Abstract
This article considers Xiamen’s two districts as examples to help identify the supply and demand of green services by focusing on the spatial equity of green space in microregions. Based on network and concentration methods, the results show there are great differences in equity enjoyed by residents. The equity value of Tong’an is generally higher than that of Jimei district, and the value of the periphery of the central urban area is generally higher than that of the inner area. Jimei relies more on traffic facilities than Tong’an. This study finds that the carrying capacity of rail transit cannot be ignored in accessibility. By measuring under three travel modes, the distribution density of green space in Jimei and Tong’an is basically similar. The main reason why Jimei’s accessibility perform better than Tong’an district is traffic. Based on the service coverage rate (C) and the recreation opportunity index (R), this article obtained the regional evenness index, and found the overall characteristics of “high in the south and low in the north.” This study suggests differences in spatial performance at the micro level are often complex, and consideration of different explanatory variables such as population may provide directions for policymaking.
1 Introduction
As an important ecological space and public resource, urban public green space has multiple functions such as maintaining ecology, improving urban landscape, and enhancing residents’ living environment [1]. With the rapid development of economy, buildings and population are highly concentrated in Chinese cities, resulting in very limited land resources for greening construction. This phenomenon is particularly significant in China’s first-tier cities such as Beijing, Shanghai, and Shenzhen [2,3]. Urban public green space refers to the space open to the public in urban green space [1,4]. On the one hand, such green space is open to the public, and all urban residents have the right to use and the opportunity to access these green resources. On the other hand, public green space belongs to the category of urban green space, with a certain coverage rate of green vegetation, providing life, leisure, and entertainment for urban residents, maintaining urban ecological diversity and other functions. Therefore, urban public green space is both open and green.
It has long been pointed out by scholars that the process of large-scale urban expansion will lead to the widening of class and racial differences within cities and regions and deeply aggravate the inequitable and unjust spatial problems in cities [5]. Pirie first proposed the concept of spatial justice [6]. He reflected on the desirability and possibility of forming the concept of spatial justice based on social justice and territorial social justice [7]. This interdisciplinary integration of knowledge systems promotes the research in urban planning, architecture, and urban studies and gradually develops into a relatively independent theoretical system of spatial equity. Some scholars believe that the connotation of urban public green space equity involves the whole process of space development, resource allocation, and participation of planning rights [1,8,9]. Citizens’ rights lie not only in the equal enjoyment of the beneficial rights of green space but also in the democratic participation in the development of green space [10]. Schlosberg believes that green space justice must include a democratic process of procedural justice; that is, the public can participate in the production process of green space, and the quality of participation can be guaranteed [11]. Rutt proposed a new agenda, advocating the establishment of decision-making bodies composed of citizens, urban planners, and stakeholders in the development of urban green space. In addition, differentiated green space planning should be carried out according to the socio-economic characteristics of different regions, and the impact of green space on different groups should be monitored to adjust the planning content [12]. This actually aims to build a new public participation mechanism procedurally to promote the equity of urban public green space. It has been evidenced for the inequity effects due to lack of attention. For instance, the results of a study on this issue, using Tabriz as the study area, show that there is a huge difference between zone 2, which has the highest amount of green space per capita in Tabriz, and zone 9, which has the lowest, indicating a lack of spatial balance in the distribution of green space in Tabriz [13].
The theory of spatial equity was discussed in developing countries later than Western countries [14]. However, with the rapid advancement of urbanization in countries such as China, a series of spatial equity problems have been triggered along with the rapid economic development, resulting in an uneven distribution of public resources between urban and rural areas and within cities, widening income gap among classes, and spatial isolation and squeezing of vulnerable groups and low-income groups [14–16]. In this context, more scholars began to pay attention to the issue of regional spatial equity in developing countries. For example, an earlier study [17] based on questionnaire survey found that residents’ happiness and well-being is positively correlated with the urban green space in Kuala Lumpur (Malaysia). However, as a rapid developing city, green service is rarely seen as a public health intervention. Xing et al. [18] calculated the scale and quantity of public green facilities in urban communities and the current demand of residents by using the sample survey data of national community construction and found that there was a certain imbalance between supply and demand of public green service facilities in urban China. Yen et al. [19] conducted a sample survey on the users of urban parks in Phnom Penh, Cambodia by combining questionnaires and interviews and analyzed the safety perception, using intention and behavioral characteristics of residents of different ages on urban public green space resources. It pointed out that the urban green spaces is always facing constrained factors such as safety and such situation mainly caused by inequity both in social and spatial planning aspects. Zhang and Zhou [20], considering the practice of public participation in the planning and management process of parks in Beijing as an example, analyzed the problems existing in the planning and management of urban green space in China and summarized the necessity of improving public participation in the planning of green space in China. With the progress of the current technology, the point of interest (POI) data provided by the map application programming interface (API) port shows high accuracy and can give real-time feedback to the real time cost of points to public green space in various parts of the city [20–22]. Therefore, such improved accessibility measurement method is considered to be more reliable than the traditional data method, and can be extended to any city and region [23]. However, research combined POI with green space is often neglected, which is of great value in achieving reliable evaluation. To fill the gap, different from other studies in the past, we chose the city Xiamen in China, as our research area. Since 2018, Xiamen has proposed to build “15 min Living Circle” and the full coverage of urban green space, but this strategy has not received corresponding evaluation. Whether the accessibility of public green services has improved is still an unevaluated issue, mainly due to the lag of official geographic data and demographic data. Therefore, this study aims to combine with the timely POI data mentioned above to bridge one of the gaps. In this strategic context, this article customizes a method for fine evaluation of green space accessibility by using network map service to realize comparative analysis of two districts with different population, gross domestic product (GDP), and transportation facilities in the same city. The purpose of this study is to achieve a critical assessment of the status quo by comparing the differences between different regions within the same local jurisdiction and to provide policy guidance for improving the quality of life of citizens. Earlier studies focused too much on the city level and provincial level due to data limitation, which lacked practical significance for residents’ life in the micro context.
2 Methods
2.1 Study area overview
Xiamen is located in the southeast of Fujian province, connected with Zhangzhou and Quanzhou, and located in the middle of the Golden Triangle of Southern Fujian. It is an important central city along the southeast coast. The land area of Xiamen is 1699.39 km2, and the sea area is about 390 km2. The research scope of this study is Jimei district and Tong ‘an district outside the island, covering a total area of 933.59 km2, as shown in Figure 1. Due to the urbanization process, the annual GDP and population of the two regions increased by 143 and 117% from 2015 to 2019. Data in this article are mainly the POI data of public service facilities, which contains five classes of education, health care, travel, scenic spot, and life service. These data are closely linked with residents’ quality of life and can be a good characterization of public service facilities distribution and social economic development [24,25].
Division of spatial units in the research area.
2.2 Approaches and tools
The equity assessment of green space allocation in this study is carried out based on the following aspects: (1) accessibility: based on the accessibility analysis, this study determines the service coverage area of green space in spatial units, (2) combined with the total area of space units (S), this study further determines the green space service coverage rate (C) and green space recreation opportunity index (R), and (3) based on the green space service coverage (C), this study determines the population ratio (P) and per capita green space location entropy (L) by combining population data.
2.2.1 Accessibility assessment based on network method
In this study, network analysis is used to analyze the accessibility. The park entrance in the form of point elements is considered as the center, the road vector data in the form of line elements is considered as the connection, the road intersection in the form of point elements is considered as the node, and the resistance value of traffic light waiting time is set as 30 s. The average speeds of walking, running, and cycling were 5, 9, and 12 km/h, respectively, and the corresponding time resistance values were calculated according to the geometric length of the road to build the network data set. The average speed values for the three modes of travel used in the calculation and comparison of data on the accessibility of green services in the study area based on network analysis refer to the default travel speeds for the respective modes of travel in the navigation function of the online map (in particular, the average travel speeds for different traffic forms on the same route were selected). The travel speeds of 5, 10, and 15 min were set as the time threshold standards of accessibility, and were calculated in ArcGIS 10.6 to obtain the accessibility results of park green space [26,27]. Based on ArcGIS 10.6 platform, overlay analysis method was used to overlay the population distribution results and the accessibility results of park green space, providing support for subsequent statistical analysis.
2.2.2 Concentration degree
The concentration degree of green space resources refers to the proportion (%) of the amount of green space resources gathered on 1% of the land area of a certain region in the whole region [28], and the calculation formula is as follows:
Based on this concept, the evaluation criteria for equity of green space resource allocation are as follows: when
The population agglomeration degree (PAD) is a term used to describe the proportion of the population (%) in a region (%) occupied by a certain area. According to Cong and Zou [29], it is calculated as follows:
PAD represents the population concentration degree of the region, reflecting the proportion of the population gathered in the geographical area of 1% of Xiamen. Among which,
2.2.3 Measurement of regional evenness
Green service coverage rate (C) refers to the ratio of the green service coverage area to the total sub-district area within the space unit. According to Yang et al. [30], its formula is as follows:
Among which,
Recreation opportunity index (R) reflects the degree of residents’ choice of recreation opportunity. Under the same green space coverage (C), the more green space number there is, the higher the green space recreation opportunity index (R) will be, and the calculation formula based on Byczek et al. [31] is as follows:
Among which,
Regional evenness (G) is jointly determined by green service coverage rate (C) and recreation opportunity index (R). The calculation formula of regional equality (G) is as follows:
3 Results
3.1 Green space allocation equity
3.1.1 Spatial analysis
This study took residential points as the center, searched the number of accessible green spaces within 800 m, and counted the sum of service effectiveness of all accessible green spaces. The equity index of each residential site is calculated. At the residential area level, the residential spots in Jimei district and Tong ‘an district are classified into different spatial units according to the administrative division of sub-districts. The corresponding equity index value can be divided into four categories: no green space reachable, high equality, moderate equality, and low equality.
The results show that less than 40% of residents in the central urban areas of Jimei and Tong ‘an can reach an urban green space within 800 m. Residential areas that cannot enjoy green space services within 800 m are mainly distributed along the outer ring of the central urban area and concentrated in the northern area. There are great differences in the equity of residents. The equity value of Tong ‘an district is generally higher than that of Jimei district, and the equity value of the periphery of the central urban area is generally higher than that of the inner area.
3.1.2 Green space and traffic network
This study draws a buffer zone of 1 km based on the distribution of main traffic arteries and subway stations in the research area by referring to previous research experience and realistic factors (Figure 2) [32]. In general, 45.4% of green spaces are located within the buffer area of 1 km of major urban traffic arteries. The research shows that the dependence of green space in Jimei district on traffic facilities is higher than that in Tong ‘an district. The carrying capacity of urban rail transit cannot be ignored from the overall ecological environment of residents’ choice of means of transportation. Therefore, 70 subway stations in two different urban areas were located in this study, and the same distance of 1 km was used as the radius of buffer zone. The results showed that 52% of the city’s green space is in the subway buffer zone.
Distribution of green space and traffic network.
3.1.3 Accessibility under three travel modes
After computing the traffic data obtained by AMAP, geographic information system (GIS) spatial expression is carried out, and the following conclusions are drawn: under the three mobility modes, 33% of the communities in Jimei district could reach more than one green space within 5 min, 41% of the communities could reach more than one green space within 10 min, and 64% of the communities could reach more than one green space within 15 min. In Tong ‘an district, 21% of the communities can reach more than one green space within 5 min, 37% can reach more than one green space within 10 min, and 57% can reach more than one green space within 15 min under the three mobility modes (Figure 3). The average time cost of residential community to green space is concentric circle, which increases gradually from the center to the periphery. The accessibility of the community located in the core area is generally better than that of the peripheral area, among which Guankou town and Xinmin town are the best. The comparative analysis shows that the distribution density of green space in Jimei district is basically similar to that in Tong ‘an district, and the accessibility of green space in Jimei district is higher than that in Tong ‘an district due to traffic factors.
Accessibility of green space in (a) 5, (b) 10, and (c) 15 min.
3.1.4 Impacts of population density on accessibility
According to the analysis results of green space accessibility of residential communities under the above three travel modes, it can be concluded that the accessibility of residential communities in the central area of Tong ‘an district and Jimei district is generally better than that of peripheral areas, and the residential communities of Xinglin Station and Xiamen North railway station have the best performance in green space accessibility. A total of 5% of the residential areas in the peripheral areas have no accessible green space within 30 min under the three ways of passage. Community green space should be added to make up for the defects. There is a significant difference in green space accessibility of residential communities in the central and peripheral areas, mainly due to the high degree of population concentration in the central area, which is shown in Figure 4. In addition, the road network and bus lines in the central area are denser. The outer northern region is the new urban area, and the infrastructure construction such as road network is being improved. The outer northern region has complex terrain, less suitable land for construction, and relatively backward development.
Kernel density map of population distribution.
3.2 Regional evenness
Under the three time thresholds, the service coverage rate (C) of TA11 (Susong Avenue) in Tong ‘an district and JM2 (Wenbin road) in Jimei district is higher. However, TA14 of Yunyang road in Tong ‘an district and JM4 of Jimei North Road in Jimei district are relatively low (Table 1). The two districts, therefore, are characterized by “high north and low south.” Under the three time thresholds, TA9 of Tong ‘an district and JM1 of Jimei district are higher, whereas TA13 of Datong street of Tong ‘an District and JM8 of Houxi street of Jimei district were lower (Table 2), showing the characteristics of “high in the south and low in the north.” Through comprehensive green service coverage rate (C) and recreation opportunity index (R), we obtained the regional evenness index of Jimei and Tong ‘an districts as shown in Figure 5. Susong Avenue and Wenbin road are higher, whereas Yunyang road and Jimei North Road are lower.
Green space service coverage (C)
| Code | Riding 5 min | Riding 10 min | Riding 15 min | Running 5 min | Running 10 min | Running 15 min | Walking 5 min | Walking 10 min | Walking 15 min |
|---|---|---|---|---|---|---|---|---|---|
| TA1 | 0.964552 | 0.991212 | 1 | 0.721641 | 0.742969 | 0.851235 | 0.60773 | 0.66412 | 0.785821 |
| TA2 | 0.645789 | 0.665789 | 0.970454 | 0.482631 | 0.586421 | 0.726363 | 0.406448 | 0.5284 | 0.670545 |
| TA3 | 0.426977 | 0.426977 | 0.426977 | 0.291582 | 0.291582 | 0.291582 | 0.135555 | 0.252788 | 0.269175 |
| TA4 | 1 | 1 | 1 | 0.621354 | 0.75 | 0.854124 | 0.323274 | 0.650216 | 0.688488 |
| TA5 | 1 | 1 | 1 | 0.75 | 0.851237 | 0.963258 | 0.631613 | 0.767983 | 0.889236 |
| TA6 | 1 | 1 | 1 | 0.75 | 0.75 | 0.75 | 0.453161 | 0.650216 | 0.692366 |
| TA7 | 0.897521 | 0.997521 | 1 | 0.757769 | 0.847769 | 0.85 | 0.638155 | 0.744977 | 0.984681 |
| TA8 | 0.756828 | 0.756828 | 1 | 0.452137 | 0.631145 | 0.85 | 0.380767 | 0.547174 | 0.784681 |
| TA9 | 0.982323 | 0.998533 | 1 | 0.834091 | 0.84868 | 0.85 | 0.70243 | 0.735767 | 0.784681 |
| TA10 | 0.477058 | 0.577058 | 0.877058 | 0.419352 | 0.469352 | 0.839352 | 0.353157 | 0.406907 | 0.774852 |
| TA11 | 0.106955 | 0.098285 | 0.655425 | 0.046259 | 0.328457 | 0.539883 | 0.038957 | 0.284757 | 0.498395 |
| TA12 | 0.527649 | 0.527649 | 0.982531 | 0.352169 | 0.424884 | 0.834278 | 0.296579 | 0.368355 | 0.770167 |
| TA13 | 0.04025 | 0.08025 | 0.124424 | 0.012365 | 0.022225 | 0.125346 | 0.010413 | 0.019268 | 0.115714 |
| TA14 | 0.69482 | 0.69482 | 0.952145 | 0.425954 | 0.575338 | 0.806931 | 0.358717 | 0.498791 | 0.744922 |
| JM1 | 1 | 1 | 1 | 0.768952 | 0.858564 | 0.965421 | 0.647573 | 0.744336 | 0.891233 |
| JM2 | 1 | 1 | 1 | 0.632584 | 0.865235 | 0.985477 | 0.532731 | 0.750119 | 0.909747 |
| JM3 | 1 | 1 | 1 | 0.62579 | 0.756452 | 0.884524 | 0.527009 | 0.655809 | 0.816552 |
| JM4 | 0.9 | 1 | 1 | 0.67 | 0.75 | 0.756589 | 0.56424 | 0.650216 | 0.698449 |
| JM5 | 0.978531 | 1 | 1 | 0.732825 | 0.756532 | 0.795221 | 0.617149 | 0.655879 | 0.734112 |
| JM6 | 1 | 1 | 1 | 0.632585 | 0.748663 | 0.845224 | 0.532731 | 0.649057 | 0.780272 |
| JM7 | 0.942487 | 0.954275 | 1 | 0.703989 | 0.71342 | 0.75 | 0.592865 | 0.618502 | 0.692366 |
| JM8 | 0.9 | 1 | 1 | 0.57 | 0.75 | 1 | 0.480025 | 0.650216 | 0.923154 |
| JM9 | 1 | 1 | 1 | 0.75 | 0.89 | 1 | 0.631613 | 0.771589 | 0.923154 |
| JM10 | 1 | 1 | 1 | 0.65224 | 0.9632 | 1 | 0.549284 | 0.83505 | 0.923154 |
| JM11 | 1 | 1 | 1 | 0.753216 | 0.953213 | 1 | 0.634321 | 0.826392 | 0.923154 |
| JM12 | 1 | 1 | 1 | 0.736646 | 0.853214 | 1 | 0.620366 | 0.739697 | 0.923154 |
| JM13 | 0.9 | 1 | 1 | 0.67 | 0.75 | 0.801235 | 0.56424 | 0.650216 | 0.739663 |
Green space recreation opportunity index (R)
| Code | Riding 5 min | Riding 10 min | Riding 15 min | Running 5 min | Running 10 min | Running 15 min | Walking 5 min | Walking 10 min | Walking 15 min |
|---|---|---|---|---|---|---|---|---|---|
| TA1 | 63.87487 | 62.15684 | 130.7005 | 47.76256 | 48.07486 | 80.70049 | 24.98651 | 26.13768 | 31.44853 |
| TA2 | 61.08411 | 61.08411 | 144.0844 | 45.97442 | 46.93825 | 94.08443 | 24.05106 | 25.51972 | 30.70501 |
| TA3 | 66.61053 | 72.61053 | 143.6491 | 50.13383 | 55.79538 | 93.64913 | 26.22701 | 30.33523 | 36.49897 |
| TA4 | 43.23724 | 43.23724 | 86.47448 | 32.54213 | 33.22436 | 36.47448 | 17.02409 | 18.06365 | 21.73396 |
| TA5 | 44.69052 | 42.18421 | 89.38104 | 33.63593 | 32.41519 | 39.38104 | 17.5963 | 17.62372 | 21.20464 |
| TA6 | 17.7804 | 17.7804 | 35.56079 | 13.38226 | 13.66281 | 30.37291 | 7.000796 | 7.428294 | 8.937631 |
| TA7 | 32.07872 | 32.07872 | 64.2825 | 24.14376 | 24.64993 | 54.90447 | 12.63057 | 13.40184 | 16.12493 |
| TA8 | 8.565666 | 8.565666 | 21.33036 | 6.446873 | 10.58203 | 18.21852 | 3.372617 | 5.753312 | 6.922314 |
| TA9 | 31.84883 | 42.84883 | 64.81524 | 23.97074 | 32.9259 | 55.35949 | 12.54005 | 17.90138 | 21.53872 |
| TA10 | 72.18152 | 72.18152 | 72.18152 | 54.32678 | 55.46572 | 61.65112 | 28.42051 | 30.15599 | 36.28332 |
| TA11 | 22.60221 | 20.77012 | 138.5077 | 21.27089 | 25.30307 | 118.3012 | 11.12765 | 13.75695 | 16.55219 |
| TA12 | 23.03652 | 23.03652 | 65.00201 | 19.09615 | 23.47209 | 55.51901 | 9.989961 | 12.76147 | 15.35445 |
| TA13 | 0.006413 | 0.006413 | 3.186993 | 0.005413 | 0.006413 | 3.186993 | 0.002832 | 0.003487 | 0.004195 |
| TA14 | 6.695945 | 6.695945 | 14.2916 | 4.746681 | 19.81014 | 12.20663 | 2.483179 | 10.77052 | 12.95895 |
| JM1 | 4.072546 | 4.072546 | 8.145091 | 3.571946 | 17.97916 | 6.956823 | 1.868628 | 9.775038 | 11.7612 |
| JM2 | 81.50404 | 81.50404 | 163.0081 | 60.39721 | 68.14819 | 139.2272 | 31.5962 | 37.05128 | 44.57965 |
| JM3 | 22.40208 | 22.40208 | 44.80416 | 18.22248 | 20.31721 | 38.26779 | 9.532906 | 11.0462 | 13.29066 |
| JM4 | 19.93779 | 19.93779 | 39.87557 | 10.04774 | 12.48623 | 34.05822 | 5.256375 | 6.788603 | 8.167963 |
| JM5 | 20.92447 | 20.92447 | 41.84894 | 13.87301 | 15.65526 | 35.7437 | 7.257524 | 8.51156 | 10.241 |
| JM6 | 63.87487 | 62.15684 | 130.7005 | 1.69827 | 8.82428 | 111.6329 | 0.888433 | 4.797646 | 5.772469 |
| JM7 | 66.61053 | 68.30213 | 143.6491 | 39.8643 | 50.94451 | 108.7197 | 20.85461 | 27.69787 | 33.32573 |
| JM8 | 1.443437 | 1.443437 | 2.886874 | 41.57163 | 1.076616 | 2.184908 | 21.74778 | 0.585342 | 0.704277 |
| JM9 | 44.69052 | 42.18421 | 89.38104 | 32.90085 | 38.46394 | 67.64733 | 17.21175 | 20.91234 | 25.16147 |
| JM10 | 31.84883 | 28.95109 | 64.81524 | 15.89135 | 26.59375 | 49.0549 | 8.313403 | 14.45868 | 17.3965 |
| JM11 | 50.55243 | 50.55243 | 101.1049 | 19.87685 | 37.70554 | 76.5204 | 10.39838 | 20.50001 | 24.66536 |
| JM12 | 22.60221 | 20.77012 | 138.5077 | 12.54977 | 15.49181 | 104.8285 | 6.565287 | 8.422693 | 10.13408 |
| JM13 | 10.8964 | 10.8964 | 21.79281 | 8.106041 | 9.1273 | 16.49371 | 4.240594 | 4.962395 | 5.970692 |
Evenness analysis of Tong’an and Jimei districts under three time thresholds. (a) 5 min, (b) 10 min, and (c) 15 min.
4 Discussion
This study improves the theoretical system for evaluating the fairness of parkland allocation in high-density urban areas. Based on the traditional evaluation of park supply from the perspective of supply and demand, the study introduces connectivity factors into the fairness measurement of parkland allocation, and reveals the influence of roads, travel modes, and travel time on the fairness measurement of parkland allocation [33,34]. At the same time, the combination of POI data and traditional yearbook statistics for population measurement breaks through the limitations of a single data source and improves the accuracy of park green space equity measurement. The spatial equity dimension is based on the geographical parity dimension with the addition of the population factor, which has commonalities and focuses on both, and is in line with the previous findings that per capita indicators should be considered when evaluating the accessibility of urban parks [35]. In addition to the supply and demand factor, the equity of parkland allocation is also influenced by the connectivity factor, and the accessibility of parkland varies considerably by mode of transport.
Overall, the poor accessibility of urban recreational green spaces for residents in the urban fringe area has a direct impact on the ecological environment and the urbanization of residents’ lifestyles in the area and is a problem that cannot be ignored [36]. This current situation reflects the fact that although some areas of the main city of Xiamen have urbanized their population, the urban infrastructure to match the urbanization is not up to standard, and the scale of the city is not in harmony with the quality of urbanization. Xiamen’s urban green space system should develop towards balance and improve the accessibility of recreational green spaces by optimizing the spatial layout of road networks and recreational green spaces in the fringe areas. Although big data and GIS spatial technology can intuitively reveal the characteristics of urban space at the macro level, future research needs to be supplemented by micro-level social surveys, field interviews on residents’ park use behavior and the use needs of various groups to obtain more detailed and informative data to make up for the shortcomings of big data and GIS spatial technology in expressing the subjective wishes of people’s needs. The default three time thresholds in this study are of equal importance for different study units, and the next step can be to determine the most necessary time thresholds for the function of each study section in high-density urban areas and the supply of green space within residential areas.
5 Conclusion
Based on the network analysis and agglomeration method, this study measured the equity of park green space allocation in two different areas of Xiamen, namely Jimei district and Tong’an district, and analyzed the accessibility differences of park green space in these two areas based on the comparison of population density and traffic network. The following conclusions were obtained.
(1) Less than 40% of residents in the central urban areas of Jimei and Tong’an districts can reach an urban park within 800 m. The residential areas that do not enjoy park services within 800 m are mainly located along the outer ring of the central urban areas, indicating that there are large differences in the equity of green space enjoyed by residents, with equity values generally higher in Tong’an district than in Jimei district, and equity values generally higher in the periphery of the central urban areas than in the inner areas.
(2) This study was conducted by locating 70 metro stations in two different urban areas, again using a distance of 1 km as the buffer zone radius. The results show that 52% of the urban park green spaces are located in the buffer zone of the metro stations, with the park green spaces in Jimei district being more dependent on transport facilities than in Tong’an district.
(3) The results of the analysis of the accessibility of park green spaces in Jimei and Tong’an districts under three different modes of transport, namely walking, cycling, and public transport, show that the spatial distribution of park green spaces in Jimei and Tong’an districts is basically similar in density, and the main influencing factor for the accessibility of park green spaces in Jimei district over Tong’an district is the transport factor.
(4) The index of geographical parity between Jimei and Tong’an districts is obtained by combining the green space service coverage ratio (C) and the green space recreation opportunity index (R), with the overall characteristics of “high in the south and low in the north.” This indicates that both Jimei and Tong’an districts have serious unbalanced population distribution, a mismatch between the supply and demand of parkland, and spatial heterogeneity in the fairness of allocation.
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Author contributions: QY and HZ designed the study and QY carried them out. JH prepared the manuscript with contributions from all co-authors.
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Conflict of interest: Authors state no conflict of interest.
References
[1] You H. Characterizing the inequalities in urban public green space provision in Shenzhen, China. Habitat Int. 2016;56:176–80.10.1016/j.habitatint.2016.05.006Search in Google Scholar
[2] Hong W, Guo R, Tang H. Potential assessment and implementation strategy for roof greening in highly urbanized areas: A case study in Shenzhen, China. Cities. 2019;95:102468.10.1016/j.cities.2019.102468Search in Google Scholar
[3] Song M, Ma X, Shang Y, Zhao X. Influences of land resource assets on economic growth and fluctuation in China. Resour Policy. 2020;68:101779.10.1016/j.resourpol.2020.101779Search in Google Scholar
[4] Fan P, Xu L, Yue W, Chen J. Accessibility of public urban green space in an urban periphery: The case of Shanghai. Landsc Urban Plan. 2017;165:177–92.10.1016/j.landurbplan.2016.11.007Search in Google Scholar
[5] Zhao P, Cao Y. Commuting inequity and its determinants in Shanghai: New findings from big-data analytics. Transp policy. 2020;92:20–37.10.1016/j.tranpol.2020.03.006Search in Google Scholar
[6] Pirie GH. On spatial justice. Environ Plan A. 1983;15(4):465–73.10.1068/a150465Search in Google Scholar
[7] Demeterova B, Fischer T, Schmude J. The right to not catch up—transitioning European territorial cohesion towards spatial justice for sustainability. Sustainability. 2020;12(11):4797.10.3390/su12114797Search in Google Scholar
[8] Xu C, Haase D, Pribadi DO, Pauleit S. Spatial variation of green space equity and its relation with urban dynamics: A case study in the region of Munich. Ecol Indic. 2018;93:512–23.10.1016/j.ecolind.2018.05.024Search in Google Scholar
[9] Ma F. Spatial equity analysis of urban green space based on spatial design network analysis (sDNA): A case study of central Jinan. China Sustain Cities Soc. 2020;60:102256.10.1016/j.scs.2020.102256Search in Google Scholar
[10] Juntti M, Costa H, Nascimento N. Urban environmental quality and wellbeing in the context of incomplete urbanisation in Brazil: Integrating directly experienced ecosystem services into planning. Prog Plan. 2021;143:100433.10.1016/j.progress.2019.04.003Search in Google Scholar
[11] Schlosberg D. Theorising environmental justice: the expanding sphere of a discourse. Environ politics. 2013;22(1):37–55.10.1080/09644016.2013.755387Search in Google Scholar
[12] Rutt RL, Gulsrud NM. Green justice in the city: a new agenda for urban green space research in Europe. Urban Forestry Urban Green. 2016;19:123–7.10.1016/j.ufug.2016.07.004Search in Google Scholar
[13] Darskhan R, Pashachini H. Location and analysis of green space distribution with spatial justice approach (Case Study: Tabriz Metropolis). J Geogr Environ Stud. 2021;9(36):7–20.Search in Google Scholar
[14] Gao J, Chen W, Yuan F. Spatial restructuring and the logic of industrial land redevelopment in urban China: I. Theoretical considerations. Land Use Policy. 2017;68:604–13.10.1016/j.landusepol.2017.07.021Search in Google Scholar
[15] Duan Q, Tan M, Guo Y, Wang X, Xin L. Understanding the spatial distribution of urban forests in China using Sentinel-2 images with Google Earth Engine. Forests. 2019;10(9):729.10.3390/f10090729Search in Google Scholar
[16] Yang T, Liu W. Inequality of household carbon emissions and its influencing factors: case study of urban China. Habitat Int. 2017;70:61–71.10.1016/j.habitatint.2017.10.004Search in Google Scholar
[17] Nath TK, Han SSZ, Lechner AM. Urban green space and well-being in Kuala Lumpur, Malaysia. Urban Forestry Urban Green. 2018;36:34–41.10.1016/j.ufug.2018.09.013Search in Google Scholar
[18] Xing L, Liu Y, Liu X, Wei X, Mao Y. Spatio-temporal disparity between demand and supply of park green space service in urban area of Wuhan from 2000 to 2014. Habitat Int. 2018;71:49–59.10.1016/j.habitatint.2017.11.002Search in Google Scholar
[19] Yen Y, Wang Z, Shi Y, Xu F, Soeung B, Sohail MT, et al. The predictors of the behavioral intention to the use of urban green spaces: The perspectives of young residents in Phnom Penh, Cambodia. Habitat Int. 2017;64:98–108.10.1016/j.habitatint.2017.04.009Search in Google Scholar
[20] Zhang S, Zhou W. Recreational visits to urban parks and factors affecting park visits: evidence from geotagged social media data. Landsc urban Plan. 2018;180:27–35.10.1016/j.landurbplan.2018.08.004Search in Google Scholar
[21] Wang Z, Ma D, Sun D, Zhang J. Identification and analysis of urban functional area in Hangzhou based on OSM and POI data. PLoS one. 2021;16(5):e0251988.10.1371/journal.pone.0251988Search in Google Scholar PubMed PubMed Central
[22] Wu L, Kim SK. Exploring the equality of accessing urban green spaces: a comparative study of 341 Chinese cities. Ecol Indic. 2021;121:107080.10.1016/j.ecolind.2020.107080Search in Google Scholar
[23] Fu C, Tu X, Huang A. Identification and characterization of production–living–ecological space in a central urban area based on POI data: a case study for Wuhan, China. Sustainability. 2021;13(14):7691.10.3390/su13147691Search in Google Scholar
[24] Li Y, Chen WH, Chen YJ, Chen HH, Jiang CM. Evaluation on eco-environment of Xiamen based on the first national geography census data. J Subtropical Resour Environ. 2016;2.Search in Google Scholar
[25] Li Y. Accessibility and socio-spatial inequalities between locals and migrants in Xiamen city. Doctoral dissertation. China: Utrecht University; 2021.Search in Google Scholar
[26] Meng LI, Lu D, Zhao Y, Luo X, Yin J. Incorporating polycentric development and neighborhood life-circle planning for reducing driving in Beijing: Nonlinear and threshold analysis. Cities. 2022;121:103488.10.1016/j.cities.2021.103488Search in Google Scholar
[27] Huang J, Zhou J. Study on livability evaluation and planning countermeasures of Community space based on multi-source data--Taking Changsha as an example. IOP Conference Series: Earth and Environmental Science. Vol. 189, Issueo. 5. IOP Publishing; 2018, November. p. 052034.10.1088/1755-1315/189/5/052034Search in Google Scholar
[28] Guo R, Song X, Li P, Wu G, Guo Z. Large-scale and refined green space identification-based sustainable urban renewal mode assessment. Math Probl Eng. 2020;2020:1–12.10.1155/2020/2043019Search in Google Scholar
[29] Cong H, Zou D. The research on the mechanism and spatial–temporal differentiation of the coupling coordination development based on industrial cluster agglomeration. Clust Comput. 2017;20(1):195–213.10.1007/s10586-017-0758-ySearch in Google Scholar
[30] Yang J, Huang C, Zhang Z, Wang L. The temporal trend of urban green coverage in major Chinese cities between 1990 and 2010. Urban Forestry Urban Green. 2014;13(1):19–27.10.1016/j.ufug.2013.10.002Search in Google Scholar
[31] Byczek C, Longaretti PY, Renaud J, Lavorel S. Benefits of crowd-sourced GPS information for modelling the recreation ecosystem service. PLoS One. 2018;13(10):e0202645.10.1371/journal.pone.0202645Search in Google Scholar PubMed PubMed Central
[32] Edrish MB, Neema MN. Super shop food accessibility analysis in Dhaka city through the travel characteristics using network analyst tool in arc GIS. In and Practice (iCERP2019) 4th GCSTMR World Congress; 2019, January. p. 78Search in Google Scholar
[33] Xu X, Wang C, Li J, Shi C. Green transportation and information uncertainty in gasoline distribution: evidence from China. Emerg Mark Financ Trade. 2021;57(11):3101–19.10.1080/1540496X.2019.1708323Search in Google Scholar
[34] Wu CF, Lin YP, Chiang LC, Huang T. Assessing highway’s impacts on landscape patterns and ecosystem services: a case study in Puli Township, Taiwan. Landsc Urban Plan. 2014;128:60–71.10.1016/j.landurbplan.2014.04.020Search in Google Scholar
[35] Pearsall H, Eller JK. Locating the green space paradox: a study of gentrification and public green space accessibility in Philadelphia, Pennsylvania. Landsc Urban Plan. 2020;195:103708.10.1016/j.landurbplan.2019.103708Search in Google Scholar
[36] Liao CN, Fu YK, Wu LC. Integrated FAHP, ARAS-F and MSGP methods for green supplier evaluation and selection. Technol Economic Dev Economy. 2016;22(5):651–69.10.3846/20294913.2015.1072750Search in Google Scholar
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