Cities 41 (2014) 54–63
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Cities
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Location analysis of retail stores in Changchun, China: A street centrality perspective ⇑ Fahui Wang a,b, , Chen Chen c, Chunliang Xiu c, Pingyu Zhang b a Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA b Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, Jilin 130102, China c School of Geographical Science, Northeast Normal University, Changchun, Jilin 130024, China article info abstract
Article history: This paper examines the location pattern of various retail stores in Changchun, China. The centrographic Received 20 December 2013 method, the nearest neighbor index and the proximity to CBD are used to provide some baseline analyses Received in revised form 14 May 2014 of their spatial distributions. Major findings are derived from the street centrality indices measured in Accepted 18 May 2014 terms of a node’s closeness, betweenness and straightness on the road network. The kernel density Available online 17 June 2014 estimation (KDE) converts both store locations and centrality values at nodes to one unit (raster pixel) for correlation analysis. Results indicate that street centrality captures location advantage in a city and Keywords: plays a crucial role in shaping the intraurban variation of commercial land use intensity. Specifically, Street centrality specialty stores value various centralities most, followed by department stores, supermarkets, consumer Retail stores Location preference product stores, furniture stores, and construction material stores. Among the stores with correlation Kernel density estimation coefficients above 0.5, specialty stores favor closeness most, department stores and supermarkets prefer Correlation betweenness, and consumer product stores value straightness most. Ó 2014 Elsevier Ltd. All rights reserved.
Introduction From a vendor’s perspective, ‘‘being central’’ is a major principle that can be traced back to the Hotelling’s (1929) classic location ‘‘No matter how good its offering, merchandising, or customer problem of ice-cream shops. The intuitive notion of central ten- service, every retail company still has to contend with three critical dency is further advanced by the space syntax analysis (Hillier & elements of success: location, location, and location’’ (Taneja, 1999, Hanson, 1984) and the complex network science (Barabási, 2002; p. 136). Location analysis is a common and important task in busi- Batty, 2008), specifically various centrality indices based on a ness management (e.g., Ghosh & McLafferty, 1987; Berman & street network. As Hillier, Penn, Hanson, et al. (1993, p.32) state, Evans, 2001; Zentes, Morschett, & Schramm-Klein, 2011), as exem- non-residential economic and service activities in urban neighbor- plified by the profound influence of the Huff (1963; 2003) model in hoods have been found to be ‘‘determined by the structure of the market studies. urban grid itself rather than by the presence of specific attractors While the importance of location for retail stores is no question, or magnets.’’ In other words, it is the configuration of a city’s street the assessment of location is not an easy task. The advancement of network that shapes its economic or social dynamics and structure. Geographic Information Systems (GIS) has helped develop and A primary focus of the space syntax approach is centrality operationalize some quantitative measures of location. This measures derived from the street network and examination of includes various indices of accessibility from the earlier potential their association with various economic activities. model by Hansen (1959) to the gravity-based availability measure There is a rich body of literature examining how urban land use (Weibull, 1976; Joseph & Phillips, 1984), and to the more recent or economic activity patterns are closely associated with various two-step floating catchment area method (2SFCA) and its general- centrality indices. The approach defines a place ‘‘being central’’ ized form (Luo & Wang, 2003; Wang 2012). The accessibility not only in terms of closeness (proximity) to other places as in tra- approach is mainly from a consumer’s perspective in order to ditional geography, but also being ‘‘intermediary, straight... and capture the convenience of a resident reaching or obtaining a critical’’ to others (Porta, Crucitti, & Latora, 2006). Therefore, it is service offered at various facilities or ‘‘attractors’’. a more comprehensive assessment of location. Most recently, Porta et al. (2009) implemented several network-based centrality ⇑ Corresponding author. Tel.: +1 225 578 6629; fax: +1 225 578 4420. indices, termed as the multiple centrality assessment (MCA) E-mail address: [email protected] (F. Wang). model, to capture location advantage of various places and found http://dx.doi.org/10.1016/j.cities.2014.05.005 0264-2751/Ó 2014 Elsevier Ltd. All rights reserved. F. Wang et al. / Cities 41 (2014) 54–63 55 them highly correlated with distributions of commercial and ser- of Commerce (in collaboration with the Changchun Institute of vice activities in an Italian city. The same set of indices is used to Urban Planning) (2011). We expanded the list by using the Baidu explain variation of land use intensity such as population and Map (map.baidu.com), a popular search engine in China similar employment densities (Wang, Antipova, & Porta, 2011). Porta, to the Google Map, to search for stores in Changchun by various Latora, Wang, et al. (2012) analyzed the association of centrality categories. The final data set includes 973 retail stores in the study with a wide range of economic activities and found that the corre- area. Each record contains a store’s name, address and business lations are higher with secondary (e.g., services) than primary (e.g., type. We are aware that the stores obtained by this approach are manufacturing) activities. not an exhaustive list of all retail stores in the study area. The However, according to our knowledge, no prior studies exam- Baidu database only maintains significant stores based on the ined whether various types of retail stores tend to be associated number of times a store being searched for, and thus includes usu- with one centrality measure more than others. In other words, ally large and notable stores. Many more stores are small with a does the location preference differ among various categories of short lifetime and not included in this study. Nevertheless, it is retail stores? Furthermore, there are few case studies from devel- the best approach that is feasible for the research team. There oping countries that apply the analysis of centrality in an intraur- are many classification schemas for retail stores and commercial ban setting. Most studies examine the relationship between outlets (Guy, 1998). This study follows the conventional Chinese centrality of transportation networks and regional development government classification (also adopted by Changchun Bureau of patterns such as in China (e.g., Li & Cai, 2004; Wang, Jin, Mo, & Commerce) with six major categories: specialty stores, department Wang, 2009; Wang, Mo, Wang, & Jin, 2011) and India (Bagler, stores, supermarkets, consumer product stores, furniture stores, and 2008). One exception is the study by Gao, Wang, Gao, and Liu construction material stores. Specialty stores are those specializing (2013) that used street betweenness to explain traffic flows in in a specific range of merchandise and related items such as clothes Qingdao, China, but its focus was not on locations of economic and footwear, electronics, books, pharmacies, spectacles, stationer- activities. There are also some intraurban studies on the location ies, toys, etc. Consumer product stores sell products and commod- patterns of various firms in China, but the case studies are limited ities in retail or wholesale for common household needs such as in scopes such as on high-tech industries (Zhang, Huang, Sun, & produces, meat, seafood, other groceries and general merchants. Wang, 2013), industrial firms in general (Qi, Fang, & Song, 2008) Construction material stores contain steel, pebble, gravel, sand, or retail stores in downtown area (Chai, Shen, & Long, 2007). None concrete, mechanical equipment, and other construction supplies. examined the association with centrality indices. The other three types are self-explanatory. This case study examines the location pattern of various retail The street network is based on a base map used by the stores in Changchun, China. According to the Changchun Bureau Changchun Bureau of Commerce (2011). The locations for retail of Commerce (in collaboration with the Changchun Institute of stores are obtained on the Baidu Map by searching for their Urban Planning (2011), retail stores are classified into six catego- addresses in Chinese one by one, and then input into ArcGIS. Like ries, namely specialty stores, department stores, supermarkets, many cities in China, Changchun does not have a typical central consumer product stores, furniture stores, and construction mate- business district (CBD) as in most Western cities despite a series rial stores. Our emphasis is whether the six types of stores display of public campaigns for planning and building one (http://finan- different location preference from the perspective of street central- ce.ifeng.com/money/roll/20090723/978630.shtml). The place that ity. By doing so, we are not only interested in validating the asso- most closely resembles the characteristics of a CBD is the Renmin ciation of centrality and economic activities, but also strive to Square, which is commonly recognized as the city center of identify the location characteristics of particular stores. Such Changchun with the highest concentration of commercial and knowledge is particularly valuable to guide urban design and plan- office buildings around it. One may also notice the radial road net- ning practice from a neighborhood scale to an entire city, as called work around the Renmin Square (Fig. 2). Identification of the CBD for by urban planners (Karimi, 2012). location helps examine and reference the spatial patterns of retail stores and centrality measures in later sections.
Study area and data sources Data preparation Changchun is the provincial capital of Jilin in northeast China (Fig. 1). Our study area is the contiguously urbanized area of The CBD is an important reference point in analysis of store Changchun. Commonly referred to as Chengqu in Chinese, the area location. For this study, both Euclidean distance and road network is similar to the term ‘‘central city’’ (in contrast to suburbia) in distance from the CBD for each store are computed in ArcGIS. The the West but perhaps a bit beyond to include some highly-urban- former is straightforward by using the ‘‘Near’’ tool in ArcToolbox, ized inner suburbs. This area is selected because of our primary and the latter is obtained by utilizing the Network Analyst module interest in examining retail stores in the urban area. It is composed of ArcGIS. As explained previously, the street network data are of five urban districts (qu) such as Chaoyang, Nanguan, Kuancheng, based in 2011 before the construction of an expressway system, Luyuan and Erdao, or 46 sub-districts (jiedao). According to the most of which is expected to be open in late 2014. Conversations 2010 Census data, it had 2.808-million population and an area of with local taxi drivers indicate that the common wisdom is to 238.35 km2. This does not include four rural counties (Nong’an, follow the shortest path in terms of road length since the major Dehui, Yushu and Jiutai) in the north and Shuangyang District (also factor influencing travel speed is by far the traffic instead of road mostly rural) in the south, all of which are part of the larger levels. Therefore, network distance is a more reasonable measure administrative area of Changchun Municipality. The study area is of spatial impedance than travel time, which is often unpredictable fairly complete with minimal edge effect as the surrounding areas in the study area. are rural with a very different landscape and low population density. For measuring street centralities, this study adopts a primal Edge effect refers to instability or unreliability of conclusions drawn approach that represents intersections as nodes and road segments from a study area when bordering areas are included or excluded. as links with lengths to connect nodes (Jiang & Claramunt, 2004). Hereafter the study area is simply referred to as Changchun. Among various centrality measures (Kuby, Tierney, Roberts, & To build the database of retail stores, we started with an Upchurch, 2005), three are critical and chosen here to measure a inventory of 263 large stores compiled by the Changchun Bureau location being close to all others, being the intermediary between 56 F. Wang et al. / Cities 41 (2014) 54–63
Fig. 1. Location of Changchun and the study area.
Fig. 2. Six types of retail stores in Changchun. others, and being accessible via a virtual straight route to all others. by the shortest paths connecting all pairs of nodes in the network, Namely, they are closeness (C C), betweenness (C B) and straight- defined as: ness (C S). C XN Closeness centrality C measures how close a node is to all the B 1 njkðiÞ C C ¼ other nodes along the shortest paths of the network. C for a node i ðN 1ÞðN 2Þ n j¼1;k¼1;j–k–i jk i is defined as:
C N 1 where n is the number of shortest paths between nodes j and k, C ¼ P jk i N and n (i) is the number of these shortest paths through node i. CB j¼1;j–idij jk captures a special property for a place: it does not act as an origin where N is the total number of nodes in the network, and dij is the or a destination for trips, but as a pass-through point. Straightness shortest distance between nodes i and j. In other words, C C is the centrality CS measures how much the shortest paths from a node inverse of average distance from this node to all other nodes. to all others deviate from the virtual straight lines (Euclidean Betweenness centrality CB measures how often a node is traversed distances) connecting them. CS is measured as: F. Wang et al. / Cities 41 (2014) 54–63 57
Eucl 1 XN d CS ¼ ij i N 1 Ed j¼1;j–i ij
Eucl S where dij is the Euclidean distance between nodes i and j. Ci mea- sures the extent to which a place can be reached directly, like on a straight line, from all other places in a city. It is a quality that makes it prominent in terms of ‘‘legibility’’ and ‘‘presence’’ (Conroy-Dalton, 2003). We use the Urban Network Analysis tool by Sevtsuk, Mekonnen, and Kalvo (2013) to implement the computation of the centrality indices. Specifically, the tool is installed as an added toolset under ArcToolbox, and the tool reads the street network dataset prepared by the ArcGIS Network and calculates the centrality indices as out- put. Fig. 3a–c shows the spatial distributions of closeness CC, betweenness CB and global straightness CS. On the maps, the centrality value for an edge (street segment) is computed as the average of its two end nodes. A larger value of each centrality index indicates a location more central and thus more advantageous. The pattern of closeness is largely concentric with the highest value around the CBD and declining gradually outwards, but the patterns of betweenness and straightness are far from clear. Finally, the kernel density estimation (KDE) method is used to transform the store locations and the street centrality values in dif- ferent scales to the same data framework (i.e., a raster system) so that the relationship between them can be assessed at the same scale (Wang, Antipova et al., 2011). The KDE estimates the density at each location as the average value within a spatial window around it by weighing nearby objects more than far ones based on a kernel function (e.g., Fotheringham, Brunsdon, & Charlton, 2000, pp.146–149). This study uses the built-in KDE tool in ArcGIS, which adopts the popular quartic function (Silverman, 1986). By employing a bandwidth of 1500 m and a cell size of 100 m 100 m, the raster layer covers a rectangular area consist- ing of 214,368 grid cells (462 rows by 464 columns) for the study area. In essence, the KDE uses the density (or average attributes) of nearby objects to represent the property at the middle location. By doing so, it converts a point layer of discrete events (store locations or nodes on the street network) to a continuous surface (i.e., ras- ter). Corresponding to three centrality measures, six types of stores plus a layer of all stores combined, we have a total of 10 raster layers by the KDE method. Fig. 4a–c shows the KDE of closeness, betweenness and straightness, respectively. Among the seven raster layers for stores, only the KDE of specialty stores is presented (Fig. 4d) as an example. The KDE value of a centrality index captures the value of a location as influenced by its surroundings, and the KDE value of a type of stores represents the relative concentration of stores around that place.
Geographic distribution patterns of retail stores
This section provides an exploratory analysis of spatial patterns of the retail stores in Changchun. Does one type of stores tend to be more clustered or dispersed around its center than other types? Do they trend in a particular direction? Is a clustering or dispersing pattern statistically significant or otherwise random? These issues are analyzed by the centrographic method and the nearest neigh- Fig. 3. Street centrality values: (a) closeness, (b) betweenness, and (c) straightness. bor method. The centrographic analysis begins with two basic centrographic methods: ‘‘mean center’’ and ‘‘standard distance.’’ The mean center distance, i.e., the stores are most spread out along the elongated can be considered the average location of stores, and the standard axis than any other directions. Here, the ‘‘Directional Distribution distance is a radius equal to one standard deviation of the distances (Standard Deviational Ellipse)’’ tool in ArcGIS is used to examine of all stores from the mean center, and thus measures the spatial the distribution patterns of store types. spread of their distribution. The ellipse method further advances Fig. 5 shows the six ellipses (one for each type of stores). Among the concepts of mean center and standard distance by identifying the six, the distribution of specialty stores is most compact around the directional variation of spatial spread captured by the standard their center, which is slightly south of the CBD. The distribution 58 F. Wang et al. / Cities 41 (2014) 54–63
Fig. 4. KDE values of (a) closeness, (b) betweenness, (c) straightness, and (d) specialty stores.
Fig. 5. Mean centers and ellipses of six types of retail stores.
patterns of supermarkets and department stores are similar to that stores are more likely to benefit from being near each other around of specialty stores but trend more toward southwest. All these the downtown than other stores in order to attract customers three types of store are mainly elongated along the north–south by their collective offering of diverse and highly specialized direction. Consumer product stores are more tilted along the commodities. Other stores are more concerned of competition for NE-SW direction. Construction material stores and furniture stores patronage of customers and tend to be more dispersed. In tend to locate more in the east than any other types of stores, and summary, these patterns are merely descriptive and reveal limited mainly along the east–west direction. That is to say, specialty information of stores’ location preference. F. Wang et al. / Cities 41 (2014) 54–63 59
Table 1 the reference type and coded as X1 = X2 = X3 = X4 = X5 = 0. Stores Results of average nearest neighbor analysis of stores. of any other type are coded by assigning a value ‘‘1’’ to one of
Store type No. Expected Observed Z-score the dummy variables and ‘‘0’’ to the rest four (e.g., X1 = 1 and distance (m) distance (m) X2 = X3 = X4 = X5 = 0 for department stores; X2 = 1 and X1 = X3 = b Specialty store 323 459.69 245.384 16.00 X4 = X5 = 0 for supermarkets; and so on). Denoting ‘‘distance’’ as Construction material 127 674.112 320.616 11.31b the dependent variable Y, the model is written as Consumer product 166 584.044 349.468 9.90b b Department store 63 800.331 405.418 7.49 Y ¼ b0 þ b1X1 þ b2X2 þ b3X3 þ b4X4 þ b5X5 ð1Þ Supermarket 212 503.751 407.229 5.34b Furniture store 82 738.407 572.096 3.90a In Eq. (1), the intercept b0 is the average distance for the refer- ence category (i.e., specialty stores), coefficient b is the difference All stores 973 270.567 153.548 25.80b i in average distances between the stores of a category coded Xi =1 a Significant at 0.01. and the stores of the reference category, and corresponding b Significant at 0.001. t-values indicate whether the differences are statistically signifi- cant. The results are reported in Table 2 (the 2nd and 3rd columns). For example, when measured in Euclidean distance, the average The nearest neighbor index measures the distance between each for specialty stores is 3476.8 m, for department stores is store and its nearest store, and then calculates the average 3476.8 + 462.3 = 3939.1 m, for supermarkets is 3476.8 + 659.9 = (Mitchell, 2005). A smaller average distance (i.e., ‘‘observed dis- 4136.7 m and so on, which are consistent with Fig. 6a. More impor- tance’’) in comparison with a random distribution of the same tantly, the corresponding t-values indicate that the differences number of stores (i.e., ‘‘expected distance’’) indicates a clustered between the reference category (specialty stores) and any types pattern, and a larger average distance represents a dispersed pat- of stores are statistically significant except for department stores. tern. The term ‘‘clustered or dispersed’’ used here refers to whether The F value (=9.21) indicates that overall the model is significant. individual stores are within proximity or far apart from each other, Note that the store types are sorted in Table 2 in an increasing whereas the term in the centrographic method implies how far the order in average distance. In summary, either by Euclidean or stores spread across their mean center. Moreover, the nearest network distance, specialty stores and department stores (with neighbor method reports a Z score to indicate whether the no significant difference between them) are closest to the CBD, identified pattern is statistically significant, and thus is considered followed by supermarkets, consumer product stores, furniture inferential spatial statistics whereas the centrographic method is stores, and construction material stores. descriptive spatial statistics. Fig. 6b–d shows the variations of average centrality values Table 1 summarizes the results. The stores altogether (without (closeness, betweenness and straightness, respectively) across differentiation of store types) are highly clustered and far from store types. The centrality values of each store are extracted from random (the result in the bottom row in Table 1). When analysis the KDE layers by identifying the raster cell where a store falls in. is conducted by store type, each of the six store types is clustered, Similarly, the regression model in Eq. (1) is applied to the three and the clustered pattern is statistically significant at 0.01. The centrality indices to test whether the KDE centrality values at store types are ordered in Table 1 according to their Z-scores from store locations differ significantly across the six store types. The the most significant to the least. It is conceivable that the top three regression results are also presented in Table 2 (the three col- types of stores (specialty stores, construction material markets, umns on the right). Recall that a higher centrality value implies and consumer product stores) may benefit from stores of the same a more favorable (central) location. In the model for closeness, type being close to each other as consumers tend to visit multiple the average centrality value for ‘‘specialty stores’’ is the intercept stores before purchases. Department stores also have a tendency of (0.000180). This value is not significantly different from ‘‘depart- being near each other, but not as strong as the first three. ment stores.’’ However, it drops by 0.000063 for ‘‘supermarkets’’, Supermarkets and furniture stores are clustered with the lowest by 0.000058 for ‘‘consumer products’’ and so on. The result for levels of statistical significance. This reflects that these two types closeness indicates that specialty stores and department stores of stores (particularly the large ones) may be more mindful of (with no significant difference between them) have the best cen- avoiding competition from their rival store than taking advantage trality, followed by consumer product stores, supermarkets, furni- of customers’ multi-store selective shopping behavior. ture stores, and construction material stores. The order is largely consistent with those for Euclidean and network distances from Analysis of store locations by store type the CBD with only consumer product stores and supermarkets switching their positions. That is to say, proximity to CBD is a This section examines whether stores differ significantly in good proxy for the closeness index in capturing a location being their location choices across store types. We compare the distances close to others. The result for straightness is very similar to that of stores from the CBD by store types as the baseline, and focus on for closeness with the same order of store types in centrality val- differences in their street centrality values. In other words, being ues. The result for betweenness is consistent with those on close- central is first measured simply by the proximity to the CBD, and ness and straightness at the two ends of the ranking, i.e., specialty then by the three comprehensive centrality indices. stores and department stores (again with no significant difference Both Euclidean and street network distances are used to assess between them) still lead the pack with the best centrality and each store’s proximity to the CBD. As shown in Fig. 6a, the order of construction material stores remain the last. However, it differs store types from the shortest to the longest average Euclidean dis- slightly in the middle, i.e., the average value is higher for tance is consistent with that of store types measured in street net- furniture stores than for supermarkets, which is higher than for work distances. Are differences in average distances statistically consumer product stores. significant across store types? This may be answered by conduct- In summary, based on Fig. 6a–d and Table 2, it is clear that ing the ANOVA (analysis of variance) test. Here a regression model specialty stores and departments enjoy the best location and the is introduced for the same purpose for its simplicity and easy construction material stores are left with the worst in either interpretation (Gu, Wang, & Liu, 2005). Five dummy variables can proximity to CBD or any centrality measure. Among the three be used to code the six store types. Since specialty stores have remaining store types, furniture stores are ranked the last in prox- the shortest average distance from the CBD, they are selected as imity to CBD, closeness or straightness and only ahead of the other 60 F. Wang et al. / Cities 41 (2014) 54–63
Fig. 6. Average values of location by store types: (a) distances from CBD, (b) closeness, (c) betweenness, (d) straightness.
Table 2 Regression results on differences in location across store types (n = 973).
Store type Euclidean distance from CBD (m) Network distance from CBD (m) Closeness Betweenness Straightness Specialty store 3476.8 (27.04)b 4164.8 (26.44)b 0.000180 (29.42)b 10.0555 (28.47)b 0.2427 (32.13)b Department store 462.3 (1.45) 571.7 (1.47) 0.000012 ( 0.82) 0.1925 ( 0.22) 0.0179 ( 0.96) Supermarket 659.9 (3.23)a 832.1 (3.32)b 0.000063 ( 6.44)b 2.8586 ( 5.09)b 0.0784 ( 6.52)b Consumer product 986.5 (4.47)b 1160.6 (4.29)b 0.000058 ( 5.53)b 3.4588 ( 5.71)b 0.0685 ( 5.29)b Furniture store 1004.2 (3.51)b 1176.4 (3.36)b 0.000066 ( 4.87)b 2.8160 ( 3.59)b 0.0809 ( 4.82)b Construction material 1440.8 (5.95)b 1671.1 (5.64)b 0.000103 ( 8.97)b 5.1495 ( 7.75)b 0.1238 ( 8.71)b F value 9.21b 8.37 21.21 16.40 20.20
Note: Value for specialty stores is intercept b0 in Eq. (1) and thus the average value for specialty store, value for other stores is coefficient bi (i = 1, 2, ..., 5) in Eq. (1) and thus the difference between that type of stores and specialty stores; t-values in parentheses. a Significant at 0.01. b Significant at 0.001. two types in betweenness; when compared to consumer product (density) of stores and centrality values across the whole study stores, supermarkets are closer to CBD and have better between- area (and thus perhaps reveals their linkage potential). It is imple- ness, but lower closeness and straightness values. This is an impor- mented by using the Band Collection Statistics tool (specifically, tant baseline for the correlation analysis in the next section, where the option ‘‘Compute covariance and correlation matrices’’) in we will offer more discussion on location selection behavior by dif- ArcGIS. The results are reported on the left side in Table 3. The ferent stores. second correlation analysis is applied to the extracted KDE values of stores and centrality indices at only existing store locations. The results are on the right side in Table 3. Correlation of street centrality and retail stores First of all, the stores in general (without differentiation by store types) are highly correlated with each centrality index (with The previous section discussed how different stores differ in all correlation coefficient above 0.82), and the correlation strength location by proximity to the CBD and street centrality indices. This differs little across centrality indices. That is to say, commercial section takes a step further to examine how the distribution of activities especially retail stores value locations of favorable cen- stores may correlate with the spatial variation of centrality values trality; however, the stores altogether do not necessarily prefer and how the correlation strength may vary between different one type of centrality than others. The former is consistent with store types and various centrality indices. Correlation analysis the finding reported in Porta et al. (2009), and the latter is not as would not be feasible between them without transformation of the same paper suggested a preference of betweenness by com- data frames because the store locations do not match with mercial activities over closeness and straightness in Bologna, Italy. centrality measures attached to the nodes of street network. As Does a type of stores prefer one particular centrality over other explained in section ‘Data preparation’, the KDE method is used centralities? This needs to be addressed by analysis of the correla- to convert both to the same raster data frame to facilitate the tions by store types, i.e., a focus of this paper. correlation analysis. Secondly, the correlations between the centrality indices and Two types of analysis are conducted: one is based on all raster store densities are all statistically significant for all store types. cells,andtheotherisonasubsetofthe cells where stores are located. With the exception of furniture stores and construction material The first is to examine correlation between the concentration stores, all correlations are strong with coefficients above 0.5. Note F. Wang et al. / Cities 41 (2014) 54–63 61
Table 3 Pearson’s correlation coefficients of KDE values of stores and centrality indices.
Store type All locations (n = 214,368) Store sites only Closeness Betweenness Straightness Average No. (n) Closeness Betweenness Straightness Average Specialty store 0.849 0.810 0.818 0.826 323 0.858 0.806 0.847 0.837 Department store 0.644 0.646 0.618 0.636 63 0.790 0.886 0.749 0.808 Supermarket 0.695 0.753 0.684 0.711 212 0.557 0.684 0.523 0.588 Consumer product 0.591 0.537 0.596 0.575 166 0.606 0.473 0.644 0.574 Furniture store 0.416 0.455 0.422 0.431 82 0.270 0.244 0.300 0.271 Construction material 0.165 0.200 0.193 0.186 127 0.240 0.247 0.252 0.246 All stores 0.848 0.845 0.833 0.842 973 0.834 0.836 0.829 0.833
Note: all coefficients are significant at 0.001. Number in bold is the highest correlation coefficientfor a store type with one of the three centrality indices. that the results based on all locations are generally consistent with supermarkets, consumer product stores, furniture stores, and con- those based on store sites only. This is particularly true for the four struction material stores. store types with correlation coefficients above 0.5. The consisten- Finally, among the four types of stores with correlation coeffi- cies refer to not only which store types exhibit higher correlation cients above 0.5, specialty stores enjoy the highest correlation with coefficients with centrality than other store types (i.e., specialty closeness; department stores or supermarkets have the highest stores tend to have the highest correlation, and consumer product correlation with betweenness; and consumer product stores corre- stores tend have the lowest), but also which centrality index late best with straightness. exhibits the highest correlation within the same store type (e.g., Would the above findings be affected by the measure scale of for specialty store, its correlation with closeness is the highest). KDE values of either stores or centrality? Denoting stores’ KDE val- Therefore, our following discussion focuses on the results based ues as y and centrality KDE values as x, we experimented with four on store sites. possible forms of correlations such as x vs. y (as reported in Thirdly, the store types in Table 3 are ordered by average corre- Table 3), ln(x) vs. y, x vs. ln(y), and ln(x) vs. ln(y). When the best fit- lation coefficients. The order is the same as the order in Table 2. ting form is not the linear one (x vs. y), the improvements in corre- Recall that Table 2 reports the average centrality values of each lation coefficients are minor, and the above three findings all hold. store type (even distances from CBD could be considered as a prim- Fig. 7a–f shows the best fitting form for each store type. One may itive measure of being central). That is to say, in general, stores recover the correlation coefficient (r) by taking the square root of with better centrality values also have stronger correlations with R2 reported in the figures. Among the four types of stores with centrality indices. Once again, specialty stores generally have the strong correlations with centrality, the order of correlation highest correlation with centrality, followed by department stores, strength remains such as: department stores vs. betweenness,
Fig. 7. Best fitting form for correlation between (a) specialty stores and closeness, (b) department stores and betweenness, (c) supermarkets and betweenness, (d) consumer product stores and straightness, (e) furniture stores and straightness, (f) construction material stores and straightness. 62 F. Wang et al. / Cities 41 (2014) 54–63 specialty stores vs. closeness, supermarkets vs. betweenness, and The most significant findings are based on the street centrality consumer product stores vs. straightness. indices, namely closeness, betweenness and straightness. The set The findings may be summarized and understood along two of centrality indices captures the very essence of location in terms lines: the overall correlation strengths across store types, and the of a place’s accessibility, intermediacy and directness among variation of correlation from one centrality index to another within others. An overall strong correlation between street centralities the same store types. The former is also consistent with our finding and store densities lends support to the interdependence between on the average centrality values reported in Section 5. The central- street network and retail stores in an intraurban setting. The ity value itself and the correlation with it indicate the preference results indicate that stores whose locations have better centrality and competitiveness of a store for location being central. The stores values also have stronger correlations with centrality indices. Both can be clearly grouped into three classes: specialty stores and the average centrality values and correlation coefficients suggest department stores at the top with correlation coefficients at that specialty stores value various centralities most, followed by around 0.8, supermarkets and consumer product stores in the mid- department stores, supermarkets, consumer product stores, furni- dle with most coefficients ranging 0.5–0.7, furniture stores and ture stores, and construction material stores. Among the four store construction material stores at the bottom with coefficients below types with correlation coefficients above 0.5, specialty stores favor 0.3. Specialty stores and department stores usually command the closeness most, department stores and supermarkets prefer highest volume of customers drawn from the widest geographic betweenness to the other two centralities, and consumer product areas. Some major flagship stores in the business center on Chon- stores value straightness most. In other words, while some types gqing Road attract citywide customers. Therefore, they demand for of stores demand higher centralities more so than others, the type the most central locations. Supermarkets and consumer product of centrality favored by stores may vary by store types. The stores also draw a large number of customers. However, most of research sheds light on location behavior by retail stores and helps such stores are highly substitutable and customers are mainly from guide urban planners in the design and allocation of commercial local areas. As a result, they are located in less competitive loca- land use (including the ongoing debate on the planning and site tions. Furniture stores and construction material stores carry selection for Changchun’s CBD). large-size and heavy commodities and consume large lots of land. Customers visit these stores with a low frequency. Therefore, they are usually located on the urban fringes where centrality values are Acknowledgement the lowest. Furthermore, between specialty stores and department stores, We would like to acknowledge the supports: a visiting profes- the former slightly prefers closeness to the other two centralities, sorship at the Northeast Institute of Geography and Agroecology and the latter favors betweenness the most. The closeness central- of Chinese Academy of Sciences in the summer of 2013 (Wang), ity captures total travel cost for customers, which may matter most Grant No. 41071109 from the National Natural Science Foundation for specialty store; and the betweenness centrality reflects the of China (Xiu) and Grant No. 41071108 from the National Natural through traffic volume of customers, which is likely a major factor Science Foundation of China (Zhang). in location choice by department stores (exemplified by many department stores on Chongqing Road, Hongqi Road and Guilin References Road in Changchun). Between supermarkets and consumer prod- uct stores, the former values betweenness most and the latter Bagler, G. (2008). Analysis of the airport network of India as a complex weighted emphasizes straightness. It is conceivable that many customers network. Physica A: Statistical Mechanics and its Applications, 387, 2972–2980. Barabási, A.-L. (2002). Linked: The new science of networks. New York: Plume. stop by supermarkets to shop on their trips for other purposes, Batty, M. (2008). Whither network science? Environment and Planning B: Planning and thus contribute to the value of betweenness for supermarkets. and Design, 35, 569–571. Straightness is an index measuring efficiency of trips visiting a Berman, B., & Evans, J. R. (2001). Retail management: A strategic approach (8th ed.). location or legibility of a place, and apparently a major quality of Upper Saddle River, NJ: Prentice Hall. Chai, Y., Shen, J., & Long, T. (2007). Downtown retail development under location sought by consumer product stores. Due to overall low suburbanization – A case study of Beijing. Chinese Geographical Science, 17, 1–9. correlation coefficients for furniture stores and construction mate- Changchun Bureau of Commerce (in collaboration with the Changchun Institute of rial stores, we refrain from discussing the differences across cen- Urban Planning & Design), 2011. Changchun commercial establishments plan 2011–2020 (draft for soliciting public inputs).
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