Cities 41 (2014) 54–63

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Location analysis of retail stores in Changchun, China: A street centralityperspective

http://dx.doi.org/10.1016/j.cities.2014.05.0050264-2751/� 2014 Elsevier Ltd. All rights reserved.

⇑ Corresponding author. Tel.: +1 225 578 6629; fax: +1 225 578 4420.E-mail address: fwang@lsu.edu (F. Wang).

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, USAb Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, Jilin 130102, Chinac School of Geographical Science, Northeast Normal University, Changchun, Jilin 130024, China

a r t i c l e i n f o

Article history:Received 20 December 2013Received in revised form 14 May 2014Accepted 18 May 2014Available online 17 June 2014

Keywords:Street centralityRetail storesLocation preferenceKernel density estimationCorrelation

a b s t r a c t

This paper examines the location pattern of various retail stores in Changchun, China. The centrographicmethod, the nearest neighbor index and the proximity to CBD are used to provide some baseline analysesof their spatial distributions. Major findings are derived from the street centrality indices measured interms of a node’s closeness, betweenness and straightness on the road network. The kernel densityestimation (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 andplays a crucial role in shaping the intraurban variation of commercial land use intensity. Specifically,specialty stores value various centralities most, followed by department stores, supermarkets, consumerproduct stores, furniture stores, and construction material stores. Among the stores with correlationcoefficients above 0.5, specialty stores favor closeness most, department stores and supermarkets preferbetweenness, and consumer product stores value straightness most.

� 2014 Elsevier Ltd. All rights reserved.

Introduction

‘‘No matter how good its offering, merchandising, or customerservice, every retail company still has to contend with three criticalelements of success: location, location, and location’’ (Taneja, 1999,p. 136). Location analysis is a common and important task in busi-ness management (e.g., Ghosh & McLafferty, 1987; Berman &Evans, 2001; Zentes, Morschett, & Schramm-Klein, 2011), as exem-plified by the profound influence of the Huff (1963; 2003) model inmarket studies.

While the importance of location for retail stores is no question,the assessment of location is not an easy task. The advancement ofGeographic Information Systems (GIS) has helped develop andoperationalize some quantitative measures of location. Thisincludes various indices of accessibility from the earlier potentialmodel by Hansen (1959) to the gravity-based availability measure(Weibull, 1976; Joseph & Phillips, 1984), and to the more recenttwo-step floating catchment area method (2SFCA) and its general-ized form (Luo & Wang, 2003; Wang 2012). The accessibilityapproach is mainly from a consumer’s perspective in order tocapture the convenience of a resident reaching or obtaining aservice offered at various facilities or ‘‘attractors’’.

From a vendor’s perspective, ‘‘being central’’ is a major principlethat can be traced back to the Hotelling’s (1929) classic locationproblem of ice-cream shops. The intuitive notion of central ten-dency is further advanced by the space syntax analysis (Hillier &Hanson, 1984) and the complex network science (Barabási, 2002;Batty, 2008), specifically various centrality indices based on astreet network. As Hillier, Penn, Hanson, et al. (1993, p.32) state,non-residential economic and service activities in urban neighbor-hoods have been found to be ‘‘determined by the structure of theurban grid itself rather than by the presence of specific attractorsor magnets.’’ In other words, it is the configuration of a city’s streetnetwork that shapes its economic or social dynamics and structure.A primary focus of the space syntax approach is centralitymeasures derived from the street network and examination oftheir association with various economic activities.

There is a rich body of literature examining how urban land useor economic activity patterns are closely associated with variouscentrality indices. The approach defines a place ‘‘being central’’not only in terms of closeness (proximity) to other places as in tra-ditional geography, but also being ‘‘intermediary, straight. . . andcritical’’ to others (Porta, Crucitti, & Latora, 2006). Therefore, it isa more comprehensive assessment of location. Most recently,Porta et al. (2009) implemented several network-based centralityindices, termed as the multiple centrality assessment (MCA)model, to capture location advantage of various places and found

F. Wang et al. / Cities 41 (2014) 54–63 55

them highly correlated with distributions of commercial and ser-vice activities in an Italian city. The same set of indices is used toexplain variation of land use intensity such as population andemployment densities (Wang, Antipova, & Porta, 2011). Porta,Latora, Wang, et al. (2012) analyzed the association of centralitywith a wide range of economic activities and found that the corre-lations are higher with secondary (e.g., services) than primary (e.g.,manufacturing) activities.

However, according to our knowledge, no prior studies exam-ined whether various types of retail stores tend to be associatedwith one centrality measure more than others. In other words,does the location preference differ among various categories ofretail stores? Furthermore, there are few case studies from devel-oping countries that apply the analysis of centrality in an intraur-ban setting. Most studies examine the relationship betweencentrality of transportation networks and regional developmentpatterns such as in China (e.g., Li & Cai, 2004; Wang, Jin, Mo, &Wang, 2009; Wang, Mo, Wang, & Jin, 2011) and India (Bagler,2008). One exception is the study by Gao, Wang, Gao, and Liu(2013) that used street betweenness to explain traffic flows inQingdao, China, but its focus was not on locations of economicactivities. There are also some intraurban studies on the locationpatterns of various firms in China, but the case studies are limitedin scopes such as on high-tech industries (Zhang, Huang, Sun, &Wang, 2013), industrial firms in general (Qi, Fang, & Song, 2008)or retail stores in downtown area (Chai, Shen, & Long, 2007). Noneexamined the association with centrality indices.

This case study examines the location pattern of various retailstores in Changchun, China. According to the Changchun Bureauof Commerce (in collaboration with the Changchun Institute ofUrban Planning (2011), retail stores are classified into six catego-ries, namely specialty stores, department stores, supermarkets,consumer product stores, furniture stores, and construction mate-rial stores. Our emphasis is whether the six types of stores displaydifferent location preference from the perspective of street central-ity. By doing so, we are not only interested in validating the asso-ciation of centrality and economic activities, but also strive toidentify the location characteristics of particular stores. Suchknowledge is particularly valuable to guide urban design and plan-ning practice from a neighborhood scale to an entire city, as calledfor by urban planners (Karimi, 2012).

Study area and data sources

Changchun is the provincial capital of Jilin in northeast China(Fig. 1). Our study area is the contiguously urbanized area ofChangchun. Commonly referred to as Chengqu in Chinese, the areais similar to the term ‘‘central city’’ (in contrast to suburbia) inthe West but perhaps a bit beyond to include some highly-urban-ized inner suburbs. This area is selected because of our primaryinterest in examining retail stores in the urban area. It is composedof five urban districts (qu) such as Chaoyang, Nanguan, Kuancheng,Luyuan and Erdao, or 46 sub-districts (jiedao). According to the2010 Census data, it had 2.808-million population and an area of238.35 km2. This does not include four rural counties (Nong’an,Dehui, Yushu and Jiutai) in the north and Shuangyang District (alsomostly rural) in the south, all of which are part of the largeradministrative area of Changchun Municipality. The study area isfairly complete with minimal edge effect as the surrounding areasare rural with a very different landscape and low population density.Edge effect refers to instability or unreliability of conclusions drawnfrom a study area when bordering areas are included or excluded.Hereafter the study area is simply referred to as Changchun.

To build the database of retail stores, we started with aninventory of 263 large stores compiled by the Changchun Bureau

of Commerce (in collaboration with the Changchun Institute ofUrban Planning) (2011). We expanded the list by using the BaiduMap (map.baidu.com), a popular search engine in China similarto the Google Map, to search for stores in Changchun by variouscategories. The final data set includes 973 retail stores in the studyarea. Each record contains a store’s name, address and businesstype. We are aware that the stores obtained by this approach arenot an exhaustive list of all retail stores in the study area. TheBaidu database only maintains significant stores based on thenumber of times a store being searched for, and thus includes usu-ally large and notable stores. Many more stores are small with ashort lifetime and not included in this study. Nevertheless, it isthe best approach that is feasible for the research team. Thereare many classification schemas for retail stores and commercialoutlets (Guy, 1998). This study follows the conventional Chinesegovernment classification (also adopted by Changchun Bureau ofCommerce) with six major categories: specialty stores, departmentstores, supermarkets, consumer product stores, furniture stores, andconstruction material stores. Specialty stores are those specializingin a specific range of merchandise and related items such as clothesand footwear, electronics, books, pharmacies, spectacles, stationer-ies, toys, etc. Consumer product stores sell products and commod-ities in retail or wholesale for common household needs such asproduces, meat, seafood, other groceries and general merchants.Construction material stores contain steel, pebble, gravel, sand,concrete, mechanical equipment, and other construction supplies.The other three types are self-explanatory.

The street network is based on a base map used by theChangchun Bureau of Commerce (2011). The locations for retailstores are obtained on the Baidu Map by searching for theiraddresses in Chinese one by one, and then input into ArcGIS. Likemany cities in China, Changchun does not have a typical centralbusiness district (CBD) as in most Western cities despite a seriesof public campaigns for planning and building one (http://finan-ce.ifeng.com/money/roll/20090723/978630.shtml). The place thatmost closely resembles the characteristics of a CBD is the RenminSquare, which is commonly recognized as the city center ofChangchun with the highest concentration of commercial andoffice buildings around it. One may also notice the radial road net-work around the Renmin Square (Fig. 2). Identification of the CBDlocation helps examine and reference the spatial patterns of retailstores and centrality measures in later sections.

Data preparation

The CBD is an important reference point in analysis of storelocation. For this study, both Euclidean distance and road networkdistance from the CBD for each store are computed in ArcGIS. Theformer is straightforward by using the ‘‘Near’’ tool in ArcToolbox,and the latter is obtained by utilizing the Network Analyst moduleof ArcGIS. As explained previously, the street network data arebased in 2011 before the construction of an expressway system,most of which is expected to be open in late 2014. Conversationswith local taxi drivers indicate that the common wisdom is tofollow the shortest path in terms of road length since the majorfactor influencing travel speed is by far the traffic instead of roadlevels. Therefore, network distance is a more reasonable measureof spatial impedance than travel time, which is often unpredictablein the study area.

For measuring street centralities, this study adopts a primalapproach that represents intersections as nodes and road segmentsas links with lengths to connect nodes (Jiang & Claramunt, 2004).Among various centrality measures (Kuby, Tierney, Roberts, &Upchurch, 2005), three are critical and chosen here to measure alocation being close to all others, being the intermediary between

Fig. 1. Location of Changchun and the study area.

Fig. 2. Six types of retail stores in Changchun.

56 F. Wang et al. / Cities 41 (2014) 54–63

others, and being accessible via a virtual straight route to all others.Namely, they are closeness (C C), betweenness (C B) and straight-ness (C S).

Closeness centrality C C measures how close a node is to all theother nodes along the shortest paths of the network. C C for a nodei is defined as:

CCi ¼

N � 1PN

j¼1;j–idij

where N is the total number of nodes in the network, and dij is theshortest distance between nodes i and j. In other words, C C is theinverse of average distance from this node to all other nodes.Betweenness centrality CB measures how often a node is traversed

by the shortest paths connecting all pairs of nodes in the network,defined as:

CBi ¼

1ðN � 1ÞðN � 2Þ

XN

j¼1;k¼1;j–k–i

njkðiÞnjk

where njk is the number of shortest paths between nodes j and k,and njk(i) is the number of these shortest paths through node i. CB

captures a special property for a place: it does not act as an originor a destination for trips, but as a pass-through point. Straightnesscentrality CS measures how much the shortest paths from a nodeto all others deviate from the virtual straight lines (Euclideandistances) connecting them. CS is measured as:

F. Wang et al. / Cities 41 (2014) 54–63 57

CSi ¼

1N � 1

XN

j¼1;j–i

dEuclij

Edij

where dEuclij is the Euclidean distance between nodes i and j. Ci

S mea-sures the extent to which a place can be reached directly, like on astraight line, from all other places in a city. It is a quality that makesit 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 centralityindices. Specifically, the tool is installed as an added toolset underArcToolbox, and the tool reads the street network dataset preparedby 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, thecentrality value for an edge (street segment) is computed as theaverage of its two end nodes. A larger value of each centrality indexindicates a location more central and thus more advantageous. Thepattern of closeness is largely concentric with the highest valuearound the CBD and declining gradually outwards, but the patternsof betweenness and straightness are far from clear.

Finally, the kernel density estimation (KDE) method is used totransform the store locations and the street centrality values in dif-ferent scales to the same data framework (i.e., a raster system) sothat the relationship between them can be assessed at the samescale (Wang, Antipova et al., 2011). The KDE estimates the densityat each location as the average value within a spatial windowaround it by weighing nearby objects more than far ones basedon a kernel function (e.g., Fotheringham, Brunsdon, & Charlton,2000, pp.146–149). This study uses the built-in KDE tool inArcGIS, which adopts the popular quartic function (Silverman,1986). By employing a bandwidth of 1500 m and a cell size of100 m � 100 m, the raster layer covers a rectangular area consist-ing of 214,368 grid cells (462 rows by 464 columns) for the studyarea. In essence, the KDE uses the density (or average attributes) ofnearby objects to represent the property at the middle location. Bydoing so, it converts a point layer of discrete events (store locationsor nodes on the street network) to a continuous surface (i.e., ras-ter). Corresponding to three centrality measures, six types of storesplus a layer of all stores combined, we have a total of 10 rasterlayers by the KDE method. Fig. 4a–c shows the KDE of closeness,betweenness and straightness, respectively. Among the sevenraster layers for stores, only the KDE of specialty stores ispresented (Fig. 4d) as an example. The KDE value of a centralityindex captures the value of a location as influenced by itssurroundings, and the KDE value of a type of stores representsthe relative concentration of stores around that place.

Fig. 3. Street centrality values: (a) closeness, (b) betweenness, and (c) straightness.

Geographic distribution patterns of retail stores

This section provides an exploratory analysis of spatial patternsof the retail stores in Changchun. Does one type of stores tend to bemore clustered or dispersed around its center than other types? Dothey trend in a particular direction? Is a clustering or dispersingpattern statistically significant or otherwise random? These issuesare analyzed by the centrographic method and the nearest neigh-bor method.

The centrographic analysis begins with two basic centrographicmethods: ‘‘mean center’’ and ‘‘standard distance.’’ The mean centercan be considered the average location of stores, and the standarddistance is a radius equal to one standard deviation of the distancesof all stores from the mean center, and thus measures the spatialspread of their distribution. The ellipse method further advancesthe concepts of mean center and standard distance by identifyingthe directional variation of spatial spread captured by the standard

distance, i.e., the stores are most spread out along the elongatedaxis than any other directions. Here, the ‘‘Directional Distribution(Standard Deviational Ellipse)’’ tool in ArcGIS is used to examinethe distribution patterns of store types.

Fig. 5 shows the six ellipses (one for each type of stores). Amongthe six, the distribution of specialty stores is most compact aroundtheir center, which is slightly south of the CBD. The distribution

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.

58 F. Wang et al. / Cities 41 (2014) 54–63

patterns of supermarkets and department stores are similar to thatof specialty stores but trend more toward southwest. All thesethree types of store are mainly elongated along the north–southdirection. Consumer product stores are more tilted along theNE-SW direction. Construction material stores and furniture storestend to locate more in the east than any other types of stores, andmainly along the east–west direction. That is to say, specialty

stores are more likely to benefit from being near each other aroundthe downtown than other stores in order to attract customersby their collective offering of diverse and highly specializedcommodities. Other stores are more concerned of competition forpatronage of customers and tend to be more dispersed. Insummary, these patterns are merely descriptive and reveal limitedinformation of stores’ location preference.

Table 1Results of average nearest neighbor analysis of stores.

Store type No. Expecteddistance (m)

Observeddistance (m)

Z-score

Specialty store 323 459.69 245.384 �16.00b

Construction material 127 674.112 320.616 �11.31b

Consumer product 166 584.044 349.468 �9.90b

Department store 63 800.331 405.418 �7.49b

Supermarket 212 503.751 407.229 �5.34b

Furniture store 82 738.407 572.096 �3.90a

All stores 973 270.567 153.548 �25.80b

a Significant at 0.01.b Significant at 0.001.

F. Wang et al. / Cities 41 (2014) 54–63 59

The nearest neighbor index measures the distance between eachstore and its nearest store, and then calculates the average(Mitchell, 2005). A smaller average distance (i.e., ‘‘observed dis-tance’’) in comparison with a random distribution of the samenumber of stores (i.e., ‘‘expected distance’’) indicates a clusteredpattern, and a larger average distance represents a dispersed pat-tern. The term ‘‘clustered or dispersed’’ used here refers to whetherindividual stores are within proximity or far apart from each other,whereas the term in the centrographic method implies how far thestores spread across their mean center. Moreover, the nearestneighbor method reports a Z score to indicate whether theidentified pattern is statistically significant, and thus is consideredinferential spatial statistics whereas the centrographic method isdescriptive spatial statistics.

Table 1 summarizes the results. The stores altogether (withoutdifferentiation of store types) are highly clustered and far fromrandom (the result in the bottom row in Table 1). When analysisis conducted by store type, each of the six store types is clustered,and the clustered pattern is statistically significant at 0.01. Thestore types are ordered in Table 1 according to their Z-scores fromthe most significant to the least. It is conceivable that the top threetypes of stores (specialty stores, construction material markets,and consumer product stores) may benefit from stores of the sametype being close to each other as consumers tend to visit multiplestores before purchases. Department stores also have a tendency ofbeing near each other, but not as strong as the first three.Supermarkets and furniture stores are clustered with the lowestlevels of statistical significance. This reflects that these two typesof stores (particularly the large ones) may be more mindful ofavoiding competition from their rival store than taking advantageof customers’ multi-store selective shopping behavior.

Analysis of store locations by store type

This section examines whether stores differ significantly intheir location choices across store types. We compare the distancesof stores from the CBD by store types as the baseline, and focus ondifferences in their street centrality values. In other words, beingcentral is first measured simply by the proximity to the CBD, andthen by the three comprehensive centrality indices.

Both Euclidean and street network distances are used to assesseach store’s proximity to the CBD. As shown in Fig. 6a, the order ofstore types from the shortest to the longest average Euclidean dis-tance is consistent with that of store types measured in street net-work distances. Are differences in average distances statisticallysignificant across store types? This may be answered by conduct-ing the ANOVA (analysis of variance) test. Here a regression modelis introduced for the same purpose for its simplicity and easyinterpretation (Gu, Wang, & Liu, 2005). Five dummy variables canbe used to code the six store types. Since specialty stores havethe shortest average distance from the CBD, they are selected as

the reference type and coded as X1 = X2 = X3 = X4 = X5 = 0. Storesof any other type are coded by assigning a value ‘‘1’’ to one ofthe dummy variables and ‘‘0’’ to the rest four (e.g., X1 = 1 andX2 = X3 = X4 = X5 = 0 for department stores; X2 = 1 and X1 = X3 =X4 = X5 = 0 for supermarkets; and so on). Denoting ‘‘distance’’ asthe dependent variable Y, the model is written as

Y ¼ b0 þ b1X1 þ b2X2 þ b3X3 þ b4X4 þ b5X5 ð1Þ

In Eq. (1), the intercept b0 is the average distance for the refer-ence category (i.e., specialty stores), coefficient bi is the differencein average distances between the stores of a category coded Xi = 1and the stores of the reference category, and correspondingt-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 averagefor specialty stores is 3476.8 m, for department stores is3476.8 + 462.3 = 3939.1 m, for supermarkets is 3476.8 + 659.9 =4136.7 m and so on, which are consistent with Fig. 6a. More impor-tantly, the corresponding t-values indicate that the differencesbetween the reference category (specialty stores) and any typesof stores are statistically significant except for department stores.The F value (=9.21) indicates that overall the model is significant.Note that the store types are sorted in Table 2 in an increasingorder in average distance. In summary, either by Euclidean ornetwork distance, specialty stores and department stores (withno significant difference between them) are closest to the CBD,followed by supermarkets, consumer product stores, furniturestores, and construction material stores.

Fig. 6b–d shows the variations of average centrality values(closeness, betweenness and straightness, respectively) acrossstore types. The centrality values of each store are extracted fromthe KDE layers by identifying the raster cell where a store falls in.Similarly, the regression model in Eq. (1) is applied to the threecentrality indices to test whether the KDE centrality values atstore locations differ significantly across the six store types. Theregression results are also presented in Table 2 (the three col-umns on the right). Recall that a higher centrality value impliesa more favorable (central) location. In the model for closeness,the average centrality value for ‘‘specialty stores’’ is the intercept(0.000180). This value is not significantly different from ‘‘depart-ment stores.’’ However, it drops by 0.000063 for ‘‘supermarkets’’,by 0.000058 for ‘‘consumer products’’ and so on. The result forcloseness indicates that specialty stores and department stores(with no significant difference between them) have the best cen-trality, followed by consumer product stores, supermarkets, furni-ture stores, and construction material stores. The order is largelyconsistent with those for Euclidean and network distances fromthe CBD with only consumer product stores and supermarketsswitching their positions. That is to say, proximity to CBD is agood proxy for the closeness index in capturing a location beingclose to others. The result for straightness is very similar to thatfor closeness with the same order of store types in centrality val-ues. The result for betweenness is consistent with those on close-ness and straightness at the two ends of the ranking, i.e., specialtystores and department stores (again with no significant differencebetween them) still lead the pack with the best centrality andconstruction material stores remain the last. However, it differsslightly in the middle, i.e., the average value is higher forfurniture stores than for supermarkets, which is higher than forconsumer product stores.

In summary, based on Fig. 6a–d and Table 2, it is clear thatspecialty stores and departments enjoy the best location and theconstruction material stores are left with the worst in eitherproximity to CBD or any centrality measure. Among the threeremaining store types, furniture stores are ranked the last in prox-imity to CBD, closeness or straightness and only ahead of the other

Fig. 6. Average values of location by store types: (a) distances from CBD, (b) closeness, (c) betweenness, (d) straightness.

Table 2Regression 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 thedifference between that type of stores and specialty stores; t-values in parentheses.

a Significant at 0.01.b Significant at 0.001.

60 F. Wang et al. / Cities 41 (2014) 54–63

two types in betweenness; when compared to consumer productstores, supermarkets are closer to CBD and have better between-ness, but lower closeness and straightness values. This is an impor-tant baseline for the correlation analysis in the next section, wherewe will offer more discussion on location selection behavior by dif-ferent stores.

Correlation of street centrality and retail stores

The previous section discussed how different stores differ inlocation by proximity to the CBD and street centrality indices. Thissection takes a step further to examine how the distribution ofstores may correlate with the spatial variation of centrality valuesand how the correlation strength may vary between differentstore types and various centrality indices. Correlation analysiswould not be feasible between them without transformation ofdata frames because the store locations do not match withcentrality measures attached to the nodes of street network. Asexplained in section ‘Data preparation’, the KDE method is usedto convert both to the same raster data frame to facilitate thecorrelation analysis.

Two types of analysis are conducted: one is based on all rastercells, and the other is on a subset of the cells where stores are located.The first is to examine correlation between the concentration

(density) of stores and centrality values across the whole studyarea (and thus perhaps reveals their linkage potential). It is imple-mented by using the Band Collection Statistics tool (specifically,the option ‘‘Compute covariance and correlation matrices’’) inArcGIS. The results are reported on the left side in Table 3. Thesecond correlation analysis is applied to the extracted KDE valuesof stores and centrality indices at only existing store locations.The results are on the right side in Table 3.

First of all, the stores in general (without differentiation bystore types) are highly correlated with each centrality index (withall correlation coefficient above 0.82), and the correlation strengthdiffers little across centrality indices. That is to say, commercialactivities especially retail stores value locations of favorable cen-trality; however, the stores altogether do not necessarily preferone type of centrality than others. The former is consistent withthe finding reported in Porta et al. (2009), and the latter is not asthe same paper suggested a preference of betweenness by com-mercial activities over closeness and straightness in Bologna, Italy.Does a type of stores prefer one particular centrality over othercentralities? This needs to be addressed by analysis of the correla-tions by store types, i.e., a focus of this paper.

Secondly, the correlations between the centrality indices andstore densities are all statistically significant for all store types.With the exception of furniture stores and construction materialstores, all correlations are strong with coefficients above 0.5. Note

Table 3Pearson’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 0.837Department store 0.644 0.646 0.618 0.636 63 0.790 0.886 0.749 0.808Supermarket 0.695 0.753 0.684 0.711 212 0.557 0.684 0.523 0.588Consumer product 0.591 0.537 0.596 0.575 166 0.606 0.473 0.644 0.574Furniture store 0.416 0.455 0.422 0.431 82 0.270 0.244 0.300 0.271Construction 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.

F. Wang et al. / Cities 41 (2014) 54–63 61

that the results based on all locations are generally consistent withthose based on store sites only. This is particularly true for the fourstore types with correlation coefficients above 0.5. The consisten-cies refer to not only which store types exhibit higher correlationcoefficients with centrality than other store types (i.e., specialtystores tend to have the highest correlation, and consumer productstores tend have the lowest), but also which centrality indexexhibits the highest correlation within the same store type (e.g.,for specialty store, its correlation with closeness is the highest).Therefore, our following discussion focuses on the results basedon store sites.

Thirdly, the store types in Table 3 are ordered by average corre-lation coefficients. The order is the same as the order in Table 2.Recall that Table 2 reports the average centrality values of eachstore type (even distances from CBD could be considered as a prim-itive measure of being central). That is to say, in general, storeswith better centrality values also have stronger correlations withcentrality indices. Once again, specialty stores generally have thehighest correlation with centrality, followed by department stores,

Fig. 7. Best fitting form for correlation between (a) specialty stores and closeness, (b) depproduct stores and straightness, (e) furniture stores and straightness, (f) construction m

supermarkets, consumer product stores, furniture stores, and con-struction material stores.

Finally, among the four types of stores with correlation coeffi-cients above 0.5, specialty stores enjoy the highest correlation withcloseness; department stores or supermarkets have the highestcorrelation with betweenness; and consumer product stores corre-late best with straightness.

Would the above findings be affected by the measure scale ofKDE values of either stores or centrality? Denoting stores’ KDE val-ues as y and centrality KDE values as x, we experimented with fourpossible forms of correlations such as x vs. y (as reported inTable 3), ln(x) vs. y, x vs. ln(y), and ln(x) vs. ln(y). When the best fit-ting form is not the linear one (x vs. y), the improvements in corre-lation coefficients are minor, and the above three findings all hold.Fig. 7a–f shows the best fitting form for each store type. One mayrecover the correlation coefficient (r) by taking the square root ofR2 reported in the figures. Among the four types of stores withstrong correlations with centrality, the order of correlationstrength remains such as: department stores vs. betweenness,

artment stores and betweenness, (c) supermarkets and betweenness, (d) consumeraterial stores and straightness.

62 F. Wang et al. / Cities 41 (2014) 54–63

specialty stores vs. closeness, supermarkets vs. betweenness, andconsumer product stores vs. straightness.

The findings may be summarized and understood along twolines: the overall correlation strengths across store types, and thevariation of correlation from one centrality index to another withinthe same store types. The former is also consistent with our findingon the average centrality values reported in Section 5. The central-ity value itself and the correlation with it indicate the preferenceand competitiveness of a store for location being central. The storescan be clearly grouped into three classes: specialty stores anddepartment stores at the top with correlation coefficients ataround 0.8, supermarkets and consumer product stores in the mid-dle with most coefficients ranging 0.5–0.7, furniture stores andconstruction material stores at the bottom with coefficients below0.3. Specialty stores and department stores usually command thehighest volume of customers drawn from the widest geographicareas. Some major flagship stores in the business center on Chon-gqing Road attract citywide customers. Therefore, they demand forthe most central locations. Supermarkets and consumer productstores also draw a large number of customers. However, most ofsuch stores are highly substitutable and customers are mainly fromlocal areas. As a result, they are located in less competitive loca-tions. Furniture stores and construction material stores carrylarge-size and heavy commodities and consume large lots of land.Customers visit these stores with a low frequency. Therefore, theyare usually located on the urban fringes where centrality values arethe lowest.

Furthermore, between specialty stores and department stores,the former slightly prefers closeness to the other two centralities,and the latter favors betweenness the most. The closeness central-ity captures total travel cost for customers, which may matter mostfor specialty store; and the betweenness centrality reflects thethrough traffic volume of customers, which is likely a major factorin location choice by department stores (exemplified by manydepartment stores on Chongqing Road, Hongqi Road and GuilinRoad in Changchun). Between supermarkets and consumer prod-uct stores, the former values betweenness most and the latteremphasizes straightness. It is conceivable that many customersstop by supermarkets to shop on their trips for other purposes,and thus contribute to the value of betweenness for supermarkets.Straightness is an index measuring efficiency of trips visiting alocation or legibility of a place, and apparently a major quality oflocation sought by consumer product stores. Due to overall lowcorrelation coefficients for furniture stores and construction mate-rial stores, we refrain from discussing the differences across cen-tralities for them.

Conclusions

This paper analyzes the location patterns of various retail storesin Changchun, China. Based on the centrographic method, the dis-tribution of specialty stores is most compact around their centerbecause they are more likely to benefit from being near each otherthan other stores in order to attract customers by their collectiveoffering of diverse and highly specialized commodities. Based onthe nearest neighbor analysis, three store types (specialty stores,construction material markets and consumer product stores) aremore clustered in order to benefit from customers’ multi-storeselective shopping behavior. The other types of stores (departmentstores, supermarkets and furniture stores) are more dispersed foravoiding competition from their rival stores. For similar reasons,in terms of Euclidean or network distance, specialty stores anddepartment stores are closest to the CBD, followed by supermar-kets, consumer product stores, furniture stores, and constructionmaterial stores.

The most significant findings are based on the street centralityindices, namely closeness, betweenness and straightness. The setof centrality indices captures the very essence of location in termsof a place’s accessibility, intermediacy and directness amongothers. An overall strong correlation between street centralitiesand store densities lends support to the interdependence betweenstreet network and retail stores in an intraurban setting. Theresults indicate that stores whose locations have better centralityvalues also have stronger correlations with centrality indices. Boththe average centrality values and correlation coefficients suggestthat specialty stores value various centralities most, followed bydepartment stores, supermarkets, consumer product stores, furni-ture stores, and construction material stores. Among the four storetypes with correlation coefficients above 0.5, specialty stores favorcloseness most, department stores and supermarkets preferbetweenness to the other two centralities, and consumer productstores value straightness most. In other words, while some typesof stores demand higher centralities more so than others, the typeof centrality favored by stores may vary by store types. Theresearch sheds light on location behavior by retail stores and helpsguide urban planners in the design and allocation of commercialland use (including the ongoing debate on the planning and siteselection for Changchun’s CBD).

Acknowledgement

We would like to acknowledge the supports: a visiting profes-sorship at the Northeast Institute of Geography and Agroecologyof Chinese Academy of Sciences in the summer of 2013 (Wang),Grant No. 41071109 from the National Natural Science Foundationof China (Xiu) and Grant No. 41071108 from the National NaturalScience Foundation of China (Zhang).

References

Bagler, G. (2008). Analysis of the airport network of India as a complex weightednetwork. Physica A: Statistical Mechanics and its Applications, 387, 2972–2980.

Barabási, A.-L. (2002). Linked: The new science of networks. New York: Plume.Batty, M. (2008). Whither network science? Environment and Planning B: Planning

and Design, 35, 569–571.Berman, B., & Evans, J. R. (2001). Retail management: A strategic approach (8th ed.).

Upper Saddle River, NJ: Prentice Hall.Chai, Y., Shen, J., & Long, T. (2007). Downtown retail development under

suburbanization – A case study of Beijing. Chinese Geographical Science, 17, 1–9.Changchun Bureau of Commerce (in collaboration with the Changchun Institute of

Urban Planning & Design), 2011. Changchun commercial establishments plan2011–2020 (draft for soliciting public inputs). <http://www.chinadmd.com/file/3rrtxeirirscrxpoa63vvsso_1.html> Accessed 09.10.13).

Conroy-Dalton, R. (2003). The secrete is to follow your nose: Route path selectionand angularity. Environment and Behavior, 35, 107–131.

Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2000). Quantitative geography:Perspectives on spatial data analysis. London: Sage.

Gao, S., Wang, Y., Gao, Y., & Liu, Y. (2013). Understanding urban traffic flowcharacteristics: A rethinking of betweenness centrality. Environment andPlanning B, 40, 135–153.

Ghosh, A., & McLafferty, S. (1987). Location strategies for retail and service firms.Lexington, MA: D.C. Heath.

Gu, C., Wang, F., & Liu, G. (2005). The structure of social space in Beijing in 1998: Asocialist city in transition. Urban Geography, 26, 167–192.

Guy, Clifford M. (1998). Classifications of retail stores and shopping centres: somemethodological issues. GeoJournal, 45, 255–264.

Hansen, W. (1959). How accessibility shapes land use. Journal of the AmericanInstitute of Planners, 25, 73–76.

Hillier, B., & Hanson, J. (1984). The social logic of space. Cambridge: CambridgeUniversity Press.

Hillier, B., Penn, A., Hanson, J., et al. (1993). Natural movement: Or, configurationand attraction in urban pedestrian movement. Environment and Planning B, 20,29–66.

Hotelling, Harold. (1929). Stability in competition. Economic Journal, 39(153),41–57.

Huff, D. L. (1963). A probabilistic analysis of shopping center trade areas. LandEconomics, 39, 81–90.

Huff, D. L. (2003). Parameter estimation in the Huff model. ArcUser, 34–36 (Oct.–Nov.).

F. Wang et al. / Cities 41 (2014) 54–63 63

Jiang, B., & Claramunt, C. (2004). Topological analysis of urban street networks.Environment and Planning B, 31, 151–162.

Joseph, A. E., & Phillips, D. R. (1984). Accessibility and utilization: geographicalperspectives on health care delivery. New York: Harper and Row.

Karimi, Kayvan. (2012). A configurational approach to analytical urban design:‘Space syntax’ methodology. Urban Design International, 17, 297–318.

Kuby, M., Tierney, S., Roberts, T., Upchurch, C. (2005). A comparison of geographicinformation systems, complex networks, and other models for analyzingtransportation network topologies. NASA/CR-2005-213522.

Li, W., & Cai, X. (2004). Statistical analysis of airport network of China. PhysicalReview E, 69, 1–6.

Luo, W., & Wang, F. (2003). Measures of spatial accessibility to healthcare in a GISenvironment: Synthesis and a case study in Chicago region. Environment andPlanning B: Planning and Design, 30, 865–884.

Mitchell, Andy (2005). In The ESRI guide to GIS analysis volume 2: Spatialmeasurements & statistics. Redlands, CA: ESRI Press.

Porta, S., Crucitti, P., & Latora, V. (2006). The network analysis of urban streets: aprimal approach. Environment and Planning B: Planning and Design, 33, 705–725.

Porta, S., Latora, V., Wang, F., et al. (2012). Street centrality and location of economicactivities in Barcelona. Urban Studies, 49, 1471–1488.

Porta, S., Latora, V., Wang, F., Strano, E., Cardillo, A., Iacoviello, V., Messora, R., &Scellato, S. (2009). Street centrality and densities of retails and services inBologna, Italy. Environment and Planning B, 36, 450–465.

Qi, W., Fang, C., & Song, J. (2008). Measurement and spatial distribution of urbanagglomeration industrial compactness in China. Chinese Geographical Science,18, 291–299.

Sevtsuk, A., Mekonnen, M., Kalvo, R. (2013). Urban network analysis: A toolbox v1.01for ArcGIS. <http://cityform.mit.edu/projects/urban-network-analysis.html>.

Silverman, B. W. (1986). Density estimation for statistics and data analysis. London:Chapman and Hall.

Taneja, S. (1999). Technology moves in. Chain Store Age, 136 (May Issue)).Wang, F. (2012). Measurement, optimization and impact of healthcare accessibility:

A methodological review. Annals of the Association of American Geographers, 102,1104–1112.

Wang, F., Antipova, A., & Porta, S. (2011). Street centrality and land use intensity inBaton Rouge, Louisiana. Journal of Transport Geography, 19, 285–293.

Wang, J., Jin, F., Mo, H., & Wang, F. (2009). Spatiotemporal evolution of China’srailway network in the 20th century: An accessibility approach. TransportationResearch Part A: Policy and Practice, 43, 765–778.

Wang, J., Mo, H., Wang, F., & Jin, F. (2011). Exploring the network structure andnodal centrality of China’s air transport network: A complex network approach.Journal of Transport Geography, 19, 712–721.

Weibull, J. W. (1976). An axiomatic approach to the measurement of accessibility.Regional Science and Urban Economics, 6, 357–379.

Zentes, Joachim, Morschett, Dirk, & Schramm-Klein, Hanna (2011). Strategic retailmanagement: text and international cases (2nd ed.). Wiesbaden, Germany:Springer Gabler.

Zhang, X., Huang, P., Sun, L., & Wang, Z. (2013). Spatial evolution and locationaldeterminants of high-tech industries in Beijing. Chinese Geographical Science, 23,249–260.