| Literature DB >> 35055638 |
Xin Xu1,2, Jing Hu1,2, Li Lv1,2, Jiaojiao Yin1,2, Xiaobo Tian1,2.
Abstract
An urban ecological recreational space (UERS), which connects the natural environment with urban residents, is an important guarantee for developing a livable city and improving the well-being of residents. However, there is a serious imbalance between the supply of UERSs and the demand of residents in many big, rapidly developing cities. Previous studies usually used indicators such as scale or quantity to measure the supply level of UERS enjoyed by residents, ignoring its own quality differences. Therefore, taking the urban development area of Wuhan as the research object, we measured the quality of UERS from four dimensions using the entropy method and designed a method to measure the supply service level under the hierarchical travel threshold to analyze the supply level of UERSs based on a community unit. Finally, combined with the demand characteristics of different groups, the matching relationship between supply and demand of UERSs in each community is quantitatively analyzed. The results show the following: (1) The quality of UERS in urban development area of Wuhan varies greatly and its distribution is extremely uneven. (2) The level of supply services and the demand level vary greatly, and the overall performance has a trend of decreasing from the city center to the periphery. (3) The overall matching relationship between supply and demand of UERS is not ideal, and more than half of the communities are in supply deficit or without services. Our study provides a novel perspective on quantifying the supply-demand relationship of UERS. It can more accurately guide decision-makers and planners in determining areas with mismatches between the supply and demand of UERSs and in making targeted layouts of UERSs and relevant policies.Entities:
Keywords: UERS; spatial justice; supply–demand balance; urban development area of Wuhan
Mesh:
Year: 2022 PMID: 35055638 PMCID: PMC8775976 DOI: 10.3390/ijerph19020816
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 3.390
Figure 1Evolution diagram of spatial justice theory.
Figure 2Map of the study area.
Classification standard and basic information of urban ecological recreational space.
| Space Level | Number | Area (hm2) | Decision Criteria | Service Radius (m) |
|---|---|---|---|---|
| City level | 72 | 7412.17 | 3000 | |
| Regional level | 85 | 1278.66 | 10 hm2 ≤ | 2000 |
| Community level | 56 | 153.87 | 0 hm2 ≤ | 1000 |
UERS quality measurement indicators and data description.
| Dimensions | Indicators | Description | Weight |
|---|---|---|---|
|
| Area ( | It reflects the scale of UERS. | 0.072 |
| Shape index ( | It represents the complexity of the shape of UERS and is calculated by dividing the area by the perimeter. | 0.089 | |
| Hygiene ( | It reflects the quality of the sanitary environment of the UERS. The sanitary environment is scored as 0–10 points based on the network street view image, public comments in DianPing, and field research. | 0.012 | |
| Environmental carrying capacity ( | It reflects the number of people that can be accommodated in the ecological recreation space in theory. Referring to the code for the design of parks in 2020 and relevant studies, the higher the level of UERS, the greater the proportion of water and slope area in the space and the larger the per capita area. Finally, it is determined that the per capita area occupied area is 25 m2–80 m2 per person, and the environmental carrying capacity is calculated by dividing the space area by the per capita area. | 0.077 | |
|
| Water ratio ( | It is the ratio of water area to total space area, which is calculated based on land use data extraction. | 0.184 |
| Vegetation coverage ( | It is the ratio of vegetation-covered area to total space area, which is calculated based on land use data. | 0.045 | |
| Temperature regulation ( | It reflects the impact of UERS on temperature and environment. Based on the remote sensing image data, the actual spatial surface temperature was calculated using ground temperature inversion methods, and the ability to reduce temperature was expressed as the difference between the actual spatial temperature and the average urban temperature. | 0.020 | |
|
| External facilities ( | It is the number of public transport, catering facilities, parking lots, shopping facilities, and other supporting service facilities within the space service radius. | 0.048 |
| Internal facilities ( | It is the number of recreational and sports facilities, toilets, parking lots, convenience stores, and other service facilities in the UERS. | 0.081 | |
| Landscape ( | It is the number of pavilions and landmark landscape in the UERS. | 0.062 | |
| Road length ( | It is the internal road length of UERS, which is vectorized based on network map and remote sensing image. | 0.060 | |
|
| Attention ( | It is the total number of public comments on the UERS. | 0.053 |
| Favorable comment ( | It is the number of positive comments on the UERS. | 0.053 | |
| Score ( | It is the users’ rating of the UERS. | 0.144 |
Figure 3Quantification of the quality of UERS (a) and the supply level of UERS (b), and quantification of the supply–demand matching relationship of UERS (c).
Meaning of supply–demand matching and fairness.
| Class | Supply–Demand Matching | Fairness | Value Range |
|---|---|---|---|
| I | Saturated | Serious inequity | 5 < |
| II | Sufficient | More equity | 1.25 < |
| III | Balanced | equity | 0.75 < |
| IV | Insufficient | Inequity | 0.35 < |
| V | Shortage | Serious inequity | 0 < |
| VI | No service | Serious inequity |
Figure 4Spatial difference in the quality level of UERS.
Statistics on the quality level of ecological recreational space in different urban areas.
| Number | Area | Basic | Ecological | Facility | Satisfaction | Quality Level | Average Quality Level | |
|---|---|---|---|---|---|---|---|---|
| Main urban area | 134 | 4539.93 | 154.62 | 181.59 | 192.89 | 186.39 | 715.49 | 5.34 |
| Easter urban area | 3 | 23.84 | 1.38 | 1.70 | 1.42 | 2.65 | 7.16 | 2.39 |
| Western urban area | 26 | 796.71 | 22.94 | 30.12 | 18.42 | 21.07 | 92.55 | 3.56 |
| Southern urban area | 17 | 1758.59 | 30.53 | 15.21 | 14.98 | 12.34 | 73.06 | 4.30 |
| Northern urban area | 8 | 279.72 | 10.91 | 3.72 | 4.97 | 6.17 | 25.77 | 3.22 |
| Southeast urban area | 15 | 677.54 | 14.64 | 12.42 | 9.34 | 12.54 | 48.95 | 3.26 |
| Southwest urban area | 10 | 490.00 | 14.97 | 5.23 | 7.98 | 8.85 | 37.03 | 3.70 |
Statistics of residents’ supply service level in different urban areas.
| Highest | Higher | Medium | Lower | No Service | |
|---|---|---|---|---|---|
| Main urban area | 54 | 525 | 354 | 114 | 48 |
| Easter urban area | 0 | 1 | 2 | 19 | 116 |
| Western urban area | 4 | 8 | 55 | 60 | 51 |
| Southern urban area | 0 | 5 | 35 | 37 | 81 |
| Northern urban area | 1 | 0 | 3 | 29 | 103 |
| Southeast urban area | 0 | 1 | 9 | 36 | 71 |
| Southwest urban area | 0 | 1 | 14 | 32 | 74 |
| Total | 59 | 541 | 472 | 327 | 544 |
Figure 5Spatial distribution map of the supply service level enjoyed by community residents.
Figure 6Spatial distribution of the residents’ supply of different groups.
Figure 7Supply–demand matching relationship of UERS in the urban development area of Wuhan.
Figure 8Proportion of communities of different groups at different supply and demand levels.