- Email: [email protected]
commercial geomatics and planning
Progress in Planning 60 (2003) 1–11 www.elsevier.com/locate/pplann
Editorial
Business/commercial geomatics and planning Maurice Yeates School of Graduate Studies, Ryerson University, 350 Victoria St., Toronto, Canada M5B 2K3
The geomatics industry has a long tradition in Canada spanning over a century—for example, the Canadian Institute of Geomatics was founded in 1882. More recently, the Canadian government has been investing in: geomatics research, with the Geomatics for Informed Decisions (GEOIDE) university based research network (www.geoide.ulaval. ca); geomatics training, with its Geomatics Professional Development Program; and, the dissemination of spatial data through the internet (e.g. investing CDN$60 million in a GeoConnections web-based initiative). Geomatics is now one of Canada’s fastest growing areas in the information/knowledge-based economy. Geomatics has traditionally focused on applications in the ‘physical’ sphere, such as, environmental management, land reform, development planning, infrastructure management, natural resource monitoring and development, and coastal zone management and mapping. Current initiatives, particularly through GEOIDE, involve encouragement of geomatics capabilities and applications in the business sector, particularly with respect to commercial activities (Yeates et al., 2002). Geomatics has emerged from a number of well established and new areas of research and professional application—surveying and mapping; geodesy; photogrammetry; remote sensing; geographic information systems/science (GIS); and, new techniques in global positioning systems (GPS), which lie broadly at the intellectual intersection of geodesy and navigation (Longley et al., 1999; Bernhardsen, 2000). Two features that this confluence of interests have in common are that: they are concerned with information that has spatial properties and can therefore be geo-referenced in some way, usually with global coordinates (latitude and longitude); and, their utility has undergone a renaissance since 1990 due to the rapid development of computing and visualization technologies (usually through stand-alone or networked PCs). Thus, areas that were once the interest of a mathematically and technically oriented few are now more accessible. With this greater accessibility, applications have become more widespread and have diffused rapidly in public sector related planning (Allinson, 1998; Gilfoyle and Thorpe, 2002). The diffusion has been less rapid in the private commercial sector, even though much decision-making is in the context of spatially distributed information. E-mail address: [email protected] (M. Yeates). 0305-9006/03/$ - see front matter q 2003 Elsevier Science Ltd. All rights reserved. doi:10.1016/S0305-9006(02)00088-0
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M. Yeates / Progress in Planning 60 (2003) 1–11
The information involved in business/commercial geomatics relates to the consumer service sector of the economy. In the Canadian situation, this sector includes retail activities, personal services, consumer related FIRE services (that is, retail aspects of finance, insurance, and real estate), restaurants, and entertainment facilities. This broad range of activities provide jobs for about 5.3 million people, or about 43% of the labour force, working in 1.2 million locations (Yeates, 2001). As it is individuals and household units that provide the demand (market), and commercial enterprises, or units (storesoutlets) that provide the supply, and each of these has a spatial location, it is vitally important that analytical work be undertaken at the highest level of geo-referenced aggregation possible—ideally involving unit record data relating to individuals or households.
M. Yeates / Progress in Planning 60 (2003) 1–11
3
CHAPTER 1 Business/commercial geomatics: the demand side An outline of the array of information that is available on the demand side for researchers in business/commercial geomatics is presented in Fig. 1. Much of the information is obtained from Statistics Canada, particularly that collected in the decennial census and the quinquennial censuses related to persons, households, and businesses; and, in the annual (for example, firms), monthly (for example, labour force), and special (for example, household expenditures) surveys. Though this data may, as is indicated in Fig. 1, be collected as unit records, that is, for individuals, or household units, confidentiality requirements impose aggregation on dissemination of the information—either aspatially (such as by income band, or economic sector), or spatially. In consequence, Statistics Canada has generated its own census geography which ranges from, for example, dissemination areas (high level of aggregation or resolution), frequently referred to as small area data, to provinces (low level of aggregation or resolution). Another major source of demand-side information is proprietary data, collected by individual companies, often at point-of-sale, or through sample surveys that are usually undertaken by companies involved with micro-marketing. This information is frequently aggregated to at least postal codes, and often lower (such as CMAs), for use by companies purchasing the information. To enhance usefulness, some commercial providers purchase high-resolution socio-economic data from Statistics Canada, integrate this with proprietary information from the company with which they are working, and reformat the information to postal codes. The transmutation of much of the data from census geography to postal code geography requires numerous apportionment assumptions.
Fig. 1. The demand-side.
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M. Yeates / Progress in Planning 60 (2003) 1–11
The central objective is to generate the data on as small an area basis as possible, that is, six digit (FSA/LDU—forward sortation area and local delivery unit). The most important analytical issues, however, that arises in business/commercial geomatics are: to what extent can inferences about individuals, households, or businesses (high level) be made from models based on information aggregated to some lower level? This issue is referred to colloquially as the ecological fallacy, and technically as the modifiable areal unit problem (MAUP), and is essentially unresolved (Fotheringham et al., 2000; Amrhein and Reynolds, 1997). The MAUP problem involves two questions: (1) can a diagnostic tool be developed that will indicate the extent of aggregation effects embedded in data sets? and, (2) is there a way of ‘solving’ the problem (i.e. a way of achieving, for example, stability in means, variances, and so forth, as data is aggregated)? The answer to the first question is that diagnostic tools are being developed (Getis, 1991; Getis and Ord, 1992; Boots et al., 2002). The answer to the second is that while there may be approaches to addressing sectoral aggregation issues (King, 1997), spatial aggregation issues are less tractable (Fotheringham et al., 2000).
M. Yeates / Progress in Planning 60 (2003) 1–11
5
CHAPTER 2 Business/commercial geomatics: the supply-side Supply-side data is less readily available in Canada for business/commercial geomatics studies than demand-side data. The unit record equivalent on the demandside would be data pertaining to each individual business location. Unfortunately, Statistics Canada does not provide an array of information about businesses at the small area level analogous to that which is provided about individuals and households. The information that is collected is usually disseminated in a highly aggregated fashion (invariably provincially), and even the best high resolution data, such as the small area retail trade estimates (SARTRE), involve a large number of suppressions to ensure confidentiality. Thus, business/commercial geomatics research that focuses on the supply-side has to involve data compiled from a variety of sources—telephone directories, business directories, assessment rolls, and field work—the analysis and linking of which requires considerable human and computing resources. The Centre for the Study of Commercial Activity (CSCA) has focused on the compilation of supply-side relational database warehouses for a number of time periods to provide information analogous to that more readily available for the demand-side (Fig. 2). It is ‘relational’ because different sources and aggregations that comprise the warehouses can be linked through both spatial and non-spatial identifiers, such as point identifications (latitude and longitude) and/or North American Industry Classification (NAIC) codes, to generate the types of data files appropriate to the analysis being undertaken. For example, a comparative analysis of changing vacancy rates in malls and retail strips requires aggregation of the unit
Fig. 2. The supply-side.
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M. Yeates / Progress in Planning 60 (2003) 1–11
record data to malls and streets. Thus, it is possible to examine the spatial implications of the changing role of different supply-side channels (downtowns, retail strips, malls, power centres, and latterly B to C e-commerce) in various parts of the country or within particular urban areas. As B to C e-commerce does not involve ‘bricks and mortar’ at market locations in the same way as the other channels, it is represented in Fig. 2 in more diffuse lettering.
M. Yeates / Progress in Planning 60 (2003) 1–11
7
CHAPTER 3 Spatial decision-support systems The demand and supply-side databases thus combine to form an interrelated data warehouse that can be utilized to provide decision support systems in a variety of private and public sector contexts. The support system (Fig. 3) normally involves four highly inter-related activities: (i)
Acquisition of geo-referenced information, usually from a variety of image and nonimage sources, and often involving different methods of collection. In business/ commercial geomatics, non-image sources dominate, though just-in-time logistical systems (e.g. regional warehouses serving a network of stores) are increasingly using satellite-based GPS tools for precise tracking of transport vehicles. (ii) The transformation, fusion, and management of data in large, three-dimensional (geo-referenced, temporal, measured) relational data warehouses (Fig. 4). As much of the data may be heterogeneous, and also involve different types of spatial units and inconsistent time periods, combination and integration can result in ‘new data’. This ‘new data’ isolates the essence of the spatial and temporal variations, and hence the uncertainties, inherent in the ‘old’ (Zhang and Goodchild, 2002). (iii) Analysis and spatial modeling to test theories and facilitate spatial forecasting. Spatial analytic tools (Bailey and Gatrell, 1995; Griffith and Layne, 1999) are being incorporated more into business/commercial geomatics. Descriptive tools for spatial
Fig. 3. A spatial decision support system.
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M. Yeates / Progress in Planning 60 (2003) 1–11
Fig. 4. A relational database in business/commercial geomatics.
data, ranging from nearest neighbor and point pattern analysis various to techniques for ascertaining spatial autocorrelation and clustering are now fairly common (Boots et al., 2002). Dynamic forecasting, invariably involving spatial interaction and multivariate regression models (de Vries et al., 2001), have been the traditional tools of market area and site location analysis for some time (Huff, 2000). But, increasingly, spatial regression, geographically weighted regression (Fotheringham et al., 1998), categorical data analysis (Wrigley, 1985), and linear programming (Lea and Menger, 1991; Baray and Cliquet, 2002), are being incorporated into spatial modeling and forecasting exercises. (iv) Analysis of planning and policy implications. The important role for geomatics, especially following the model building exercises, is to provide various scenarios based on various assumptions provided by the decision-makers (or anticipated by the spatial analyst). In essence, the models identify the underlying ‘drivers’ of the changing business/commercial environment (such as store sales in a national chain), and different assumptions concerning the ‘drivers’ provide a variety of forecasts (Clarke et al., 1998; Vlachopoulou et al., 2001). Decision-makers then focus on those ‘drivers’ that can be changed or altered by the company (Buckner, 1998).
M. Yeates / Progress in Planning 60 (2003) 1–11
9
CHAPTER 4 The collection The papers in this collection contribute in their various ways to the public/private planning support system. The paper by Gomez-Insuasti on the spatial structure of the Canadian commercial sector, highlights the ways in which new forms of supply-side segmentation, such as ‘malls, big-box, and flagship formats’, are coming to dominate certain aspects of the economy, particularly in larger metropolitan areas and the western Provinces. This paper thus contributes to impact analysis and asset management—the ‘creative destruction’ (Schumpeter, 1934) of new supply-side formats affects urban and regional planning and the optimization of asset mix. Hernandez’s paper is unusual in commercial analysis—it is a single industry study that focuses not only on aggregated national trends, but local spatial manifestations of growth and change. The industry is the home improvements sector—which in 2001 generated sales of over $22 billion, accounting for 7% of total retail sales in Canada, and involved more than 5500 home improvement chain stores. The sector has undergone significant growth and structural transformation over the last decade due to: burgeoning housing starts which have been at historic highs for a number of years; widespread implementation of new supply-side technologies, especially those associated with big-box retailing; and, consequent high levels of corporate concentration which have impacted negatively on independent retailers, buying groups and retail co-operatives. The study thus addresses issues relating to impact analysis and municipal planning. Simmons and Jones examine the relationship between the location of the supply-side and the market served. While the magnitude and range of the supply-side in large markets is quite predictable, small markets display considerable variation due to differences in levels of centrality, per capita income, and local specialization. Nevertheless, market growth is, in general, the major influence on growth in supply-side employment and business activities. The findings in this study contribute particularly to regional planning, for public policy in Canada has to focus on the one hand on the consequences of demand driven growth (due to the concentration of immigration in these larger places) on the commercial structure of metropolitan areas; and, on the other hand, the consequences of market stagnation (in much of the Maritime Provinces, Saskatchewan, and Manitoba) on service provision. In the paper on the health of commercial nucleations, Jones turns specifically to matters that are of special concern to the planning of commercial structure within a metropolis. Two measures of ‘commercial health’ are used to examine change: vacancy rates, and turnover rates. By examining both spatial and temporal variations in these attributes across an entire urban system, it is possible to examine the variability and volatility of an urban retail/commercial landscape across a number of dimensions, and provide insights into a variety of social and economic issues. These include the health of local neighborhoods, the impact of new retail formats on existing commercial nodes, and the relative performance of different commercial real estate assets and classes. The paper by Doucet examines the ways in which changes in the supply-side, in this case the changing role of traditional department stores in the consumer service sector, are
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M. Yeates / Progress in Planning 60 (2003) 1–11
reflected spatially in differing imprints of mergers and acquisitions. Doucet indicates that while histories of department stores are quite numerous, there is a dearth of research into the spatial ebb-and-flow of department store networks at either national or local levels. This stands in stark contrast to the numerous detailed analyses of the spatial spread of supermarket chains and systems. Given the recent rapid growth of discount department store chains in Canada, such as Wal-Mart and Zellers, his study of the ‘creative destruction’ waged on the traditional department store networks by these newer channels is both timely and instructive. E-commerce has been the buzz during the past decade. Given the newness of the business to commercial (B2C) supply-side channel, and the fact that most estimates of its impact on ‘brick and mortar’ commercial channels, have been based on, at best, inadequate private surveys, and, at worst, guesses, the paper by Michalak and Calder is a bench-mark study. It is based on analyses of the annual surveys of e-commerce now undertaken by Statistics Canada, which have been piggy-backed on its Labour Force Survey since 1997. The paper contributes not only to the scant body of acceptable sample survey data on the subject, but also draws attention to the ways in which this new supplyside channel may be influencing consumer behaviour (particularly with the youth market) and the development of multi-channel retailing. The last paper provides a framework for a geomatics ‘conversation’ that tries to draw together the threads of the supply-side changes that have been discussed in the previous papers. The conversation focuses on the ways in which supply-side innovations are impacting the infrastructure of the consumer service industry in the GTA, and it provides a conjectural framework for analyzing the future. This conjectural framework, couched numerically in the form of an infrastructure impact calculator, provides a basis for examining the broad implications of various scenarios. It is a speculation procedure, combining both demand and supply-side information, that is proving useful to those in asset management and municipal planning.
References Allinson, J. (Ed.), 1998. GIS in Practice, Royal Town Planning Institute, London. Amrhein, C., Reynolds, H., 1997. Using the Getis statistic to explore aggregation effects in metropolitan Toronto census data. The Canadian Geographer 41 (2), 137–149. Bailey, T., Gatrell, A., 1995. Interactive Spatial Analysis, Longman, London. Baray, J., Cliquet, G., 2002. Retail store location: an application of signal processing with the p-median model. In: Tynan, A.C., (Ed.), Academy of Marketing: Proceedings of the Annual Conference (on CD), School of Business, University of Nottingham, UK, p. 15. Bernhardsen, T., 2000. Geographic Information Systems: An Introduction, Wiley, New York. Boots, B., Okabe, A., Thomas, R. (Eds.), 2002. Modeling Geographical Systems: Statistical and Computational Applications, Kluwer, Dordrecht. Buckner, R.W., 1998. Site Selection: New Advancements in Methods and Technology, Lebhar-Friedman, New York. Clarke, G., Langley, R., Cardwell, W., 1998. Empirical applications of dynamic spatial interaction models. Computers, Environment and Urban Systems 22, 157–184. Fotheringham, A.S., Charlton, M.E., Brunsdon, C., 1998. Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis. Environment and Planning A 30, 1905– 1927. Fotheringham, A.S., Brunsdon, C., Charlton, M.E., 2000. Quantitative Geography, Sage, London.
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Getis, A., 1991. Spatial interaction and spatial autocorrelation: a cross-product approach. Environment and Planning A 23, 1269–1278. Getis, A., Ord, J.K., 1992. The analysis of spatial association by use of distance statistics. Geographical Analysis 24, 189– 206. Gilfoyle, I., Thorpe, P., 2002. Geographic Information Management in Local Government, Taylor and Francis, London. Griffith, D.A., Layne, L.J., 1999. A Casebook for Spatial Statistical Analysis, OUP, New York, pp. 3– 33. Huff, D., 2000. Market Area Planner: User Guide, MPSI Systems Inc, Tulsa. King, G., 1997. A Solution to the Ecological Problem: Reconstructing Individual Behavior from Aggregate Data, Princeton UP, New Jersy. Lea, A.C., Menger, G.L., 1991. An overview of formal methods for retail site evaluation and sales forecasting, part 3: location-allocation models. The Operational Geographer 9 (1), 17–27. Longley, P., Goodchild, M., Maguire, M., Rhind, D., 1999. Geographical Information Systems: Principles, Techniques, Applications and Management, vol. 1 and 2. Wiley, New York. Schumpeter, J.A., 1934. The Theory of Economic Development: An Inquiry into Profits, Capital, Credit, Interest, and the Business Cycle, Harvard University, Cambridge, translated by Redvers Opie. Vlachopoulou, M., Silleos, G., Manthou, V., 2001. Geographical information systems in warehouse site selection decisions. International Journal of Production Economics 71, 205– 212. de Vries, J.J., Nijkamp, P., Rietveld, P., 2001. Alonso’s theory of movements: developments in spatial interaction modeling. Journal of Geographical Systems 3, 233–256. Wrigley, N., 1985. Categorical Data Analysis for Geographers and Environmental Scientists, Blackburn Press, London. Yeates, M., 2001. Business/retail geomatics: a developing field. The Canadian Journal of Regional Science 24 (3), 375–386. Yeates, M., Jones, K., Hernandez, T., 2002. Business geomatics in Canada: a marketing perspective. In: Tynan, A.C., et al. (Eds.), Academy of Marketing: Proceedings of the Annual Conference (on CD), School of Business, University of Nottingham, UK, pp. 2–5. Zhang, J., Goodchild, M., 2002. Uncertainty in Geographical Information, Taylor and Francis, London.
Editorial
Business/commercial geomatics and planning Maurice Yeates School of Graduate Studies, Ryerson University, 350 Victoria St., Toronto, Canada M5B 2K3
The geomatics industry has a long tradition in Canada spanning over a century—for example, the Canadian Institute of Geomatics was founded in 1882. More recently, the Canadian government has been investing in: geomatics research, with the Geomatics for Informed Decisions (GEOIDE) university based research network (www.geoide.ulaval. ca); geomatics training, with its Geomatics Professional Development Program; and, the dissemination of spatial data through the internet (e.g. investing CDN$60 million in a GeoConnections web-based initiative). Geomatics is now one of Canada’s fastest growing areas in the information/knowledge-based economy. Geomatics has traditionally focused on applications in the ‘physical’ sphere, such as, environmental management, land reform, development planning, infrastructure management, natural resource monitoring and development, and coastal zone management and mapping. Current initiatives, particularly through GEOIDE, involve encouragement of geomatics capabilities and applications in the business sector, particularly with respect to commercial activities (Yeates et al., 2002). Geomatics has emerged from a number of well established and new areas of research and professional application—surveying and mapping; geodesy; photogrammetry; remote sensing; geographic information systems/science (GIS); and, new techniques in global positioning systems (GPS), which lie broadly at the intellectual intersection of geodesy and navigation (Longley et al., 1999; Bernhardsen, 2000). Two features that this confluence of interests have in common are that: they are concerned with information that has spatial properties and can therefore be geo-referenced in some way, usually with global coordinates (latitude and longitude); and, their utility has undergone a renaissance since 1990 due to the rapid development of computing and visualization technologies (usually through stand-alone or networked PCs). Thus, areas that were once the interest of a mathematically and technically oriented few are now more accessible. With this greater accessibility, applications have become more widespread and have diffused rapidly in public sector related planning (Allinson, 1998; Gilfoyle and Thorpe, 2002). The diffusion has been less rapid in the private commercial sector, even though much decision-making is in the context of spatially distributed information. E-mail address: [email protected] (M. Yeates). 0305-9006/03/$ - see front matter q 2003 Elsevier Science Ltd. All rights reserved. doi:10.1016/S0305-9006(02)00088-0
2
M. Yeates / Progress in Planning 60 (2003) 1–11
The information involved in business/commercial geomatics relates to the consumer service sector of the economy. In the Canadian situation, this sector includes retail activities, personal services, consumer related FIRE services (that is, retail aspects of finance, insurance, and real estate), restaurants, and entertainment facilities. This broad range of activities provide jobs for about 5.3 million people, or about 43% of the labour force, working in 1.2 million locations (Yeates, 2001). As it is individuals and household units that provide the demand (market), and commercial enterprises, or units (storesoutlets) that provide the supply, and each of these has a spatial location, it is vitally important that analytical work be undertaken at the highest level of geo-referenced aggregation possible—ideally involving unit record data relating to individuals or households.
M. Yeates / Progress in Planning 60 (2003) 1–11
3
CHAPTER 1 Business/commercial geomatics: the demand side An outline of the array of information that is available on the demand side for researchers in business/commercial geomatics is presented in Fig. 1. Much of the information is obtained from Statistics Canada, particularly that collected in the decennial census and the quinquennial censuses related to persons, households, and businesses; and, in the annual (for example, firms), monthly (for example, labour force), and special (for example, household expenditures) surveys. Though this data may, as is indicated in Fig. 1, be collected as unit records, that is, for individuals, or household units, confidentiality requirements impose aggregation on dissemination of the information—either aspatially (such as by income band, or economic sector), or spatially. In consequence, Statistics Canada has generated its own census geography which ranges from, for example, dissemination areas (high level of aggregation or resolution), frequently referred to as small area data, to provinces (low level of aggregation or resolution). Another major source of demand-side information is proprietary data, collected by individual companies, often at point-of-sale, or through sample surveys that are usually undertaken by companies involved with micro-marketing. This information is frequently aggregated to at least postal codes, and often lower (such as CMAs), for use by companies purchasing the information. To enhance usefulness, some commercial providers purchase high-resolution socio-economic data from Statistics Canada, integrate this with proprietary information from the company with which they are working, and reformat the information to postal codes. The transmutation of much of the data from census geography to postal code geography requires numerous apportionment assumptions.
Fig. 1. The demand-side.
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M. Yeates / Progress in Planning 60 (2003) 1–11
The central objective is to generate the data on as small an area basis as possible, that is, six digit (FSA/LDU—forward sortation area and local delivery unit). The most important analytical issues, however, that arises in business/commercial geomatics are: to what extent can inferences about individuals, households, or businesses (high level) be made from models based on information aggregated to some lower level? This issue is referred to colloquially as the ecological fallacy, and technically as the modifiable areal unit problem (MAUP), and is essentially unresolved (Fotheringham et al., 2000; Amrhein and Reynolds, 1997). The MAUP problem involves two questions: (1) can a diagnostic tool be developed that will indicate the extent of aggregation effects embedded in data sets? and, (2) is there a way of ‘solving’ the problem (i.e. a way of achieving, for example, stability in means, variances, and so forth, as data is aggregated)? The answer to the first question is that diagnostic tools are being developed (Getis, 1991; Getis and Ord, 1992; Boots et al., 2002). The answer to the second is that while there may be approaches to addressing sectoral aggregation issues (King, 1997), spatial aggregation issues are less tractable (Fotheringham et al., 2000).
M. Yeates / Progress in Planning 60 (2003) 1–11
5
CHAPTER 2 Business/commercial geomatics: the supply-side Supply-side data is less readily available in Canada for business/commercial geomatics studies than demand-side data. The unit record equivalent on the demandside would be data pertaining to each individual business location. Unfortunately, Statistics Canada does not provide an array of information about businesses at the small area level analogous to that which is provided about individuals and households. The information that is collected is usually disseminated in a highly aggregated fashion (invariably provincially), and even the best high resolution data, such as the small area retail trade estimates (SARTRE), involve a large number of suppressions to ensure confidentiality. Thus, business/commercial geomatics research that focuses on the supply-side has to involve data compiled from a variety of sources—telephone directories, business directories, assessment rolls, and field work—the analysis and linking of which requires considerable human and computing resources. The Centre for the Study of Commercial Activity (CSCA) has focused on the compilation of supply-side relational database warehouses for a number of time periods to provide information analogous to that more readily available for the demand-side (Fig. 2). It is ‘relational’ because different sources and aggregations that comprise the warehouses can be linked through both spatial and non-spatial identifiers, such as point identifications (latitude and longitude) and/or North American Industry Classification (NAIC) codes, to generate the types of data files appropriate to the analysis being undertaken. For example, a comparative analysis of changing vacancy rates in malls and retail strips requires aggregation of the unit
Fig. 2. The supply-side.
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M. Yeates / Progress in Planning 60 (2003) 1–11
record data to malls and streets. Thus, it is possible to examine the spatial implications of the changing role of different supply-side channels (downtowns, retail strips, malls, power centres, and latterly B to C e-commerce) in various parts of the country or within particular urban areas. As B to C e-commerce does not involve ‘bricks and mortar’ at market locations in the same way as the other channels, it is represented in Fig. 2 in more diffuse lettering.
M. Yeates / Progress in Planning 60 (2003) 1–11
7
CHAPTER 3 Spatial decision-support systems The demand and supply-side databases thus combine to form an interrelated data warehouse that can be utilized to provide decision support systems in a variety of private and public sector contexts. The support system (Fig. 3) normally involves four highly inter-related activities: (i)
Acquisition of geo-referenced information, usually from a variety of image and nonimage sources, and often involving different methods of collection. In business/ commercial geomatics, non-image sources dominate, though just-in-time logistical systems (e.g. regional warehouses serving a network of stores) are increasingly using satellite-based GPS tools for precise tracking of transport vehicles. (ii) The transformation, fusion, and management of data in large, three-dimensional (geo-referenced, temporal, measured) relational data warehouses (Fig. 4). As much of the data may be heterogeneous, and also involve different types of spatial units and inconsistent time periods, combination and integration can result in ‘new data’. This ‘new data’ isolates the essence of the spatial and temporal variations, and hence the uncertainties, inherent in the ‘old’ (Zhang and Goodchild, 2002). (iii) Analysis and spatial modeling to test theories and facilitate spatial forecasting. Spatial analytic tools (Bailey and Gatrell, 1995; Griffith and Layne, 1999) are being incorporated more into business/commercial geomatics. Descriptive tools for spatial
Fig. 3. A spatial decision support system.
8
M. Yeates / Progress in Planning 60 (2003) 1–11
Fig. 4. A relational database in business/commercial geomatics.
data, ranging from nearest neighbor and point pattern analysis various to techniques for ascertaining spatial autocorrelation and clustering are now fairly common (Boots et al., 2002). Dynamic forecasting, invariably involving spatial interaction and multivariate regression models (de Vries et al., 2001), have been the traditional tools of market area and site location analysis for some time (Huff, 2000). But, increasingly, spatial regression, geographically weighted regression (Fotheringham et al., 1998), categorical data analysis (Wrigley, 1985), and linear programming (Lea and Menger, 1991; Baray and Cliquet, 2002), are being incorporated into spatial modeling and forecasting exercises. (iv) Analysis of planning and policy implications. The important role for geomatics, especially following the model building exercises, is to provide various scenarios based on various assumptions provided by the decision-makers (or anticipated by the spatial analyst). In essence, the models identify the underlying ‘drivers’ of the changing business/commercial environment (such as store sales in a national chain), and different assumptions concerning the ‘drivers’ provide a variety of forecasts (Clarke et al., 1998; Vlachopoulou et al., 2001). Decision-makers then focus on those ‘drivers’ that can be changed or altered by the company (Buckner, 1998).
M. Yeates / Progress in Planning 60 (2003) 1–11
9
CHAPTER 4 The collection The papers in this collection contribute in their various ways to the public/private planning support system. The paper by Gomez-Insuasti on the spatial structure of the Canadian commercial sector, highlights the ways in which new forms of supply-side segmentation, such as ‘malls, big-box, and flagship formats’, are coming to dominate certain aspects of the economy, particularly in larger metropolitan areas and the western Provinces. This paper thus contributes to impact analysis and asset management—the ‘creative destruction’ (Schumpeter, 1934) of new supply-side formats affects urban and regional planning and the optimization of asset mix. Hernandez’s paper is unusual in commercial analysis—it is a single industry study that focuses not only on aggregated national trends, but local spatial manifestations of growth and change. The industry is the home improvements sector—which in 2001 generated sales of over $22 billion, accounting for 7% of total retail sales in Canada, and involved more than 5500 home improvement chain stores. The sector has undergone significant growth and structural transformation over the last decade due to: burgeoning housing starts which have been at historic highs for a number of years; widespread implementation of new supply-side technologies, especially those associated with big-box retailing; and, consequent high levels of corporate concentration which have impacted negatively on independent retailers, buying groups and retail co-operatives. The study thus addresses issues relating to impact analysis and municipal planning. Simmons and Jones examine the relationship between the location of the supply-side and the market served. While the magnitude and range of the supply-side in large markets is quite predictable, small markets display considerable variation due to differences in levels of centrality, per capita income, and local specialization. Nevertheless, market growth is, in general, the major influence on growth in supply-side employment and business activities. The findings in this study contribute particularly to regional planning, for public policy in Canada has to focus on the one hand on the consequences of demand driven growth (due to the concentration of immigration in these larger places) on the commercial structure of metropolitan areas; and, on the other hand, the consequences of market stagnation (in much of the Maritime Provinces, Saskatchewan, and Manitoba) on service provision. In the paper on the health of commercial nucleations, Jones turns specifically to matters that are of special concern to the planning of commercial structure within a metropolis. Two measures of ‘commercial health’ are used to examine change: vacancy rates, and turnover rates. By examining both spatial and temporal variations in these attributes across an entire urban system, it is possible to examine the variability and volatility of an urban retail/commercial landscape across a number of dimensions, and provide insights into a variety of social and economic issues. These include the health of local neighborhoods, the impact of new retail formats on existing commercial nodes, and the relative performance of different commercial real estate assets and classes. The paper by Doucet examines the ways in which changes in the supply-side, in this case the changing role of traditional department stores in the consumer service sector, are
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reflected spatially in differing imprints of mergers and acquisitions. Doucet indicates that while histories of department stores are quite numerous, there is a dearth of research into the spatial ebb-and-flow of department store networks at either national or local levels. This stands in stark contrast to the numerous detailed analyses of the spatial spread of supermarket chains and systems. Given the recent rapid growth of discount department store chains in Canada, such as Wal-Mart and Zellers, his study of the ‘creative destruction’ waged on the traditional department store networks by these newer channels is both timely and instructive. E-commerce has been the buzz during the past decade. Given the newness of the business to commercial (B2C) supply-side channel, and the fact that most estimates of its impact on ‘brick and mortar’ commercial channels, have been based on, at best, inadequate private surveys, and, at worst, guesses, the paper by Michalak and Calder is a bench-mark study. It is based on analyses of the annual surveys of e-commerce now undertaken by Statistics Canada, which have been piggy-backed on its Labour Force Survey since 1997. The paper contributes not only to the scant body of acceptable sample survey data on the subject, but also draws attention to the ways in which this new supplyside channel may be influencing consumer behaviour (particularly with the youth market) and the development of multi-channel retailing. The last paper provides a framework for a geomatics ‘conversation’ that tries to draw together the threads of the supply-side changes that have been discussed in the previous papers. The conversation focuses on the ways in which supply-side innovations are impacting the infrastructure of the consumer service industry in the GTA, and it provides a conjectural framework for analyzing the future. This conjectural framework, couched numerically in the form of an infrastructure impact calculator, provides a basis for examining the broad implications of various scenarios. It is a speculation procedure, combining both demand and supply-side information, that is proving useful to those in asset management and municipal planning.
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