Open Geosciences 2020; 12: 1541–1556

Research Article

Zhao Yannan, Zhang Lu*, and Zhang Xinhuan* The spatial distribution of retail outlets in Urumqi: The application of points of interest

https://doi.org/10.1515/geo-2020-0149 provide a reference for guiding the adjustment and opti- received May 17, 2020; accepted October 21, 2020 mization of retail layouts.

Abstract: Studies on the distribution of retail outlets are Keywords: retail outlets, category, spatial distribution, crucial for optimizing urban resource allocation, and POIs, Urumqi their rationalized layout plays an important role in devel- oping urban economies and meeting consumer demand. However, the literature on the subject has been limited by data collection. In the era of big data, there has been an 1 Introduction emergence of geo-referenced data that are more accurate and convenient and thus more able to be applied in the Rapid urbanization has brought drastic changes to the [ ] retail analysis. This study addresses this lacuna by development of contemporary retail 1 . Although there exploring the spatial distribution of various retail outlets are lots of online shopping stores, retail stores occupy an with the application of points of interest. Our study important place in modern cities, particularly in terms of [ ] demonstrates that (1) the retail outlets in Urumqi are con- economic growth and employment 2,3 . Retail activity, centrated in the inner city. The inner-city area has which is one of the most important cost factors for urban fl a higher density distribution of retail outlets, and the residents, has played a powerfully in uential role not ff development of the retail function is more mature. (2) only in socioeconomic development but also in a ecting ’ [ – ] Various categories of retail outlets display dissimilar urban residents life quality 4 6 . Studies on the spatial fi agglomeration characteristics and hot spots. Specialty distribution of retail outlets have signi cant importance stores, clothing and footwear stores, convenience stores, for land management and the optimization of urban [ ] and home-building material markets have lower average resource allocation 5 , and its rationalized layout is nearest-neighbor distances. (3) The retail outlets in the critical for the development of urban economies and [ ] subject area are directionally distributed in the north- meeting consumer demands 7,8 . west–southeast direction, which is the result of both With the continuous development of the retail - the topographical condition and government interven- industry, the issues of retail outlets are gaining increas [ ] tion in Urumqi. (4) Various categories of retail outlets ing attention 9 . Previous studies have focused on retail [ ] [ ] tend to agglomerate at different scales. These results location 10 , hierarchy 11 , consumer behavior and [ ] are conducive to the exploration of the location rules commercial space 12 , and retail outlet aggregation and [ ] and layout preferences of retail locations, and they competition 13 . Studies on the distribution patterns of retail outlets are of great significance to understanding retail industry rules [14]. However, what has been missed in previous studies is that the retail outlets can be divided  into several categories (e.g., supermarkets and conveni- * Corresponding author: Zhang Lu, Department of International ence stores), which raises the research question that what Economics and Trades, School of International Economics and - Management, Beijing Technology and Business University, Beijing, are the distribution characteristics of various retail out 100048, China, e-mail: [email protected] lets. Ignoring the differences is unfavorable to display * Corresponding author: Zhang Xinhuan, State Key Laboratory of different characteristics in spatial distribution. One of Desert and Oasis Ecology, Xinjiang Institute of Ecology and the reasons for the research lacuna is the lack of data. Geography, Chinese Academy of Sciences, Urumqi 830011, China, The data used in retail studies have traditionally mostly - e mail: [email protected] - Zhao Yannan: Institute of Geographic Sciences and Natural been obtained through economic census data and ques Resources Research, Chinese Academy of Sciences, Chaoyang tionnaire surveys, which has led to a lack of timeliness District, Beijing 100101, China, e-mail: [email protected] and comprehensiveness of such studies. Generally, two

Open Access. © 2020 Zhao Yannan et al., published by De Gruyter. This work is licensed under the Creative Commons Attribution 4.0 International License. 1542  Zhao Yannan et al. obstacles make it a challenge to obtain proper data. The to study properties in space. An enormous need is felt first obstacle is the limitation of data availability. The fact to utilize POIs for retail studies as the pace at which that the retail industry generates an enormous amount of new communities have grown and the sheer number of information makes it very difficult to obtain. The second geospatial variables have been generated in the process obstacle pertains to the fact that most information on the cannot be efficiently captured traditionally. In terms of retail industry is not available to the public. As noted by research, we propose POI as an alternative data source Tomlan [15], although commercial activity is everywhere for characterizing spatial distribution for the following and well-known, the identification and utilization of reasons. The greatest advantage of POIs is that they are “good” data are relatively new developments. entities of interest with well-defined locations. Another Big data bring opportunities and challenges to spa- obvious feature of POIs is that they are available from tial data mining. In addition to the use of statistical data, service providers as well as in online maps. In addition, and other types of data, open-access data play an in- POIs have great potential for global analyses since they creasing role in related studies [16]. Recent advances in provide nearly global coverage. With all these advan- some geospatial big data, such as location-based service tages, POIs offer new avenues for exploring the geogra- data [17], points of interest (POIs)[18], and open data phies of user-generated content. from Internet, have been applied to the research of urban Existing studies on the application of POIs in retail structure. Specifically, POIs, i.e., specific point locations outlet analysis focused on the three research agendas. that may be useful or interesting, represent an explosion The first research agenda is to explore the spatial distri- of rich, spatially embedded information. As a conse- bution pattern of retail outlets. The rational distribution quence, there is a proliferation of research using POIs of retail outlets is of great significance to the economic in commercial studies [19]. In contrast to traditional sta- development of a city and to satisfy the daily consump- tistical data, POI data sets represent a much finer grained tion of residents. Some scholars explored the agglomera- picture of retail distribution [20]. Gaode map, which is a tion and spatial patterns of retail outlets [24]. In addition, map software used as a proxy for retail outlets, records previous studies show that the spatial distribution of the location information and helps to identify spatial dis- retail outlets is related to the transport infrastructure [2] tribution characteristics for retail centers. In addition, and street centrality [20]. The second research agenda studies on retail outlets in northwestern China, which focuses on the identification of retail hot spots and the has been characterized as a less developed area, are quite delineation of type zones. The identification of retail out- limited. Thus, the main aim of our analysis is to analyze lets plays an important role in the study of urban com- the distribution characteristics of various retail outlets mercial structure. Previous studies explored the scope, with the application of POIs. spatial structure, and mode of commercial centers in The remainder of this study is organized as follows. cities such as Beijing and Guangzhou [25,26]. The third Section 2 reviews the rationality of the application in research agenda is to evaluate commercial services which retail studies. Section 3 describes the study area, data are essential to increase the attractiveness of customers. sources, and methods. Section 4 reports the main results Previous studies show that there is a spatial distribution of the study while Section 5 discusses the main findings difference in customer satisfaction. Among these, the lo- as well as some limitations of the analysis. Finally, Sec- cation of metro stations and business centers is the key tion 6 presents our main conclusions and some avenues factor for customers’ satisfaction with commercial facil- for future research. ities [27]. Some scholars also discussed the rationality of commercial service facilities’ layout [28]. This is the first research agenda to be addressed by our analysis. The overall distribution of retail outlets has 2 POIs as data sets in the literature been well examined in Western European and Southeast Asian countries and Japan from the perspectives of cen- In the era of big data, we have seen the emergence of geo- tral place and economic agglomeration theories [29–31]. referenced data which have increasingly attracted in- China has undergone a very dramatic shift from the scar- terest from geographic information system (GIS) scholars city of basic consumer goods to relative abundance in and have become popular in geographical research recent years. As a country experiencing a transition to a [21,22]. Increasing numbers of data on POIs are being market-oriented economy, Chinese cities provide inter- generated from online platforms [23]. Thus, they are esting cases for studying the spatial structure of shopping becoming more useful and straightforward for researchers center. The existing research mainly involves the The spatial distribution of retail outlets in Urumqi: The application of POIs  1543 development mode of shopping centers and mechanisms brands and leisure patterns to meet the needs of emer- [32]. It is noteworthy that the market-oriented economy ging retail activities. The total retail sales of consumer in China has also promoted the explosion of retail outlets, goods in 2019 were 138.92 billion, which is 5.05 times for instance, leisure, entertainment, catering, etc. Thus, that in 2006 [39]. Thus, the special location and position the current studies are not conducive for reflecting on the of Urumqi make such research necessary to consider the distribution of retail outlets by different categories. The retail industry in a new light with interesting implications continual emergence of new types of retail business calls for other cities. for the analysis of how each category of retail outlets Besides, recent changes in urban planning and the distributes. Along with the development of quantitative continuous rising retail outlets also indicated the neces- geography and GISs, mathematical models (e.g., analy- sity for preliminary exploration of the spatial distribution tical hierarchy process, analysis of correlation, and geo- of retail outlets in Urumqi. Specifically, the spatial dis- graphically weighted regression) and spatial statistical tribution of retail outlets in Urumqi is closely related to analyses (e.g., kernel density estimation, spatial gravity, the urban development and construction process, which and hot spot analysis) are new tools for the quantitative is encouraged by the restructuring of the urban space. In analysis of shopping centers [21,33]. Results showed that the 1980s, the urban construction in Urumqi was domi- retail outlets are not evenly distributed in urban spaces nated by the reconstruction of the old city, and most and tend to be geographically concentrated. Specifically, of the retail outlets were concentrated in the old city. homogeneous agglomeration occurs when several retai- Therefore, the spatial distribution of retail outlets in lers selling similar products gather together while hetero- Urumqi was characterized by a high concentration in geneous agglomeration occurs when many retailers selling the old city. Since the 1990s, the urban construction of different kinds of products [34,35]. In sum, studies on the Urumqi shifted from the reconstruction of the old city to agglomeration characteristics of different retail enterprises the development of the new district. The construction of can provide background information for urban planning new districts (e.g., the Xinshi District, the Economic and development. Technological District, and the Midong District) witnessed the process of the extension and spreading of new retail outlets to the periphery (Figure 1b). However, retail out- lets in the inner city are still in a leading position in terms 3 Methodology of quantity, scale, and service level [40]. The continuous improvement in Urumqi’s nationali- 3.1 Study area zation status and the increasing urban population have brought development opportunities for the retail industry. According to “Urumqi Urban Master Plan 2014–2020,” From the perspective of location, Urumqi is considered as Urumqi is located as an important central city in north- the “Pivot of Asia” and a “new center of gravity” between western China and an international business center to Europe, the Middle East, and Central Asia [36]. From the Central and West Asia. Accordingly, relying on sur- perspective of position, Urumqi is the second largest po- rounding mountains and urban agricultural landscape pulous city in northwestern China (Figure 1a).Asitisat belts, the land layout pattern of “one axis, two hearts, the intersection of international trade routes (e.g., the Old and seven groups” will be finally formed (Figure 1c). Silk Road, the Belt, and Road), it has an increasingly Under this background, studies on the spatial structure active commercial life with varied retail outlets ranging characteristics of the retail outlets in Urumqi will help to from street shopping to its world-famous covered bazaars provide a reference for its spatial layout and structural [37]. Additionally, Urumqi has great potential for the optimization. Therefore, our analysis attempts to address development of various retail outlets due to its rapid this research lacuna by exploring the distribution char- population and income growth. In the past 10 years, the acteristics of retail outlets in Urumqi. urban population and retail consumption have greatly increased in Urumqi. The urban resident population in Urumqi in 2019 was 3.55 million, which is 1.78 times the number in 2006, and the per capita income was 42,667 3.2 Data sources yuan in 2019, which is 4.12 times that in 2006 [38].Asa result, the number of various retail outlets has rapidly Our study focuses on the central urban area with refer- increased with the addition of various international ence to the “Urumqi Urban Master Plan 2014–2020.” 1544  Zhao Yannan et al.

Figure 1: Location of study area.

Three areas, i.e., the downtown area, the three-ring area stores, specialty stores, shopping centers, factory direct (Outer and Inner Ring Road, Second Ring Road, and Ring sales, and department stores. Referring to China’s current Expressway) and the old city center in the downtown classification of retail formats (GB/T18106-2010) and area, are added to the map after the process of registra- taking into account the actual retail industry in Urumqi, tion and vectorization. The properties of retail stores in- this analysis divided the retail centers into nine outlets, clude name, address, longitude, latitude, and business including specialty stores, clothing and footwear stores, scope, and referred to as POIs applied in our analysis. home-building material markets, convenience stores, The amenity POIs data are gathered from Gaode Maps shopping malls, appliance electronics stores, supermar- (http://m.amap.com/), which is the primary product of kets, integrated markets, and bird and insect markets the AutoNavi Software. Given that the positions and (Table 1). In total, 4,599 centers in specialty stores (the properties can be explored through these POIs, after cor- largest category) and 3,799 centers in clothing and foot- recting and matching addresses, a total of 17,710 retail wear stores were obtained. centers were extracted. To check the usability of POI data, The retail outlets in Urumqi are unevenly distributed the information of retail centers are randomly selected and do not always match the population density. The and checked by a sampling survey, telephone inquiry, population densities of the Tianshan District, the Xinshi and field visits. District, the Shayibake District, and the Shuimogou In general, the classification of retail outlets is based District are relatively high, and the number of retail out- on business models, service functions, product structure, lets owned by 10,000 people is also relatively high (Table 2). location, scale, target customer groups, etc. Regarding The average density of retail outlets in Urumqi is 1.29/ the classification standards of retail formats in China, the km2, the average density of the retail outlets in the Tian- Chinese national standard “Classification of Retail Formats” shan District is 27.58 km2, and the average density in the (GB/T18106-2000, GB/T18106-2004, GB/T18106-2010) has Dabancheng District is only 0.01/km2. Moreover, the been issued successively since 2000. Accordingly, the number of retail outlets per 1,000 persons in the Tian- retail centers are divided into 10 categories, which are, shan District is 786.52, while the value in Urumqi County namely, convenience stores, grocery stores, supermar- is only 84.66, showing a difference of 9.3 times between kets, discount stores, warehouse member stores, specialty the two areas. The spatial distribution of retail outlets in Urumqi: The application of POIs  1545

Table 1: The number and proportion of retail outlets in Urumqi

Outlets Scope description Number Proportion (%) (parcel)

Specialty stores Specific category of goods, reflecting professionalism, such as office 4,599 25.97 supplies, toy stores, stationery stores, and special trading places (pawn shops, auction houses) Clothing and footwear Mainly clothing, shoes, hats and leather goods stores 3,799 21.45 stores Home-building material Specializing in the sale of building materials, decoration, and household 2,838 16.02 markets items in kitchens and bathrooms, such as hardware, steel, and cement stores Convenience stores Instant products, newspapers and magazines, and daily necessities such 3,128 17.66 as 7 Eleven, Haolinju, and Night and day Shopping malls Concentrated by a variety of retail stores and service facilities, mainly in 1,094 6.18 the manner of counter sales Appliance electronics Consumer electronics and electrical products such as computers, mobile 753 4.25 stores phones, and digital goods Supermarkets Mainly in packaging products such as Carrefour, Aijia. 733 4.14 Integrated markets Agricultural and sideline products market, fruit market, aquatic seafood 584 3.30 market, and vegetable market Bird and insect markets Pets, flowers, birds, and fish markets 202 1.14 All retail outlets 17,710 100

3.3 Method as a point, so that a point-pattern analysis can be per- formed. As a result, the characteristics of human activities Point-pattern analysis tools discern spatial patterns and physical processes can be quantified and simulated. through the measurement and comparison of the attribute The point-pattern analysis is applied through four values and geographic location [41]. The spatial point pat- steps. First, we use the nearest-neighbor index to tern, which consists of a series of points, is driven by an describe the spatial agglomeration features of retail unknown generation mechanism that is irregularly scat- centers at different scales. Second, nearest-neighbor tered in the study area. Since the distribution of points is hierarchical clustering is applied to analyze the agglom- quite different, the point mode analysis focuses on the spa- eration characteristics of retail outlets. Third, location tial distribution characteristics of all points and their mutual entropy analysis is applied to examine the agglomeration relationship such as aggregation, randomization, and uni- features of retail outlets in different categories and form distribution, instead of focusing on single-point attri- circles. Last, we use the method of K function to divide butes. In the study, all types of retail outlets are abstracted the hierarchy of urban retail business circles in Urumqi.

Table 2: Density of retail outlets in Urumqi

Districts Population Area (km2) Number of Number of outlet/ Number of Population/km2 (10,000) outlets 1,000 persons outlets/km2

Tianshan 59.96 171 4,716 786.52 27.58 3,506.43 Shayibake 54.99 422.5 4,150 754.68 9.82 1,301.54 Xinshi 60.72 262.52 4,166 686.10 15.87 2,312.97 Shuimogou 28.74 277.56 2,140 744.61 7.71 1,035.45 ETD Zone 23.73 275.59 1,250 526.76 4.54 861.06 Dabancheng 4.16 4,759.18 52 125.00 0.01 8.74 Midong 28.35 3,407.42 1,183 417.28 0.35 83.20 Urumqi county 6.26 4,141 53 84.66 0.01 15.12 Total 266.91 13,716.77 17,710 663.52 1.29 194.59

Note: ETD Zone refers to the economic technological district. 1546  Zhao Yannan et al.

3.3.1 Nearest-neighbor index the total number of retail outlets in the Kth tread zone.

Therefore, NK–A indicates the ratio of the number of retail The nearest-neighbor index examines the distances outlets A in the Kth tread zone to the total number of between each point and its closest points [42]. The func- retail outlets in the Kth tread zone. Empirically, the tion of the nearest-neighbor index is calculated by the higher the NK–A value, the higher the specialization of following equation: retail outlets in the Kth tread zone.

r 2∑/dNi NNI ==α i (1) rr NA/ 3.3.4 Ripley’s K where rα refers to the average nearest-neighbor distance between i and its nearest neighbor, rr refers to the ex- Ripley’s K-function [47] and the weighted K-function are pected random distance, N refers to the number of points popular methods to determine whether a set of point in the research area, and α refers to the area of study. The features is clustered at multiple different distances. Since value of the nearest-neighbor index is usually above 0. the distribution pattern of point elements varies with the For an NNI value above 1, the spatial distribution pattern scale, Ripley’s K-function is used to reflect the specific indicates a scattered distribution. For an NNI value below distribution mode of point elements at different scales. 1, the spatial distribution pattern indicates a clustered It is one of the most commonly used methods for point- distribution. If the NNI = 1, the points in the study area pattern analysis. are randomly distributed. The Ripley’s K(d) represents the ratio of the average of the actual point elements to the density of the sample points within the distance d and the formula is as follows: - 3.3.2 Nearest neighbor hierarchical clustering n n wdij() K()=dA∑∑ ; (3) n2 The nearest-neighbor hierarchical clustering algorithm is i==11j an algorithm used to detect hot spots in the spatial dis- where n refers to the number of point elements, wij (d) tribution of feature points. The algorithm aims to find refers to the distance between i and j of within distance d, different pairs of clusters and to merge them by following and A refers to the area of the research area. Furthermore, the paths in the nearest-neighbor graph of the clusters Ripley’s K function measures whether the spatial distribu- [43].Bydefining the threshold of distance and comparing tion pattern of the actual observation point elements is a it with the distance of each set of point pairs, the point clustered, scattered, or random distribution. The related will be counted in the aggregate units when the distance indicators are calculated as follows: between one point and other points (at least one) is lower than the threshold. As a result, multilevel hot spots will Kd() L()=d − d (4) be obtained [44]. π

3.3.3 Location entropy analysis 4 Results Location entropy is a widely used indicator in judging the distribution of a certain spatial element. The application of the analysis is used to reflect the degree of specializa- 4.1 Distribution characteristics in each tion of a certain industry or enterprise [45,46]. This study tread zone uses location entropy to examine the specialization of retail outlets located in different categories and circles. The circle analysis adopts centers on the geometric center fi The parameter is defined to capture these features and is of a speci c analysis object. Then concentric circles with ff calculated as follows: di erent radii and the same spacing are formed. This method has obtained a certain level of consensus and is N nn (2) KA−−=/ KA K applied in our analysis. One of the issues in the applica- where NK–A refers to the location entropy value of retail tion of circle analysis is the determination of the radius. outlets A in the Kth tread zone, nK–A refers to the number In our study, 5 km is chosen as the threshold radius of retail outlets A in the Kth tread zone, and nK refers to referring to (1) in general, human needs can be resolved The spatial distribution of retail outlets in Urumqi: The application of POIs  1547 mostly within a radius of 5 km and (2) the idea of “5-km outlets in the fifth tread zone reaches the lowest level, 179 life circle” has been widely accepted in urban planning in outlets, and the number again increases in the sixth tread China. The circle is centered on the People’s Square; and zone, with 204 outlets. then based on the circle, six circles are expanded out- Comparing the various retail outlets with the outlets ward at the same interval. As a result, all points in the in the first tread zone, it is found that the number of retail outlets are divided into six tread zones in each convenience stores (Figure 3a), clothing and footwear buffer radius of 5, 10, 15, 20, and 25 km. The six tread stores (Figure 3c), specialty stores (Figure 4a), and bird zones are named as follows: the primary tread zone and insect markets (Figure 4b) decreases most in the (0–5km), secondary tread zone (5–10 km), third tread second tread zone. In the third tread zone, compared zone (10–15 km), fourth tread zone (15–20 km), fifth tread with the outlets in the second tread zone, the number zone (20–25 km), and sixth tread zone (25–30 km), from of specialty stores (Figure 4a), appliance electronics the inner city to the periphery of the city. The distribution stores (Figure 4c),home-building material markets characteristics in each tread zone are shown in Figure 2. (Figure 5a), and integrated markets (Figure 5c) decreases The following two initial observations can be made about the most. In the fourth tread zone, compared with the the spatial distribution characteristics. outlets in the third tread zone, the number of all outlets (1) In summary, the spatial distribution of retail out- increases except shopping malls (Figure 5b), and the lets in Urumqi shows a center directional sign (Figure 2). number of specialty stores increases the most (Figure 4a). In terms of the overall number of the retail outlets in each From the fourth tread zone to the sixth tread zone, the tread zone, the primary tread zone is where most (10,137) outlets including supermarkets (Figure 3b) and home- of the retail outlets are located, accounting for 57.24% of building material markets (Figure 5a) maintain relatively the total. In other words, the central tread zone is the stable levels. The rate of change in different retail outlets main distribution area for retail outlets. As the distance in different spheres shows their preference for location increases, the number of retail outlets first decreases and selection. The low rate of increase and the low rate of then increases. For instance, there are 5,258 outlets in the decrease indicate that their location adaptability is rela- second tread zone, 695 in the third tread zone, and 1,248 tively strong and that their sensitivity to location prefer- in the fourth tread zone. Moreover, the number of retail ence is weak. (2) Each tread zone is dominated by different retail outlets (Figure 6). The observations in terms of each tread zone are as follows. In the primary tread zone, the retail outlets with location entropies larger than 1 are clothing and footwear stores, specialty stores, and bird and insect markets. In the second tread zone, the retail outlets with location entropies larger than 1 are convenience stores, supermarkets, home-building material markets, and shopping malls. The third tread zone is dominated by convenience stores, supermarkets, shopping malls, and integrated markets. Moreover, the retail outlets with loca- tion entropies larger than 1 in the fourth tread zone are convenience stores, supermarkets, appliance electronics stores, home-building material markets, and integrated markets. Reviewing the fifth tread zone, it is noteworthy that appliance electronics stores, convenience stores, and supermarkets have high location entropies compared with other retail outlets. In the sixth tread zone, retail outlets including integrated markets, appliance electro- nics stores, and supermarkets have relatively high loca- tion entropy values. The number of retail outlets in the fifth and sixth zones is 383, accounting for 2% of the total. Thus, only the first four zones are discussed subsequently. In Figure 2: The distribution of overall retail outlets in tread zones. reviewing the first four zones, the following observations 1548  Zhao Yannan et al.

Figure 3: The distribution of retail outlets in tread zones ((a) convenience stores, (b) supermarkets, and (c) clothing and footwear stores).

Figure 4: The distribution of retail outlets in tread zones ((a) specialty stores, (b) bird and insect markets, and (c) appliance electronic stores). are made. The location entropy of convenience stores and markets peak in the third tread zone (10–15 km) and are supermarkets peaks in the third tread zone (10–15 km) the lowest in the primary tread zone (0–5km)(Figure 6). and is the lowest in the primary tread zone (0–5km). The location entropies of clothing and footwear stores, specialty stores, and bird and insect markets peak in the primary tread zone (0–5km) and are the lowest in the 4.2 The agglomeration characteristics of third tread zone (10–15 km). The location entropy of ap- retail outlets pliance electronics stores peaks in the fourth tread zone (15–20 km) and is the lowest in the third tread zone In summary, retail outlets show significant clustering (10–15 km). The location entropy of home-building mate- characteristics. The nearest-neighbor indexes of all retail rial markets peaks in the second tread zone (5–10 km) outlets are less than 1, and the Z-test values are less than and is the lowest in the primary tread zone (0–5km). −2.58, indicating an obvious aggregative distribution The location entropies of shopping malls and integrated under the 1% significance level (Table 3). The spatial distribution of retail outlets in Urumqi: The application of POIs  1549

Figure 5: The distribution of retail outlets in tread zones ((a) home-building material markets, (b) shopping malls, and (c) integrated markets).

Furthermore, since significant results were obtained Specifically, regarding convenience stores, there are with all the retail outlets (Figure 7), clustering character- 88 first-order hot spots and 6 second-order hot spots istics in different directions can be made with the appli- (Figure 8a), which are located in satellite plaza, the cation of the nearest-neighbor hierarchical clustering People’s Government, the intersection of West Jinsha- algorithm. The significant second-order hot spots can jiang Road and Karamay Road, the Midong District, east be observed in the retail outlets of supermarkets (Figure of East Ring Road, and the Black Mountain area. Addi- 8b), shopping malls (Figure 9a), bird and insect markets tionally, there is a third-order hot spot zone between the (Figure 9b), and appliance electronics stores (Figure 9c; South of Suzhou Road and north of Outer Ring Road. Table 4). Moreover, significant third-order hot spots Regarding the supermarkets, there are 15 first-order hot can be seen in the retail outlets of convenience stores spots, 10 of which are located in the inner city. Addition- (Figure 8a), clothing and footwear stores (Figure 8c), ally, two second-order hot spots are found in the north of home-building material markets (Figure 10a), specialty Xinyi Road and the inner city. Regarding the clothing and stores (Figure 10b), and integrated markets (Figure 10c). footwear stores, there are 80 first-order hot spots, eight

Figure 6: The location entropy of retail outlets in tread zones. 1550  Zhao Yannan et al. ) % ( cance level fi 81.48 1 18.76 1 76.60 1 39.43 1 42.70 1 26.64 1 30.30 1 value Signi 212.99 1 103.75 1 102.16 1 − − − − − − − − − Z − neighbor - index Nearest neighbor -

Figure 7: Nearest neighbor index of the overall retail outlets in Urumqi.

second-order hot spots, and 1 third-order hot spot area. Expect average nearest distance Regarding the shopping malls, there are 24 first-order hot spots between the north of Suzhou Road and south of Kashi Road and 2 second-order hot spots in the inner city. Regarding the bird and insect markets, there are 7 first-order and 2 second-order hot spots. Regarding the neighbor - home-building material markets, 56 first-order, 5 second- order, and 1 third-order hot spots are formed. Regarding the appliance electronics stores and integrated markets, there are 18 first-order hot spots and 1 second-order hot

distance spot in the inner city. Regarding the specialty stores, there are 129 first-order, 11 second-order, and 1 third- order hot spots.

4.3 The agglomeration at different scales

With the application of the K function, the spatial char- acteristics in various retail outlets at different scales can neighbor hierarchical clustering analysis of retail outlets - be observed. As a result, the distance range of the loca- tion layout for all retail outlets is obtained since the retail outlets are agglomerated at different scales. According to The nearest the analysis results, within 12 km, the actual function building material markets 2,838 58.45 291.53 0.20 - values in all retail outlets are larger than the upper limit Specialty storesClothing and footwear storesConvenience stores 3,799 4,599 3,128 29.60 45.44 86.96 225.48 226.84 306.09 0.13 0.20 0.28 Table 3: Category Number of outlets Average nearest Home Shopping malls 1,094 135.95 418.14 0.33 Appliance electronics stores 753 138.38 555.83 0.25 Supermarkets 733 222.59 536.41 0.42 Integrated marketsAll retail outlets 584 17,710 264.68 21.57 624.45 131.99 0.42 0.16 Bird and insect markets 202of 288.04 the confidence interval, 929.16 and the significance 0.31 test The spatial distribution of retail outlets in Urumqi: The application of POIs  1551

Figure 8: Nearest neighbor index of retail outlets in Urumqi (convenience stores, supermarkets, and clothing and footwear stores).

Figure 9: Nearest neighbor index of retail outlets in Urumqi (shopping malls, bird and insect markets, and appliance electronic stores). passes. In other words, within the observation range of (Figure 11a) also have large spaces for siting, which are 12 km, all retail outlets show obvious agglomeration agglomerated at distances of 6.22 and 6.20 km, respec- characteristics. tively. (3) The peak distances of the retail outlets, such as While observing the trend of the L(d) function curve, integrated markets, appliance electronics stores, shopping the clustering characteristics of all retail outlets are malls, and specialty stores, decrease, which are 5.51, 5.24, similar, which presents an inverted U. However, the 5.21, and 5.11 km, respectively (Figure 11b).Therefore,these spatial scales of the retail outlets vary. Regarding the retail outlets have smaller ranges of distance for siting. (4) tendency of the L-value curve in each retail outlet, a The clothing and footwear stores and bird and insect mar- decreasing trend can be observed. kets have the lowest peak distances. The appearances of the distance peaks differed. Among them, (1) convenience stores have the largest agglomeration value at a distance of 7.26 km (Figure 11a), which show that the convenience stores exhibit agglo- 5 Discussion meration characteristics in a larger scale and that the convenience stores can be sited in a larger space. (2) Home- Retail activity and location are closely related to the building material markets (Figure 11b) and supermarkets economic and social progress of cities. The research 1552  Zhao Yannan et al.

Table 4: The number of the first-order and second-order hot spot in causes of retail agglomeration. Retail agglomeration retail outlets reduces the uncertainty and search costs for consumers [51–53]. As a result, the agglomerated retail outlets are Category First-order Second-order attractive to consumers and thus increase the overall hot spot hot spot benefit of retail. Specialty stores 129 11 Our analysis shows that each category of retail out- Convenience stores 88 6 lets is agglomerated in space. According to Hotelling’s Clothing and footwear 80 8 Principle of Minimum Differentiation, retailers opening stores Home-building material 56 5 stores near competitors can expand sales areas and markets maximize profits [54]. Various categories of retail outlets Shopping malls 24 2 exhibit different service radii and agglomeration charac- Appliance electronics 18 1 teristics. Specifically, three retail outlets, including spe- stores cialty stores, convenience stores, and clothing and foot- Integrated markets 18 1 wear stores, have large numbers of first-order hot spots. Supermarkets 15 2 - Bird and insect markets 7 2 Four retail outlets, including home building material All retail outlets 435 38 markets, shopping malls, appliance electronics stores and integrated markets, have fewer hot spots. Supermar- kets are also significantly affected by population density. presented here suggests methodological approach which However, high density of the subject area has made it relates to a concern for the representativeness of POI spatially discretely distributed to achieve large service data. The distribution of retail outlets in Urumqi displays coverage. Moreover, bird and insect markets have large that the center is favored over the periphery. Retail out- ranges of services and, therefore, do not exhibit a signif- lets are more likely distributed in the inner city. This icant agglomeration. The agglomeration differences in characteristic is consistent with the distribution of the retail outlets are in line with Reilly’s law of retail gravita- population, in that there is high population density in tion [55] and Huff’s spatial interaction model [56]. Both the inner city. Customers in the inner city tend to have approaches calculate the power of retailers as being di- higher buying power than that of the periphery. Our rectly proportional to its size and inversely proportional study is consistent with the traffic and market principles to the distance from the consumer. that retailers tend to agglomerate in places with good In addition, the distribution of retail outlets is also traffic conditions and high population density [48–50]. constrained by the topographical condition and urban Moreover, the retail outlets in Urumqi show significant development strategy in Urumqi. The retail outlets in clustering characteristics. From an economic point of Urumqi show a directional distribution in the north- view, retail externality is considered one of the important west–southeast direction, which is the result of both

Figure 10: Nearest neighbor index of retail outlets in Urumqi (home-building material markets, specialty stores, and integrated markets). The spatial distribution of retail outlets in Urumqi: The application of POIs  1553

Figure 11: Results of Ripley’ K on retail outlets in Urumqi. the topographical condition and government interven- 6 Conclusion tion. Specifically, human activity is constrained by the topographical condition, which is bordered on three sides While the retail activity keeps developing, what remains (except the north side) by mountains. Additionally, under to be examined is the spatial structure under various the direction of the urban development strategy, which retail outlets. The rational layout of retail outlets is of is “controlling southwards and expanding northwards,” great significance in meeting the consumption needs of the population is increasingly agglomerated in the sur- urban residents, promoting urban economic develop- rounding area of that direction. ment, and optimizing the allocation of land resources. Our analysis shows additional potential issues of Confined to data availability, the previous studies had layout characteristics in Urumqi. For instance, retail out- difficulties reflecting the spatial layout and development lets compete extensively in the inner city and are of retail outlets. In recent years, due to the rapid devel- unevenly distributed in new districts, which appears to opment of big data, data sources for urban space studies be an unreasonable layout state according to central are increasingly abundant. This study uses POIs as the place theory. As Seim [57] showed, retail merchants data source to identify the distribution of retail outlets in must consider not only the location but also the distance Urumqi. The retail outlets in Urumqi present the fol- from competitors. To prevent these shortcomings, inter- lowing distribution characteristics: (1) the distribution vention from the outside (e.g., local government) is ne- of retail outlets density generally decreases and entropy cessary. Empirical experiences have shown that retail value increases from the inner city to the periphery, in- planning policies are very diverse and respond to so- dicating that retail outlets in the inner city are distributed ciety’s needs and national specificities [58–60]. densely and comprehensively. (2) Various categories of The current analysis suffers from several limitations, retail outlets display dissimilar agglomeration character- which indicate a potential direction for future research. istics and hot spots. Specialty stores, clothing and foot- The first limitation relates to the accuracy of the data wear stores, convenience stores, and home-building obtained with respect to the retail outlets, which must material markets have lower average nearest-neighbor dis- be further improved. The POI data used in this study tances. Significant third-order hot spots can be seen in the are obtained from Gaode and Tencent open data. After retail outlets of convenience stores, clothing and footwear correction and address matching, although the data are stores, home-building material markets, specialty stores, confirmed by a sampling survey, telephone inquiry, and and integrated markets. (3) Influenced by the topographical field visit, the data are massive and cannot be fully condition and urban development strategy, the retail outlets investigated and confirmed. Thus, data classification de- are agglomerated in different directions. (4) Various cate- viations might exist. Besides, there is a lack of analysis of gories of retail outlets tend to agglomerate at different scales. factors behind the generation of distributional character- We believe our analysis makes contributions to the istics. Thus, studies exploring factors such as road net- literature from the following three aspects. (1) The central work density and building density can be conducted in contribution of our analysis is to set out an alternative the future. data source for spatial analysis of retail outlets. The 1554  Zhao Yannan et al. development of information technologies has provided urbanization that is prevalent in Chinese cities, self- us with high-volume high-velocity data and also a variety organization will quickly fail. Therefore, intervention from of spatially and temporally referenced data. Grasping the the outside is necessary. Only a combination of self-orga- opportunities of the big data era is critical for urban nization and retail planning can promote the successful development [33]. In sum, the arrival of the big data era development of the retail industry and city development. has injected new blood into the study of commercial geography. (2) The second contribution is to remind Acknowledgments: This work was financially supported researchers to differentiate various categories of retail by “Light of West China” Program of CAS(2019-XBQNXZ- outlets in commercial studies. Relatively limited atten- A-005) and China Postdoctoral Science Foundation tion has been paid to explore the various retail outlets (2020M680660). categories. One of the advantages of POI data is that it helps to display precise distributions of various retail out- Author contributions: Zhao Yannan developed the ori- lets. In this regard, our analysis offers empirical evidence ginal idea for the study and conceived of and designed for understanding the different characteristics among re- the methodology together. Zhao Yannan drafted the tail outlet categories. By identifying the distribution rules manuscript, which was revised by Zhang Lu, Zhang of different retail outlets and summarizing the spatial Xinhuan. All authors have read and approved the final distribution of retail outlets in different categories, our manuscript. We would like to thank the anonymous analysis plays an important role in the development of reviewers and editor for their constructive comments on retail activities and the layout of retail facilities. (3) The an earlier version of this paper. third contribution is to enrich the empirical studies on commercial geography. Previous studies on retail outlets Conflict of interest: The authors declare no conflict of mainly focus on the eastern cities in China, for instance, interest. Beijing, Shanghai, Shenzhen, Changchun, etc. Located in the plain area, these cities have relatively better develop- Data availability statement: Some or all data, models, ment basis and conditions. 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