/learning within firms is more prevalent in the case of science- based industries. To the extent that these processes are vital to the knowledge production and vitality of firms in each set of industries there are bound to have signifi- cant interrelationships with their respective spatial systems. It is this connection that is investigated in the empirical analysis of this paper.

JACOBS EXTERNALITIES

The role of diversity in urban economic growth and development remains an open question. Often formu- lated as contrasting the views of Jacobs with the MAR perspective that favours local specialization. They tend wholly to assume interaction between similar/dissimilar agents within local settings. This largely ignores the wider formulation proposed by JACOBS (1961) that the specific characteristic of urban form play a direct and important role in the social interaction and mixing of ideas that generate local dynamism. Arguably, the scale that mattered more to Jacobs was the neigh- bourhood. In particular, she argued that mixed land uses and a variety of building types within close proxi- mity meant that not only would there be more social interaction but that diverse people would mix as a matter of course. Scott offers a contemporary take on this notion and adds that cities and their environments

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are not just containers of social interaction but directly reflect cultural norms and values. He relates that

the creative field of the city can be seen, in short, as a system of cues and resources providing materials for imagi- native appropriation by individuals and groups as they pursue the business of work and life in urban space. But it is also a sort of canvas on which creative and innovative acts are variously inscribed. Within this field, individuals are continually if intermittently entangled in transactional exchanges with one another, and in this manner they receive and emit signals that are variously charged with information.

(SCOTT, 2010, p. 121)

A key point made by Scott is that the city does not simply act as a focal point for social interaction and learning but that there is are continual iterative processes between people and the environments which they inhabit. The main implication for the ideas proposed in this paper is that the particularities of urban environ- ments play an active role in the creative process.

While the role of urban environments in knowledge- production processes has been frequently tested at the regional scale, there are also a number of examples of sub-regional research (CURRID and CONNOLLY, 2008). For science-based industries this research has often focused on science parks and includes a significant policy component and associated criticisms (MASSEY, QUINTAS, and WIELD, 1992; SHEARMUR and DOLOR- EUX, 2000). Furthermore, the effectiveness of science park developments in terms of increased knowledge pro- duction and economic competitiveness(WESTHEAD and BATSTONE, 1998). For the creative industries sub- regional geographies are commonly viewed as ‘scenes’ which take into account surrounding supporting activi- ties and amenities such as cafes, restaurants, bars, and per- formance venues that blur the lines between production and consumption of cultural products (MOLOTCH, 2002; CURRID and WILLIAMS, 2010; SILVER et al., 2011). The literatures on science parks and creative scenes give mention to urban form, in neither instance is it often considered a central aspect of each place. This seems somewhat at odds with the role that space has in bringing people together (or keeping them apart) and how vital social interaction is to the production of knowledge. The empirical section of this paper examines the spatial arrangements of creative industries and science-based industries in three Canadian city-regions. Specifically, the urban form of ‘creative’ and ‘science- based’ neighbourhoods are compared on the basis of how they may affect patterns of social interaction, with a focus on interaction between firms.

AN ASSESSMENT OF URBAN FORM AND KNOWLEDGE TYPOLOGIES

Quantitative research on industrial clusters often utilizes data at the regional scale that enables comparisons to be

made between places. A problem with this approach can be that nuances and variation within regions. This paper addresses this problem by using comprehensive firm- level data provided by Dun & Bradstreet in order to demonstrate stark differences of the spatial arrangements of creative industries and science-based industries within the three largest city-regions in Canada (Toronto, Mon- treal and Vancouver). Clear and consistent patterns of spatial concentration of these industries are evident in all three regions. Creative industries are found on the edge of the central business districts while the science industries tend to be located in suburban industrial and office parks. By examining the common characteristics of these distinct neighbourhoods one can begin to make inferences about the patterns of social interaction between workers in these firms.

The latest census figures report that the Toronto (5.6 million), Montreal (3.8 million) and Vancouver (2.3 million) regions account roughly one-third of the population of Canada. They are also the most economically diverse (BECKSTEAD and BROWN, 2003) and as they are the main gateways for immi- grants to the country they are also home to the most culturally diverse populations. ‘Creative’ industries and ‘science’ industries are defined using a similar method to SPENCER (2011) that cross-tabulates the percentage of workers with post-secondary qualifica- tions and the field of study in which their highest qua- lification was earned for detailed industries (four-digit NAICS; N = 300). From this method six creative industries and five science industries are identified (Table 1) as being the most archetypical cases that align with the twin concepts of creative and scientific knowledge production. Over half of all employment in ‘creative’ industries (57.1%) and ‘science’ industries (54.7%) in Canada is located in the three largest city- regions. This is a significantly disproportionate share as the location quotients for the two sets of industries across the three cities are 1.65 for the ‘creative’ indus- tries and 1.58 for the ‘science’ industries. These results are similar for each group of industries for each indi- vidual city-region.

The data provided by Dun & Bradstreet include roughly 1.4 million individual records of establishments for the years 2001, 2006 and 2011 representing an esti- mated 98% of all establishments across all sectors. All three regions display similar industrial location patterns with the creative industry establishments heavily con- centrated on the edge of the central business districts and the science industry establishments predominantly situated in suburban areas (see Appendices A–C in the Supplementary data online). These patterns are charac- terized by a series of contrasting traits. The creative industry neighbourhoods tend to be centrally located in older and denser areas that are highly accessible by public transportation, while the science industry neigh- bourhoods tend to be located in newer lower density areas, often in close proximity to the junctions of

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major highways. There are also localized clusters of science industry firms in the city cores, which are mainly software businesses that have relationships to the finance industry and similar local customers. Average gross rents tend to be higher in creative neigh- bourhoods than in science neighbourhoods, although many creative firms choose the lower cost areas within the sub-markets of central areas while the opposite is true for science firms seeking higher quality and more expensive space in suburban markets. The urban/subur- ban patterns of creative/science firms seems to be irre- spective of firm size and revenue. This suggests that traditional urban economic cost-based factors are not the main drivers of location decisions. Rather there seems to be a willingness, particularly of creative firms, to pay more in order to be in close physical proximity to similar businesses. The dense sub-regional agglomera- tion patterns of creative industries appear to be more relational in nature than economic. Both types of neigh- bourhoods usually have local anchors, but with the crea- tive industries the anchors are just as likely to be institutional (higher education, public galleries and thea- tres) rather than large firms as is the tendency with the science-based industries.

There are also stark patterns associated with the resi- dential patterns of professionals who work in each type of industry (see Appendix D in the Supplementary data online). Creative and cultural workers live in high numbers in the same neighbourhoods where creative industries are located. There is a high degree of overlap between home location and work location that suggests a high degree of overlap between home life and work life. This is congruent with research on various creative ‘scenes’ that expand on the notion that the economic and social dimensions of creative work are often inseparable. Perhaps the more surprising finding is that scientists also display residential prefer- ences that appear to be in close proximity to areas with high level science industry employment. The difference, however, is that rather than being a symbio- tic overlapping geography, scientists tend to live in

suburban neighbourhoods that are adjacent to locations with heavy concentrations of science industries. The lack of any direct overlap is mainly due to the strict sep- aration of land uses in the suburban areas of these city regions. It is unclear as to whether a greater mixing of land uses would engender a similar pattern of overlap as seen with the creative industries. From such general spatial analysis there does not seem to be any compelling evidence of significant integration of work life in the science industry neighbourhoods.

The main implication of these patterns is the degree to which the spatial arrangements of work and residence may affect the probabilities of social interaction and learning beyond the intentional connection made through the course of a traditional workday. The denser, more urban, and overlapping geographies of the ‘creative’ industry workplaces and the homes of their workers suggests that the likelihood of interaction is greater than it is for the less dense, more suburban, and separated geographies of ‘science’ industry workplaces and residential neighbourhoods.

A more detailed examination of specific neighbour- hoods in each of the regions helps to reveal even greater nuances in the contrasting spatial arrangements of science industries and creative industries. Figs 1–5 highlight pairs of science and creative neighbourhoods for each of the three regions in order to provide additional visual clues as to their specific characteristics. All maps are to the same scale, representing 4 km2, so that direct comparisons can be made. Specifically, Fig. 1 shows that the densities of firms and employ- ment are much higher in creative neighbourhoods than in science neighbourhoods. In many cases mul- tiple creative business establishments are located in the same buildings and blocks, whereas the science firms are much more spatially diffused. These contrast- ing proximities suggest a higher probability of direct face-to-face interaction as well as serendipitous encounters in the creative neighbourhoods than in the science neighbourhoods. As was stated above, a great deal of this has to do with the differences in

Table 1. Overview of ‘creative’ and ‘science’ industries

‘Creative’ industries ‘Science’ industries

NAICS Codes’ definitions 5121 Motion picture and video industries 3254 Pharmaceutical and medicine manufacturing 5122 Sound recording industries 5112 Software publishers 5151 Radio and television broadcasting 5415 Computer systems design and related services 5414 Specialized design services 5417 Scientific research and development services 7111 Performing arts companies 6215 Medical and diagnostic laboratories 7115 Independent artists, writers and performers

Employment LQ National share (%) Employment LQ National share (%)

Montreal 43550 1.62 18.4 68455 1.61 18.4 Toronto 62855 1.63 26.6 98505 1.62 26.4 Vancouver 28385 1.76 12.0 37075 1.46 9.9 Three-region total 134790 1.65 57.1 204035 1.58 54.7

Note: LQ, location quotient.

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land use and zoning between the central areas and suburbs. Fig. 2 shows the land use patterns of the crea- tive and science in neighbourhoods in each of the three regions. In each case the creative neighbourhoods show a distinctly mixed residential, commercial, institutional, and recreational land-use pattern while the science neighbourhoods tend to be exclusively zoned for com- mercial purposes. Mixed land-use patterns are an indi- cation that people are performing all manners of activities within the neighbourhood. Being directly physically integrated into such a diverse landscape is

bound to have knowledge spillover effects, many of which are unintentional. Land use is not only more mixed in creative neighbourhoods but the neighbour- hood structure is also more finely grained which suggests greater variation in the landscape. Fig. 3 dis- plays the block patterns for both types of neighbour- hood. The street patterns of the creative neighbourhoods are clearly more finely grained and differentiated while the science neighbourhoods are defined by much larger blocks. These patterns are sug- gestive of greater walkability in the former which, in

Fig. 1. Firm locations in ‘creative’ and ‘science’ neighbourhoods in Montreal, Toronto and Vancouver

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combination with a mixed land use pattern, opens up more possibilities for diverse and serendipitous encoun- ters. Fig. 4 provides further evidence of greater vari- ation in the creative neighbourhoods by comparing the patterns of building footprints. JACOB’s (1969) sug- gestion that new economic uses demand old buildings seems to hold true for the creative industries which tend to reside in buildings adapted from their original purposes, but not for the science industries which are more likely to be in purpose-built office and light

industrial premises. A final note of comparison between these two types of neighbourhoods is the rela- tive opportunities for consumption. Fig. 5 shows the locations of cafes, restaurants, and bars in each neigh- bourhood. They are differentiated according to whether they are single locations or multiple/chain locations. There are vastly more consumption opportu- nities in the creative neighbourhoods which reinforces the notion that such places are intense sites of social interaction. Additionally, the cafes, restaurants, and

Fig. 2. Land-use patterns of ‘creative’ and ‘science’ neighbourhoods in Montreal, Toronto and Vancouver

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bars are more likely to be single locations. This suggests a greater degree of authenticity in consumption oppor- tunities, meaning that the offerings are more likely to be produced locally and reflective of the local culture in which they are integrated. One further, importance of the presence of cafes, restaurants, and bars is that

they attract people from other neighbourhoods and by doing so extend the time period during each day that the neighbourhoods. This adds to the social vibrancy of the creative neighbourhoods and possibili- ties for social interactions that are less available in the places that science industries tend to inhabit.

Fig. 3. Block patterns of ‘creative’ and ‘science’ neighbourhoods in Montreal, Toronto and Vancouver

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Beyond the static picture of the creative and science neighbourhoods in the three regions, growth and change dynamics also lend some insights into the social mechanisms in each type of industry. There is a relatively consistent spatial pattern of change in the number of firms for each set of industries in each

region (see Appendix E in the Supplementary data online). Change in the spatial patterns of the science- based industries seems to occur between competing sub- urban office markets. While the specific reasons cannot be well explained with the data in this paper, possible underlying causes for these shifts include new office

Fig. 4. Building footprint patterns of ‘creative’ and ‘science’ neighbourhoods in Montreal, Toronto and Vancouver

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construction that offers superior facilities as well as price variations. The important point in this respect is that while there are shifts between suburban sub-markets, they do not vary significantly by the characteristics dis- cussed in the previous sections. The pattern of local change for the creative industries in two of the three regions is more specific and clear. In Montreal and Toronto creative industry firms are moving from directly next to the financial district to areas slightly further away. In Montreal the movement is mainly

from Old Montreal to Le Plateau and Mile End, while in Toronto it is from Queen West to Parkdale and the Junction. In Vancouver there some evidence of a move from Yaletown and Gastown to the Broad- way Corridor, but this picture is less clear. This push away from the centre is congruent with notions of the creative industries requiring both central and inexpen- sive locations. This combination of traits however, makes such firm locations highly susceptible to forces of gentrification. This is somewhat detectable in the

Fig. 5. Cafe, restaurant and bar locations in ‘creative’ and ‘science’ neighbourhoods in Montreal, Toronto and Vancouver

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data on cafes, restaurants, and bars as there is a similar pattern of more multiple/chain locations entering the areas in the traditional creative neighbourhoods. Sub- sequently the areas of high growth show a higher level of authenticity.

While none of the many defining characteristics of creative and science neighbourhoods evident from this data on their own provide any direct observations of different types and amounts of social interaction, in com- bination they offer a strong circumstantial case that there are direct linkages between the physical characteristics of neighbourhoods, social interaction, and knowledge pro- ducing activities. Table 2 outlines a summary of the rela- tive traits of creative neighbourhoods and science neighbourhoods. The characteristics associated with the creative neighbourhoods, namely dense, mixed use, and diverse landscapes, are suggestive of environments that provide abundant opportunities for social interaction outside of the regular workplace. Conversely, science neighbourhoods display characteristics that imply very few opportunities for social interaction outside of business premises. While it is clear that both creativity- and science-based industries are highly dependent on knowl- edge production which in both cases involves social inter- action, the differences between the specific contexts that they tend to inhabit suggests that the spatial patterns of these social processes vary significantly.

THREE QUESTIONS CONCERNING EVOLUTIONARY PROCESSES, URBAN FORM AND KNOWLEDGE TYPOLOGIES

The empirical section of this paper demonstrates that there are stark differences in the spatial systems of creative industries and science-based industries. Distinct knowl- edge production processes appear to have significant

implications to the characteristics of constituent networks and firms. Furthermore, these differences are not easily detected at the traditional regional scale of analysis but require a closer look at sub-regional spatial patterns of economic activities. In order to discuss the possible impli- cations of these findings the paper returns to the questions posed at the beginning of the paper.

How might urban form impact evolutionary economic and learning processes? Are certain industries influenced by urban form to a greater degree?

From a theoretical perspective urban environments play a role in knowledge production processes by influencing the probabilities of interaction and learning between agents. As SCOTT (2010) suggests, cities are also made from human knowledge and thus reflect back cultural information to their inhabitants. Cities are highly complex entities that offer many possibilities for novel ideas to form and grow. Evolutionary processes are fun- damentally probabilistic in nature and certain spatial arrangements of workplaces, residences, and ‘third spaces’ such as streets, parks, restaurants, and cafes, are certain affect the likelihood of interaction and sub- sequent learning. It is also essential to consider what types of knowledge may be more likely exchanged as a result of largely unintentional encounters in urban settings. Scott seems to suggest that cities are more apt play a direct role in the production of cultural knowledge.

There are indications from the data that particular types of urban environments are an important factor in the location of creative industries within dense and diverse areas of cities that offer maximum opportunities for social interaction between individuals and across firms. The intense spatial clusters in the three cities indi- cate that specific neighbourhoods provide particular ecosystems which creative scenes require in order to thrive. These are mixed-use neighbourhoods that provide a plethora of opportunities for social interaction outside the walls of the firm. Residential locations of workers are almost totally congruent with workplace locations suggesting that the live–work relationship is deeply intertwined. Overall, there is the sense that the city itself is a much greater input to the creative process and that cultural knowledge can be obtained by simply walking down the street. The large amount and variety of such knowledge can be directly incorpor- ated into the knowledge production process through forging novel combinations of such experiences. These experiences are often not consciously planned but rather owe to the serendipity of highly complex environments from which the unexpected arises. Based on these observations it certainly can be argued that the creative industries involve evolutionary pro- cesses in which the city itself plays an important role.

Science-based firms are typically located in much different types of neighbourhoods than creative firms.

Table 2. Summary of neighbourhood characteristics

Characteristics ‘Creative’

neighbourhoods ‘Science’

neighbourhoods

Firm location Edge of core Suburban Office rents Medium–high Low–medium Firm structure Micro-small Medium–large Anchors Venues; institutions Large firms; institutions Workforce

location Overlapping Adjacent

Transportation Public; walk; bike Private (cars) Density Very high Low–medium Land use Mixed Mono Building types Varied; adapted reuse New; purpose built Bars, restaurants,

cafes Dense; authentic Sparse; inauthentic

Change/ evolution

Gentrification Sprawl

Social dynamics Larger social networks; inter-firm (?)

Smaller social networks; intra-firm (?)

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Often single use and car centric, they do not present very many opportunities for social interaction outside the walls of the firm. People who work in such neigh- bourhoods do not likewise live in them suggesting a greater separation between home life and the work- place. As the neighbourhoods have typically been pro- duced in an intentional and highly planned manner they do not provide the same sort of opportunities for serendipitous interaction and mixing. With these factors in mind, it is difficult to observe any sense of how Jacobs-type externalities play a day-to-day func- tioning of science-based industries. That being said, it would be a mistake to claim that there are not evol- utionary processes at work within these industries. Instead, it would be more prudent to suggest that the social dimensions of the evolutionary process in each type of industry vary in their respective natures but also vary according to scale. It seems that social networks play a larger role in the creative industries which involves more inter-firm interaction while science- based firms contain more interaction and learning pro- cesses. This does not mean that local externalities do not play an important role in the evolution of science based but rather they may be more difficult to detect as they may happen on a less frequent basis. As a result, the specific neighbourhood level characteristics may not matter as much and hence the difference in the sub-regional spatial patterns.

How might one detect the impact of urban form on the evolutionary and learning processes of various industries? Does one need to consider sub-regional scales?

While science-based industries cluster in the same type of regions as creative industries they occupy very differ- ent types of neighbourhoods within those regions. While the emerging evolutionary economic geography literature does not place a greater importance on the tra- ditional regional scale, there seems to be a tendency to use this scale empirically. There are certainly a number of pragmatic reasons for this that relate to data avail- ability and how labour market areas are defined. It would be remiss however, to adopt the traditional regional scale as the main unit of analysis as it tends to obfuscate the actual social interactions that fuel the evol- utionary processes that are the ultimate aim of such inquiry. There has been a large amount of research undertaken both the firm-level and (regional) spatial systems, it may be that the meso-level of industries and networks (GLÜCKLER, 2007) might provide the greatest future insights.

With this in mind, there is perhaps somewhat of an emerging tendency to focus on science and technology industries while neglect others. These sectors do tend to drive long-run economic growth and have been well discussed in the literature. Technological evolution has arguably a longer and richer history in the literature par- ticularly as it pertains to economic development

(SCHUMPETER, 1942; NELSON and WINTER, 1982; ARTHUR, 1989). The danger in this respect is that cul- tural evolution will be viewed in a lesser role, one which it may not deserve especially in light of theoretical advances in economic geography since the ‘cultural turn’. More to the point, it may be preferable that the co-evolution of technology and culture be studied within economic geography as the two are often diffi- cult to untangle. Specifically, this could involve research that spans multiple types of economic sectors and under- standing how evolutionary processes occur across mul- tiple scales.

Can/should planners and designers work with economic develop- ment practitioners to create urban environments that facilitate these processes?

There is always a danger with using concepts and rheto- ric such as evolution that are derived from the language of the natural sciences as it can lead to a laissez-faire approach to policy making. Any process that can be described in such ways can lead to the opinion that it be best left alone to take its ‘natural course’. Evolution can be unforgiving and harsh, especially to the ‘weak’. This is not how progressive societies are built. It would also be false to assume that such processes can be fully managed, as complex self-organizing systems tend to defy such attempts over the longer term. Instead, a more suitable approach may be to understand the evolutionary principles that generate knowledge and spur economic growth with the aim of working with these processes in order to achieve optimal outcomes that involve a balance between growth and equality (MACKINNON et al., 2009). A practical example in relation to the findings of this paper is the notion that neighbourhoods can be designed in ways that increase social interaction and learning. This means implement- ing design guidelines that follow the principles of urban- ism promoted by Jacobs and others, specifically creating arranging buildings and the spaces between them in a way that encourages people to come into direct contact with one another. Small walkable blocks, mixed uses, and public spaces are all key ingredients. The practice of urban design at the neighbourhood level is more often concerned with issues of ‘liveability’ rather than economic vitality. There may indeed be cases where urban design decisions lead to more mixed uses and greater levels of social interaction, but it is not usually motivated from an economic development perspective. In order to accomplish this better, more research is needed on how people and firms relate to one another in urban (versus suburban) environments. One first needs a more nuanced understanding of what physical forms foster more interaction which underpins evolutionary economic activities.

While the evidence in this paper suggests that crea- tive industry firms have more to gain from Jacobs

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externalities at the neighbourhood scale – this should not be interpreted that other types of economic activity do not benefit at all. There is a growing recognition that even in science-based industries working in close proxi- mity that increases social interaction between firms (and related institutions) can provide advantages. Examples of such thinking include the MaRS Discovery District in Toronto and New York’s ‘Tech City’ on Roosevelt Island that each involves situating science-based start- up businesses in the immediate vicinity of universities (and other related institutions) near the downtown core. It may be too early to deem these attempts success- ful but it shows instincts towards building environments that may foster the evolution of firms and industries. A possible danger in this respect is that it may not be poss- ible to design and engineer such environments. Most of the creative neighbourhoods highlighted in this paper were not produced intentionally but rather evolved into what they are due to their highly flexible and adapt- able characteristics. This in the end may hold the secret to any successful marriage between urban design and economic development.

CONCLUSIONS

While there is general recognition that cities act to magnify social interaction, particularly between hetero- geneous agents, and thereby stimulate learning, many of the precise details of how this happens are still relatively unclear. Cities are highly complex entities and it is cer- tainly not possible to map all of the relationships and exchanges that occur within them. Much of one’s knowledge and understanding of how urban environ- ments influence human activity (and vice versa) can only be extrapolated from a smaller scale or indirect observation. In relation to evolutionary processes of learning from interaction within and between urban regions there are many questions that one can ask even though one never will be able to answer them fully. For example, who interacts with who? How often do exchanges take place? Where do they take place? What is learned from these interactions? What is the impact of information and communication tech- nologies? Again, on a city-wide systematic level these questions can never be comprehensively addressed empirically, but instead one can build up fuller pictures from smaller studies. The danger in doing so is making generalizations that are not accurate or representative.

Creative industries and science industries tend to be found in high concentrations in the same urban regions. One could suppose then that the underlying dynamics are also similar. This paper examines the sub-regional locations of these industries (and their workforces) and finds quite divergent patterns suggesting that quite different dynamics are at work. While firm location is not ‘everything’ nor is it

deterministic in terms of outcomes, it can say a great deal about the relational aspirations of economic activity in a more probabilistic fashion. The dense neighbour- hood-level clusters of creative industry firms in close proximity to the residences of their workers means that the individuals engaged in these economic activities are in the same spaces with one another for a greater amount of time. This suggests that there is a higher like- lihood of more social interaction in such environments. This is accentuated by the observations that there are also more ‘third spaces’ such as parks, restaurants, cafes, and vibrant streets in creative neighbourhoods that provide specific places for such opportunities. Highly contrasting observations are made of the characteristics of the neighbourhoods where science-based industries locate. These are typically low density suburban locations with little space provided for social interaction to occur outside of the premises of firms. Furthermore, there is no immediate relationship between where science workers live and where they live, thus reducing the space–time overlap and the probability of inter- action. These contrasting sub-regional geographies may in part reflect contrasts underlying social dynamics and how they are influenced by particular urban environments. To date much of evolutionary economic geography has explored the evolution of science and technology and it should not be assumed that the dynamics of cultural evolution are similar in character. Furthermore, the relationship of creative industries to the city should not be taken on par with the relationship of science industries to the city.

There is have an improving understanding as to how the specific design of buildings, and in particular the space between buildings, influences how people relate to one another. There is also a decent understanding that evolutionary processes of social interaction and learning as they pertain to creativity and innovation in the economy. However, the link between these two areas of research could certainly be made stronger. As noted above, for reasons of complexity, research on the social dynamics of urban economic processes is extremely difficult to do on a systematic multi-sectoral basis. A research instrument that does hold potential for this agenda is the Canadian General Social Survey.3 In par- ticular, there are special topics on both social engagement (i.e. networks) and time use that provide rich detail on how individuals interact with one another as well as what they do over the course of a typical day. The tra- ditional drawback of such datasets is that the geography variables are extremely limited (i.e. rural/urban; pro- vince) and thus cannot lend any further insights into the influences of specific urban settings. That being said the underlying survey instrument asks respondents for their postal codes which means the possibility exists for the generation of variables based on more nuanced geo- graphical data. Examples, of this could be population density, or walkability scores. Such variables when cross-tabulated with social network and time-use

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variables could go further in addressing question of how urban settings influence social dynamics. When cross- tabulated by the economic activity of the respondent this could then be linked to questions pertaining to par- ticular industries and occupations. Ideally, there would be additional work related questions in the General Social Survey (GSS) such as work postal code and a differ- entiation between social engagement with colleagues and friends/family. If such variables were included in the future there could be a much richer economic dimension to the survey which could greatly enhance research connecting local environments, networks, and creativity and innovation.

Acknowledgements – The author would like to thank five anonymous reviewers for their insightful comments. He also thanks all members of the local IDEAs team. Additional thanks to Dun & Bradstreet for providing the data. A previous version of this paper was presented at the Association of American Geographers Annual Meeting in New York, NY, USA, February 2012.

Disclosure statement – No potential conflict of interest was reported by the author.

Funding – Funding for this research was generously pro- vided by the Canadian Foundation for Innovation and the Ontario Research Fund.

Supplemental data – Supplemental data for this article can be accessed at http://dx.doi.org/10.1080/00343404. 2015.1019846

NOTES

1. Defined in detail in the next section of this paper. 2. Data are at the establishment level. 3. The Canadian General Social Survey differs from the

American General Social Survey in a key respect in that special topic questions are asked of all respondents (N = 25 000) rather than a smaller subset (N = 1000) allowing for more robust cross-tabulations.

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