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BY 4.0 license Open Access Published by De Gruyter Open Access April 4, 2022

Urban green service equity in Xiamen based on network analysis and concentration degree of resources

Qiuxia Yang, Huanqi Zhan and Jian Huang
From the journal Open Geosciences


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 [1416]. 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 [2022]. 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].

Figure 1 
                  Division of spatial units in the research area.

Figure 1

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:

(1) HRAD j = HR j HR n × 100 % A j A n × 100 % = HR j A j HR n A n .

HRAD i is the concentration degree of green space resources in Tong ‘an district, HRAD j is the concentration degree of green space resources in Jimei district, HR i is the amount of green space resources in Tong ‘an district, HR j is the amount of green space resources in Jimei district, A i is the land area of Tong ‘an district, A j is the land area of Jimei district, HR n is the total green space resources of Xiamen city, and A n is the total land area of Xiamen city.

Based on this concept, the evaluation criteria for equity of green space resource allocation are as follows: when HRAD > 1, it indicates that the amount of green space resources gathered in the land area of a certain region accounts for more than 1% of Xiamen, indicating that the allocation of green space resources in this region is relatively rich. When HRAD = 1, it indicates that the distribution of green space resources in the region is absolutely equal according to geographical scale.

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:

(2) PAD i = P i P n × 100 % A i A n × 100 % = P i A i P n A n .

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, P is the number of people in the area, and P n is the total urban population. We define the area where PAD > 2 as regions with a dense population, and where 0.5 < PAD < 2 as regions with mean population, and where PAD < 0.5 as sparsely populated regions. When this concept is combined with green space concentration to evaluate the equity of green space resources, the evaluation criteria are as follows: when HRAD–PAD = 0 (or ≈0), it indicates that the green space resources gathered in this area basically meet the needs of permanent residents. When HRAD–PAD > 0, it indicates that the allocation of green space resources in this area is relatively overpopulated. When HRAD–PAD <0, it indicates that the allocation of green space resources in this area is insufficient.

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:

(3) C = A R S × 100 % .

Among which, S is the total area of the space unit, A R is the total area of service coverage based on accessibility of each green space in this space unit, and the overlap of coverage area is not repeated calculation.

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:

(4) R = A RE S × 100 % .

Among which, A RE is the total area of service coverage of each green space in this space unit based on accessibility, and the overlap of coverage area is calculated superposition. The natural breakpoint method was used to grade the results of green service coverage (C) and recreation opportunity Index (R). It specifically divided into five grades of high, relatively high, medium, relatively low, and low. Score from high to low assigned 5–1 points, get C′ and R′.

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:

(5) G = C + R 2 .

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.

Figure 2 
                     Distribution of green space and traffic network.

Figure 2

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.

Figure 3 
                      Accessibility of green space in (a) 5, (b) 10, and (c) 15 min.

Figure 3

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.

Figure 4 
                     Kernel density map of population distribution.

Figure 4

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.

Table 1

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

Table 2

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
Figure 5 
                   Evenness analysis of Tong’an and Jimei districts under three time thresholds. (a) 5 min, (b) 10 min, and (c) 15 min.

Figure 5

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.

  1. Author contributions: QY and HZ designed the study and QY carried them out. JH prepared the manuscript with contributions from all co-authors.

  2. Conflict of interest: Authors state no conflict of interest.


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Received: 2021-09-29
Revised: 2022-01-05
Accepted: 2022-02-02
Published Online: 2022-04-04

© 2022 Qiuxia Yang et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.