Quantitative Methods and Applications in GIS - Chapter 4

Part II Basic Quantitative Methods and Applications 4 GIS-Based Trade Area Analysis and Applications in Business Geography and Regional Planning “No matter how good its offering, merchandising, or customer service, every retail company still has to contend with three critical elements of success: loca-tion, location, and location” (Taneja, 1999, p. 136). Trade area analysis is a common and important task in the site selection of a retail store. A trade area is simply “the geographic area from which the store draws most of its customers and within which market penetration is highest” (Ghosh and McLafferty, 1987, p. 62). For a new store, the study of proposed trading areas reveals market opportunities with existing competitors (including those in the same chain or franchise) and helps decide on the most desirable location. For an existing store, it can be used to project market potentials and evaluate the performance. In addition, trade area analysis provides many other benefits for a retailer: deter-mining the focus areas for promotional activities, highlighting geographic weak-ness in its customer base, projecting future growth, and others (Berman and Evans, 2001, pp. 293–294). There are several methods for delineating trade areas: the analog method, the proximal area method, and the gravity models. The analog method is non-geographic, and more recently is often implemented by regression analysis. The proximal area method and the gravity models are geographic approaches and can benefit from GIS technologies. The analog and proximal area methods are fairly simple and are discussed in Section 4.1. The gravity models are the focus of this chapter and are covered in detail in Section 4.2. Because of this book’s emphasis on GIS applications, two case studies are presented in Sections 4.3 and 4.4 to illustrate how the two geographic methods (the proximal area method and the gravity models) are implemented in GIS. Case study 4A draws from traditional business geography, but with a fresh angle: instead of the typical retail store analysis, it analyzes the fan bases for two professional baseball teams in Chicago. Case study 4B demonstrates how the techniques of trade area analysis are used beyond retail studies. In this case, the methods are used in delineating hinterlands (influential areas) for major cities in northeast China. Delineation of hinterlands is an important task for regional planning. The chapter is concluded with some remarks in Section 4.5. 55 © 2006 by Taylor & Francis Group, LLC 56 Quantitative Methods and Applications in GIS 4.1 BASIC METHODS FOR TRADE AREA ANALYSIS 4.1.1 ANALOG METHOD AND REGRESSION MODEL The analog method, developed by Applebaum (1966, 1968), is considered the first systematic retail forecasting model founded on empirical data. The model uses an existing store or several stores as analogs to forecast sales in a proposed similar or analogous facility. Applebaum’s original analog method did not use regression analysis. The method uses customer surveys to collect data of sample customers in the analogous stores: their geographic origins, demographic characteristics, and spending habits. The data are then used to determine the levels of market penetration (e.g., number of customers, population, and average spending per capita) at various distances. The result is used to predict future sales in a store located in similar environments. Although the data may be used to plot market penetrations at various distances from a store, the major objective of the analog method is to forecast sales but not to define trade areas geographically. The analog method is easy to implement, but has some major weaknesses. The selection of analog stores requires subjective judgment (Applebaum, 1966, p. 134), and many situational and site characteristics that affect a store’s performance are not considered. A more rigorous approach to advance the classical analog method is the usage of regression models to account for a wide array of factors that influence a store’s performance (Rogers and Green, 1978). A regression model can be written as Y = b0 + b1x1 + b2 x2 +...+ bn xn where Y represents a store’s sales or profits, x’s are explanatory variables, and b’s are the regression coefficients to be estimated. The selection of explanatory variables depends on the type of retail outlets. For example, the analysis on retail banks by Olsen and Lord (1979) included variables measuring trade area characteristics (purchasing power, median household income, homeownership), variables measuring site attractiveness (employment level, retail square footage), and variables measuring level of competition (number of competing banks’ branches, trade area overlap with branch of same bank). Even for the same type of retail stores, regression models can be improved by grouping the stores into different categories and running a model on each category. For example, Davies (1973) classified clothing outlets into two categories (corner-site stores and intermediate-site stores) and found significant differences in the variables affecting sales. For corner-site stores, the top five explanatory variables are floor area, store accessibility, number of branches, urban growth rate, and distance to nearest car park. For intermediate-site stores, the top five explanatory variables are total urban retail expenditure, store accessibility, selling area, floor area, and number of branches. 4.1.2 PROXIMAL AREA METHOD A simple geographic approach for defining trade areas is the proximal area method, which assumes that consumers choose the nearest store among similar outlets (Ghosh © 2006 by Taylor & Francis Group, LLC GIS-Based Trade Area Analysis and Applications in Geography and Planning 57 and McLafferty, 1987, p. 65). This assumption is also found in the classical central place theory (Christaller, 1966; Lösch, 1954). The proximal area method implies that customers only consider travel distance (or travel time as an extension) in their shopping choice, and thus the trade area is simply made of consumers that are closer to the store than any other. Once the proximal area is defined, sales can be forecasted by analyzing the demographic characteristics within the area and surveying their spending habits. The proximal area method can be implemented in GIS by two ways. The first approach is consumers based. It begins with a consumer location and searches for the nearest store among all store locations. The process continues until all consumer locations are covered. At the end, consumers that share the same nearest store constitute the proximal area for that store. In ArcGIS, it is implemented by utilizing the near tool in ArcToolbox. The tool is available by invoking Analysis Tools > Proximity > Near. The second approach is stores based. It constructs Thiessen polygons from the store locations, and the polygon around each store defines the proximal area for that store. The layer of Thiessen polygons may then be overlaid with that of consumers (e.g., a census tract layer with population information) to identify demographic structures within each proximal area.1 In ArcGIS, Thiessen polygons can be gener-ated from a point layer of store locations in ArcInfo coverage format by choosing Coverage Tools > Analysis > Proximity > Thiessen. For example, Figure 4.1a to c show how the Thiessen polygons are constructed from five points. First, five points are scattered in the study area as shown in Figure 4.1a. Second, in Figure 4.1b, lines are drawn to connect points that are near each other, and lines are drawn perpen-dicular to the connection lines at their midpoints. Finally, in Figure 4.1c, the Thiessen polygons are formed by the perpendicular lines. The proximal area method can be easily extended to use network distance or travel time instead of Euclidean distance. The process implemented in both case studies 4A and 4B follows closely the consumers-based approach. The first step is to generate a distance (time) matrix, containing the travel distance (time) between each consumer location and each store (see Chapter 2). The second step is to identify the store within the shortest travel distance (time) from each consumer location. Finally, the informa-tion is joined to the spatial layer of consumers for mapping and further analysis. 4.2 GRAVITY MODELS FOR DELINEATING TRADE AREAS 4.2.1 REILLY’S LAW The proximal area method only considers distance (or time) in defining trade areas. However, consumers may bypass the closest store to patronize stores with better prices, better goods, larger assortments, or a better image. A store in proximity to other shopping and service opportunities may also attract customers farther than an isolated store because of multipurpose shopping behavior. Methods based on the gravity model consider two factors: distances (or time) from and attractions of stores. Reilly’s law of retail gravitation applies the concept of the gravity model to delin-eating trade areas between two stores (Reilly, 1931). The original Reilly’s law was used to define trading areas between two cities. © 2006 by Taylor & Francis Group, LLC 58 Quantitative Methods and Applications in GIS B A C D E (a) B A C D E (b) B A C D E (c) FIGURE 4.1 Constructing Thiessen polygons for five points. Breaking point d1x d2x Store 1 (S1) Store 2 (S2) X d12 FIGURE 4.2 Breaking point by Reilly’s law between two stores. Consider two stores, stores 1 and 2, that are at a distance of d12 from each other (see Figure 4.2). Assume that the attractions for stores 1 and 2 are measured as S1 and S2 (e.g., in square footage of the stores’ selling areas) respectively. The question is to identify the breaking point (BP) that separates trade areas of the two stores. The BP is d1x from store 1 and d2x from store 2, i.e., d1x + d2x = d12 (4.1) By the notion of the gravity model, the retail gravitation by a store is in direct proportion to its attraction and in reverse proportion to the square of distance. © 2006 by Taylor & Francis Group, LLC ... - tailieumienphi.vn