The accessibility evaluation between ecological and commercial spaces is important for “production-living-ecological” coordination. This article selects Tongzhou of Beijing as an example. Significance tests showed the commercial facilities in Tongzhou district followed an agglomerated distribution. Further exploration of the relationship between concentrated commercial and ecological spaces show the distribution of ecological space in Tongzhou district is unbalanced, which shows that the northern, western, and central regions of Tongzhou district cover a wide area, whereas the southeast region has a low level of accessibility. The coverage rate of ecological space of each street with 10 min walking is lower than 50% on average. The coverage rate of walking for 20 min varies greatly, and the sub-districts with high coverage rate are basically distributed along the north–south central axis and east–west central axis. Emerging commercial spaces have poor accessibility towards ecological space, which indicates that in the new commercial space, ecological space has not been considered in the planning. It also reflects that in the existing daily ecological management, managers tend to focus on the ecological remediation of the built area and ignore the ecological planning in the community construction stage of commercial space.
As an important component of the urban ecosystem, green space, lake, and other functional spaces play an important role in the ecological balance of the urban environment . They have many functions, including ecology, landscape, aesthetics, and rest, and have a profound impact on improving the living environment, protecting biodiversity, and maintaining the balance of the ecological system [2,3]. In 2017, China began to put special emphasis on the concept of ecological space in its policy documents. According to the Measures for The Control of the Use of Natural Ecological Space, “ecological space” refers to the territorial space with natural properties, and its main function is to provide ecological services or products. In the past decades, the construction of ecological space in China has made progress, but with the improvement of life quality, people’s demand for ecological space is no longer limited to quantity, and people pay more attention to whether it is convenient and fast to obtain ecological services . Accessibility has become one of the important indicators to evaluate the rationality of ecological space layout and service efficiency . Accessibility refers to the relative difficulty for residents to reach their destination from any point to overcome spatial resistance, which is often measured by time, distance, cost, and other indicators. It is an important aspect in the study of evaluating the rationality of urban public service facilities layout and service fairness. Scholars have studied the accessibility of ecological services from different perspectives, such as different modes of transportation, different users, and different types of parks [3,4,5].
Although this kind of research has been rich, its problems are very obvious, mainly because the starting point is often the point of residence, which leads to the lack of accuracy and classification research. When conducting accessibility research, it is inevitable to obtain the data of residents’ residence points, and residents’ life in urban areas is often ubiquitous. Therefore, how to effectively improve the policy recommendations often need to classify and screen residents’ residence so as to draw more effective geographical analysis conclusions. In order to solve this problem, this article believes that more attention should be paid to more micro space such as a commercial space. The demand for ecological services is particularly high in commercial spaces. For urban areas, commercial space often faces the problem of separation from ecological space . Due to the continuous acceleration of urbanization, commercial space often overstocks ecological space, which leads to the lack of ecological services in the urban commercial centre, which will have a negative impact on the regional ecological balance. For example, earlier studies [7,8] found that commercial centres are more likely to face the threat of heat island effect. In addition, the appearance of the city, residents’ rest and life will all receive negative effects, which is not conducive to the external exchange and residents’ health. The reasons are easy to understand. Looking at the history of urban development, it can be seen that commercial areas are not only the most important carriers of urban economic activities but also one of the most active areas in the city . The prosperity of commerce has long been found to be a driver of the land use. Commercial activities play important roles in urban expansion. Commercial spaces are areas where human activities are most active, and thus are the core parts of ecological governance . Commercial centres provide people with places to shop, relax, consume, entertain, and socialize and can also reflect the environmental pressure, citizen’s living conditions, and the evolution of the city. With the expansion, reconstruction, combination, and renewal of urban spaces, commercial space is also changing accordingly. Simple and one-level commercial centres can no longer meet people’s needs, and commercial areas with multi-centre and multi-level structures have become an inevitable developmental trend for urban commerce in modern cities. This also provides a reference for the priority of ecological space construction and governance.
As early as the 1920s, international scholars have already studied locations of urban commercial areas. Specifically, several representative theories have been developed to guide studies in this field, namely the central place theory , Losch landscape , and the Huff Gravity model for retail . Furthermore, inspired by the central place theory proposed by Christelle, a market hierarchy model, based on the location selection criteria to obtain the maximum profit with a series of assumptions, is established to divide the business hierarchy. Guided by this theoretical model, researchers have conducted a large number of empirical and in-depth studies. For example, Huff  studied the different functions of commercial districts and found that the functional nature of commercial districts was related to factors such as accessibility, development stage, and location. Miller and Tolle  used location-aware technology and open data to build a 3D city model to study the built environment and habits of local residents in US cities and to conduct a behavioural analysis of their interests, which was used to evaluate the current physical, mental, and social well-being of US citizens. Until recent years, some scholars have gradually combined the commercial space of production nature with ecological and living space and put forward the concept of “production-living-ecological” space, which has attracted the attention of scholars in developing countries such as China . With the rapid development of China’s urban economy, the continuous advancement of industrialization and urbanization, and the large-scale development and construction, the problems of urban ecological environment and the imbalance have become increasingly obvious. In May 2019, China issued the Guidelines on Establishing and Supervising the Implementation of the Territorial Space Planning System, which clearly stated the basic requirements for the coordination of “production-living-ecological” space. This marks that commercial space and other production space have become an important concern of urban environment governance; how to achieve its coordination has become a practical problem. This article takes the coordination of commercial space and ecological space as the research object and discusses it from the perspective of accessibility.
Based on above, the geospatial element of commercial space is the key to help realize the “production-living-ecological” coordination [17,18]. The accessibility evaluation between commercial space and ecological space plays an important role in further planning and ecological construction. This article selects a district of Beijing, the capital of China, as the research object to explore the interaction between the main commercial space and ecological space. Unlike earlier studies, this study area is one of the fastest growing commercial cities in China and is also the administrative centre of Beijing. In this particular area, the existing urban commercial space is no longer able to meet the needs of the current rapid development of Beijing, and the future development potential of Tongzhou, an urban fringe area, is generally favoured. Therefore, a study on the distribution of commercial space in Tongzhou, which is still in the process of transformation, can clarify the future commercial development and infrastructure development of this area. In the research process, a variety of open source network data are used as data source, and ArcGIS network analysis tools are used to study the accessibility between commercial space and ecological space. Then, based on the current pattern of commercial space, the planning suggestions of ecological space construction are given. This article aims to contribute to the “production-living-Ecological” coordination in China.
2.1 Data and studied area
Tongzhou district is located in the southeast of Beijing, at the northern end of the Beijing-Hangzhou Grand Canal. The geographical coordinates of the area are 39°36′–40°02′ north latitude and 116°32′–116°56′ east longitude. It is 36.5 km wide from east to west and 48 km long from north to south, covering an area of 905.95 km2. The whole area is located in the flood plain of the Yongding and Chaobai rivers, with a flat topography and an average altitude of 20 m. Tongzhou district has ten towns, one Hui township, and four streets. According to the construction plan for the sub-city centre announced by the Beijing Tongzhou People’s Government, an average of 430 million dollars will be invested in Tongzhou every day in 2021. In terms of transportation, a comprehensive transportation hub is under construction at the Beijing City Sub-centre Station, which will realize seven lines of interchange and five tracks of interchange in the future. The 49,000 m2 Dong Xia Yuan transportation hub, which is planned to introduce ten bus lines, will be the core interface between external and internal transportations in the city’s sub-centre when completed. This year, the city will also continue to build its road network, with the construction of a new 2.1 km-long east extension of Canal East Street, the main road in the city, and the renewal of the 6 km-long extension of Tongma Road. Not only that, but investment in education, healthcare, and ecological environment is also on the plan. The number of new schools in Beijing’s urban sub-centre will increase to four, medical and nursing home areas to eight, cultural and sports projects to eight, as well as ecological greening and urban renewal projects to three.
The present study focused on Tongzhou district in Beijing, and got access to POI data in December 2020 published on two open platforms, namely Baidu Maps and Gaode Maps. The data were cleaned, deduplicated, and classified, and data about the locations of shopping, catering, hotels (three main commercial facilities) were obtained. The geographic information system software was used to build a database of spatial data. After spatial matching, deduplication, and deletion of commercial outlets being difficult to recognize a total of 18,849 valid POI data were included for further analysis. For ecological space, we extracted vector data of ecological space patches and road network including parks, grassland, forests, wetlands, and lakes and rivers based on Open Street Map. Figure 1 was obtained by using ArcGIS to superimpose the above data.
2.2 Kernel density analysis
Kernel density analysis is a widely used non-parametric estimation method in spatial analysis. It can be used to calculate the density of elements in the surrounding areas. This method takes the position of a specific element point as the centre and distributes the attributes of the point within a specified threshold range (a circle with a radius of r). The density is the highest at the centre position, and it attenuates when the distance increases, and eventually, there is a point with a density of 0. By calculating each element point in the area with the same method and adding up the densities obtained at the same position, a smooth point element density plane can be calculated [19,20,21,22,23,24]. The formula is as follows:
where is the kernel function, is the threshold radius, is the number of point-like spots, and is the estimated distance from the point to the sample . In order to accurately and concisely reflect the distribution characteristics of commercial points, repeated trials were conducted to decide a threshold value and as a result, 2 km was selected as the distance threshold.
2.3 Network analysis method
Network analysis method is used to analyse all the entrances and exits of ecological space by creating network service area . The ecological accessibility of a region can be represented by the proportion of ecological space radiation area in the region, which can simulate people’s behaviour from commercial point more accurately. A basic network consists of four parts: source, chain, node, and resistance. Specifically, the main entrances and exits of ecological space (such as parks) are taken as the source, the bidirectional urban road is taken as the chain, the road intersection is taken as the node, and the walking time is taken as the resistance. In the research process, with 10, 20 and 30 min as time intervals, ArcGIS Network Analyst tool was used to create pedestrian road network datasets . The entrance and exit of ecological space such as park are selected as facility points, and the passing time, driving direction, and breakpoint of each road are set to generate a closed area enclosed by residents along the road network for a certain time in walking mode. Then the graded distribution range of the areas accessible to ecological services can be obtained.
2.4 Getis-Ord statistical index method
To reflect the distribution of urban POI hotspots, it is a common practice to use local characteristics as indicators to describe the spatial agglomeration degree of a certain attribute in the local area of each spatial unit. Commonly used local indices include Anselin Local Moran’s I and Getis-Ord [26,27]. However, Local Moran’s I only reflects the spatial agglomeration of observations with similar or different attributes but cannot indicate the levels of these attribute values. Therefore, the present study first used the Anselin Local Moran’s I index to calculate the spatial autocorrelation of attribute values of different features (with a 95% confidence level). In the next step, we applied the Getis-Ord statistical index to determine the changes in the commercial POI cluster hotspots in the city centre of Beijing to reflect the aggregation of elements with high and low values in the studied area. Getis-Ord considers each element in the dataset. By calculating the z score ( value) and p values, the spatial clustering position of high-value or low-value elements is obtained. The formula is provided below:
In this formula, is the element attribute value of the j spatial unit, is the total number of POIs, and represents the matrix of spatial weight. The local Getis-Ord statistics can be tested and can be expressed by the corresponding standardized scores (z value). A positive and significant value (z value) indicates that the value around the spatial unit is relatively large (above the average value), and thus there is a high degree of spatial agglomeration; on the contrary, a negative value indicates that the spatial unit has a low degree of spatial agglomeration.
2.5 Ripley’s K function
For different spatial scales, the spatial distribution characteristics of elements may differ. On a small scale, the distribution may show a tendency of agglomeration, whereas on a large scale, a random distribution or a uniform discrete distribution is more likely to occur. Ripley’s K function can analyse the distribution patterns of spatial point elements on different spatial scales . The formula is provided as follows:
In this formula, represents how large the studied area is, is the number of commercial outlets in each industry, is the distance threshold, and is the distance between a commercial outlet i and outlet J in a certain industry, within the distance threshold of . Besag proposed to use instead of , and take the linear transformation of the square root of to keep the variance stable. The formula is as follows:
The relationship between and can be used to test the spatial distribution pattern of each retail industry within the range of the distance . When = 0, the industry is randomly distributed, > 0 indicates that the industry follows an agglomerated distribution, whereas < 0 suggests that the industry follows a scattered distribution .
3.1 Descriptive statistics
From the results presented in Table 1, it can be seen that in terms of the commercial facilities in this study, the shopping category accounted for the highest proportion (59.92%), followed by the catering category, which accounted for about 1/3 of the total facilities (36.69%). The hotel category showed the lowest proportion (3.39%).
3.2 Spatial agglomeration characteristics
With spatial statistics tools, the POI point proximity analysis was conducted. The results are presented in Table 2. The average estimated distance between commercial facilities in Tongzhou district was 137.64 m, and the average observed distance was 26.48 m. The average estimated distance was higher than the average observed distance, indicating that the overall spatial distribution showed a tendency of agglomeration. The nearest neighbour ratio was 0.19, and the z score was −212.11. Significance tests showed that the commercial facilities in Tongzhou district followed an agglomerated distribution. Similarly, excluding theatres with too few points, the nearest neighbour index was calculated for three types of commercial facilities, namely catering, shopping, and hotels. It was found that the commercial facilities showed a tendency of spatial agglomeration (see Table 2). In addition, a smaller r value indicated a higher degree of agglomeration. By comparing the r values, it can be seen that the catering category showed the highest degree of agglomeration, followed by the shopping category, and the hotel category.
|Category||Expected mean distance (m)||Observed mean distance (m)||Average nearest neighbour ratio||z-score|
3.3 Overall commercial distribution
The overall density distribution trend can be observed by checking data of kernel density. According to the kernel density formula, there are two key parameters: the spatial weight function K and the distance threshold h. Research showed that the choice of the weight function has trivial effects on the results, whereas cautions should be warranted in deciding the distance threshold. In selecting the attenuation threshold, several tests were conducted (by testing multiple values of 500, 800, 1,000, 1,200, and 1,500 m). By observing the density distribution of different thresholds, it is found that choosing the thresholds of 500 and 800 m would result in a situation where the density distribution is concentrated on a small area near the element point. Instead, 1,000 m is ideal. The effects of choosing 1,200 and 1,500 m were basically the same, which can be used as references for overall observation. Therefore, 1,000 and 1,500 m were used for further analysis and comparison. The kernel map showed that there may be a spatial structure of one centre and multiple clusters (see Figure 2).
3.4 Overall accessibility of ecological space
The network analysis method was used to analyse the walking accessibility of ecological space in Tongzhou district and each sub-district (town) in Beijing (see Figure 3). On the whole, the spatial distribution of ecological space in Tongzhou district is unbalanced, which shows that the northern, western, and central regions of Tongzhou district cover a wide area, while the southeast region has a low level of accessibility. Statistically, the coverage rate of ecological space walking for 10, 20, and 30 min in Tongzhou district is 47.37, 57.13 and 77.06%, respectively, which is still far from the planning target of “embracing nature”. Among the administered streets (towns), Xinhua sub-district, Yuqiao sub-district and Zhongcang sub-district have higher pedestrian accessibility of ecological space than the average level, and the coverage of ecological space is relatively uniform. The coverage rate of walking 10, 20 and 30 min reached 58.95, 100, 100% and 58.99, 98.41, 100% and 58.97, 86.20 and 100%, respectively, significantly higher than other areas. In contrast, the coverage rate of 10 and 30 min walking accessibility in Yongledian town is significantly lower than the overall average level, which is 34.37 and 1.83%, respectively.
3.5 Comparison of ecological spatial accessibility of different sub-districts (towns)
ArcGIS was used to superimpose the accessibility analysis of ecological space in Tongzhou district of Beijing with sub-district boundaries, and calculate the proportion of park walking radiation range in each sub-district as an important basis for measuring the level of ecological space service in each sub-district (Figure 4). Overall, the coverage rate of ecological space of each sub-district with 10 min walking is lower than 50% on average. The coverage rate of walking for 20 min varies greatly, and the sub-districts with high coverage rate are basically distributed along the north–south central axis and east–west central axis. Sub-districts with a high coverage rate of 30 min walking are widely distributed and mainly concentrated in Tongzhou Station and its surrounding areas, and their coverage areas overlap with areas with a high coverage rate of 10 and 20 min ecological space walking. Although Tongzhou district was set as the sub-administrative centre of Beijing in 2015, the average coverage rate of the ecological space of the sub-districts under its jurisdiction is relatively high, and the coverage rate of the sub-districts is less than 30%, but its ecological space construction started relatively late and failed to achieve a balanced distribution in space. Among them, Xinhua sub-district, Yongshun sub-district, Lucheng sub-district, Taihu town, and Zhangjiawan town have multiple parks distribution, and the road network is dense; hence, the overall level of ecological space walking accessibility is high. Xiji town, Yongledian town, and other southeastern areas are the urban boundary. Their accessibility of ecological space showed a trend of polarization with Xinhua sub-district, Yongshun sub-district, Lucheng town, Taihu town, and Zhangjiawan sub-district.
4.1 Concentration and structure of commercial space
In order to help identify the drivers of distribution to help formulate ecological planning. We divided the sub-districts based on the surfaces and linked the commercial facilities to different sub-district surfaces. In the next step, we created a new field to calculate the area of each plot and calculated the density of commercial facilities on the plot (unit: unit/m2). As such, the sub-district density distribution map of commercial and entertainment facilities was obtained . First, the main urban area was divided into grids of 1,000 m × 1,000 m and 1,500 m × 1,500 m. The commercial centre was further identified by Getis-Ord hot spot analysis, and the global G was generated and tested. The observation value of general G was 0.000039, with a z score of 4.186342 and a p value of 0.000028. This indicated that the overall degree of spatial agglomeration or dispersion was not significant. However, the global irrelevance did not imply local irrelevance. It may be possible that the correlations were stronger in some areas and weaker in other areas. Therefore, the local Moran’s I and local G statistics were further used to observe the spatial clustering (see Figure 5).
The results of local Moran’s I showed that a higher value indicated a higher level of attribute similarity between the elements and the surrounding elements. The H–H areas in the Figure 5 showed similar attributes with the surroundings and passed the test, and there were the Wanda Plaza commercial centre in the northwest and the Xinhua sub-district commercial area in the west. The L–L area mainly covered the undeveloped area in the south of the intersection of Fenggangjian river and Beijing–Tianjin Expressway. The Getis-Ord value showed that there were three areas with obvious hot spots. One is the area of Tongzhou West Station–Tongzhou Beiguan Station, which is surrounded by Tongyan Expressway-North Canal-West Street of Canal-Tongchao Street-Beiyuan South Road. Near this area, there were Beijing Vocational College of Finance and Trade, Beijing Materials College, Beijing Open University, and Agricultural Broadcasting and Television School. The second is the Songzhuang town area, which is surrounded by the East Sixth Ring Road-Luyuan North Street-Xintong Road-Youdi Road-Tongyan Expressway. Near this area, there were Beijing University of Technology and Jiahua College of Beijing Technology and Business University. The third is the area close to the Funuodeng Life Plaza, which is surrounded by Jiachuang Road-Kechuang Fifth Street-Jinghai Road. Among them, the agglomerations of hotspots in Xinhua West Street, Xinhua North Road, and Beiyuan South Road were the most salient, indicating that the commercial facilities in these commercial clusters were densely distributed and displayed a greater impact on the surrounding areas.
4.2 Lack of accessibility of ecological services
Combined with the above analysis, it can be found that the commercial pattern of Tongzhou has a major commercial centre space in the northwest region, in which the accessibility of 10, 20, and 30 min is satisfactory overall. This means that in the main commercial centre, the government’s layout of the residents’ quality of life and ecological space is basically reasonable. However, in addition to the main commercial zone, we find two new commercial circles in Tongzhou area, which surround multiple commercial complexes. In the further hot spot analysis in Figure 6, we specifically identified relevant geographical locations.
The concentration of hot spots in these commercial clusters was relatively weak. According to the results of hotspot analysis, a total of five commercial centres were detected, namely Greenland Central Plaza, Wanda Plaza, Cathay Pacific Department Store, Gudermat Shopping Plaza, and Funuodeng Life Plaza. These centres constituted a commercial centre system in Tongzhou district, Beijing (Figure 6). Local autocorrelation analysis, however, showed that the high density of commercial facilities was often observed in shopping malls, whereas eye-catching high values tended to appear in commercial facilities near universities and cultural and creative industries. These findings may suggest that commercial facilities in the cultural, educational, and high-tech industries may have greater developmental potentials. Thus, it can be inferred that the Tongzhou West Station-Tongzhou Beiguan Station area may be a high-potential area in Tongzhou district for placing commercial facilities. By using kernel density analysis, cluster analysis, and hot spot analysis, we can confirm that the expansion trend of the current commercial space has been presented and the commercial pattern of “1 + N” has been formed. We can see from the comparison that although these emerging commercial spaces have become hot spots for future business expansion, their ecological space is lacking. In particular, the two commercial spaces in the western region performed poorly in our accessibility analysis above. This means that in the new commercial space, ecological space has not been considered in the planning. At the same time, it also reflects that in the existing daily ecological management, managers tend to focus on the ecological remediation of the built area, and ignore the ecological planning in the community construction stage of commercial space.
Geospatial element of commercial space is the key to help realize the “production-living-ecological” coordination. The accessibility evaluation between commercial space and ecological space plays an important role in further planning and ecological construction. This article selects a district of Beijing, the capital of China, as the research object to explore the interaction between the main commercial space and ecological space. With spatial statistics tools, the POI point proximity analysis was conducted. The average estimated distance between commercial facilities in Tongzhou district was 137.64 m, and the average observed distance was 26.48 m. The average estimated distance was higher than the average observed distance, indicating that the overall spatial distribution showed a tendency of agglomeration. The nearest neighbour ratio was 0.19, and the z score was −212.11. Significance tests showed that the commercial facilities in Tongzhou district followed an agglomerated distribution. In order to further explore the relationship between concentrated commercial space and ecological space, we calculate the accessibility of ecological space. Overall, the spatial distribution of ecological space in Tongzhou district is unbalanced, which shows that the northern, western, and central regions of Tongzhou district cover a wide area, whereas the southeast region has a low level of accessibility. The coverage rate of ecological space of each street with 10 min walking is lower than 50% on average. The coverage rate of walking for 20 min varies greatly, and the sub-districts with high coverage rate are basically distributed along the north–south central axis and east–west central axis. Through comparison, it can be found that the commercial pattern of Tongzhou has a major commercial centre space in the northwest region, in which the accessibility of 10, 20, and 30 min is satisfactory overall. However, emerging commercial spaces have poor accessibility towards ecological space. In particular, the two commercial spaces in the western region performed poorly in our accessibility analysis. The construction of the regional road network and the renewal of old neighbourhoods are still incomplete, such as the environmental improvement and upgrading of the South Street area in Tongzhou district, especially the improvement of the 18 half-cut hutongs. As a result, the accessibility of green spaces for residents in the area is not ideal compared to the accessibility of commercial spaces.
This means that in the new commercial space, ecological space has not been considered in the planning. At the same time, it also reflects that in the existing daily ecological management, managers tend to focus on the ecological remediation of the built area, and ignore the ecological planning in the community construction stage of commercial space.
Conflict of interest: Author states no conflict of interest.
 Dai X, Wang L, Tao M, Huang C, Sun J, Wang S. Assessing the ecological balance between supply and demand of blue-green infrastructure. J Environ Manag. 2021;288:112454.10.1016/j.jenvman.2021.112454Search in Google Scholar PubMed
 Lin YH, Hong CF, Lee CH, Chen CC. Integrating aspects of ecosystem dimensions into Sorghum and wheat production areas in Kinmen, Taiwan. Land Use Policy. 2020;99:104965.10.1016/j.landusepol.2020.104965Search in Google Scholar
 Zhou C, Zhang D, He X. Transportation accessibility evaluation of educational institutions conducting field environmental education activities in ecological protection areas: a case study of Zhuhai City. Sustainability. 2021;13(16):9392.10.3390/su13169392Search in Google Scholar
 Le Texier M, Schiel K, Caruso G. The provision of urban green space and its accessibility: Spatial data effects in Brussels. PLoS one. 2018;13(10):e0204684.10.1371/journal.pone.0204684Search in Google Scholar PubMed PubMed Central
 Li X, Huang Y, Ma X. Evaluation of the accessible urban public green space at the community-scale with the consideration of temporal accessibility and quality. Ecol Indic. 2021;131:108231.10.1016/j.ecolind.2021.108231Search in Google Scholar
 Leal Filho W, Icaza LE, Neht A, Klavins M, Morgan EA. Coping with the impacts of urban heat islands. A literature based study on understanding urban heat vulnerability and the need for resilience in cities in a global climate change context. J Clean Prod. 2018;171:1140–9.10.1016/j.jclepro.2017.10.086Search in Google Scholar
 Zhang WH, Chou LC, Chen M. Consumer perception and use intention for household distributed photovoltaic systems. Sustain Energy Technol Assess. 2022;51:101895.10.1016/j.seta.2021.101895Search in Google Scholar
 Duan X, Hu Q, Zhao P, Wang S, Ai M. An approach of identifying and extracting urban commercial areas using the nighttime lights satellite imagery. Remote Sens. 2020;12(6):1029.10.3390/rs12061029Search in Google Scholar
 You H, Yang X. Urban expansion in 30 megacities of China: categorizing the driving force profiles to inform the urbanization policy. Land Use Policy. 2017;68:531–51.10.1016/j.landusepol.2017.06.020Search in Google Scholar
 Li Q, Liu Q, Guo X, Xu S, Liu J, Lu H. Evolution and transformation of the central place theory in e-business: China’s C2C online game marketing. Sustainability. 2019;11(8):2274.10.3390/su11082274Search in Google Scholar
 Miller HJ, Tolle K. Big data for healthy cities: Using location-aware technologies, open data and 3D urban models to design healthier built environments. Built Environ. 2016;42(3):441–56.10.2148/benv.42.3.441Search in Google Scholar
 Potter RB. Correlates of the functional structure of urban retail areas: An approach employing multivariate ordination. Prof Geograp. 1981;33(2):208–15.10.1111/j.0033-0124.1981.00208.xSearch in Google Scholar
 Duan Y, Wang H, Huang A, Xu Y, Lu L, Ji Z. Identification and spatial-temporal evolution of rural “production-living-ecological” space from the perspective of villagers’ behavior–A case study of Ertai Town, Zhangjiakou City. Land Use Policy. 2021;106:105457.10.1016/j.landusepol.2021.105457Search in Google Scholar
 Yang Y, Bao W, Liu Y. Coupling coordination analysis of rural production-living-ecological space in the Beijing-Tianjin-Hebei region. Ecol Indic. 2020;117:106512.10.1016/j.ecolind.2020.106512Search in Google Scholar
 Hao FL, Wang SJ, Feng ZX, Yu TT, Ma L. Spatial pattern and its industrial distribution of commercial space in Changchun based on POI data. Geograph Res. 2018;37(2):366–78.Search in Google Scholar
 Bai Y, Chou L, Zhang W. Industrial innovation characteristics and spatial differentiation of smart grid technology in China based on patent mining. J Energy Storage. 2021;43:103289.10.1016/j.est.2021.103289Search in Google Scholar
 Tian Y, Jim CY, Wang H. Assessing the landscape and ecological quality of urban green spaces in a compact city. Landsc Urban Plan. 2014;121:97–108.10.1016/j.landurbplan.2013.10.001Search in Google Scholar
 Basit A, Amin NU, Shah ST, Ahmad I. Greenbelt conservation as a component of ecosystem, ecological benefits and management services: evidence from Peshawar City, Pakistan. Environ Dev Sustain. 2021;1–25.10.1007/s10668-021-01890-3Search in Google Scholar
 Jeong D, Kim M, Song K, Lee J. Planning a green infrastructure network to integrate potential evacuation routes and the urban green space in a coastal city: the case study of Haeundae District, Busan, South Korea. Sci Total Environ. 2021;761:143179.10.1016/j.scitotenv.2020.143179Search in Google Scholar PubMed
 Myers OM, Reyier E, Ahr B, Cook GS. Striped mullet migration patterns in the Indian river lagoon: a network analysis approach to spatial fisheries management. Mar Coast Fish. 2020;12(6):423–40.10.1002/mcf2.10137Search in Google Scholar
 Amaguaya FRO, Hernández JRH. Improvement of public transport routes with ArcGIS network analyst. Case study: Urban Center of Milagro, Ecuador. International Conference on Applied Human Factors and Ergonomics. Cham: Springer; 2020. p. 31–610.1007/978-3-030-51566-9_5Search in Google Scholar
 Shariati M, Mesgari T, Kasraee M, Jahangiri-Rad M. Spatiotemporal analysis and hotspots detection of COVID-19 using geographic information system (March and April, 2020). J Environ Health Sci Eng. 2020;18(2):1499–507.10.1007/s40201-020-00565-xSearch in Google Scholar PubMed PubMed Central
 Wang Z, Lam NS. Extending getis–ord statistics to account for local space–time autocorrelation in spatial panel data. Prof Geograp. 2020;72(3):411–20.10.1080/00330124.2019.1709215Search in Google Scholar
© 2022 Ying Xue, published by De Gruyter
This work is licensed under the Creative Commons Attribution 4.0 International License.