Assessing Spatial Accessibility to Hierarchical Urban Parks by Multi-Types of Travel Distance in Shenzhen, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Source and Preprocessing
2.3. Method
2.3.1. Modified Gaussian-Based 2SFCA
2.3.2. Estimation of Travel Distance
2.3.3. Parameters
2.3.4. Measurement of Spatial Disparity
- (1)
- If ZAi > 0 and ZPopDi > 0, community i has high accessibility and high population density.
- (2)
- If ZAi < 0 and ZPopDi < 0, community i has low accessibility and low population density.
- (3)
- If ZAi > 0 and ZPopDi < 0, community i has high accessibility and low population density.
- (4)
- If ZAi < 0 and ZPopDi > 0, community i has low accessibility and high population density.
3. Results
3.1. Proximity between Communities and Urban Parks under Four Types of Distance
3.1.1. Proximity without Distance Threshold
3.1.2. Proximity under Fixed Distance Threshold
3.2. Results of MG2SFCA at Three Levels of Parks
3.2.1. Statistical Analysis of Park Accessibility
3.2.2. Spatial Analysis of Park Accessibility
3.3. Disparity Analysis of the Supply-to-Demand
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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District | Population (Million) | Number of Park | Total Park Area (km2) | Park Area per Capita (m2) | ||||||
---|---|---|---|---|---|---|---|---|---|---|
Community Park | City Park | Natural Park | Community Park | City Park | Natural Park | Community Park | City Park | Natural Park | ||
Futian | 0.76 | 147 | 12 | 2 | 0.60 | 7.24 | 7.35 | 0.79 | 9.57 | 9.72 |
Luohu | 0.92 | 133 | 10 | 3 | 0.66 | 10.85 | 18.22 | 0.72 | 11.74 | 19.72 |
Yantian | 0.17 | 32 | 8 | 2 | 0.27 | 5.86 | 44.67 | 1.61 | 35.13 | 267.97 |
Nanshan | 1.17 | 65 | 28 | 1 | 0.94 | 12.23 | 9.91 | 0.80 | 10.45 | 8.47 |
Bao’an | 4.56 | 142 | 15 | 4 | 2.71 | 6.30 | 34.51 | 0.59 | 1.38 | 7.56 |
Longgang | 3.45 | 103 | 18 | 7 | 1.35 | 15.16 | 56.13 | 0.39 | 4.40 | 16.28 |
Longhua | 2.29 | 46 | 5 | 5 | 0.86 | 0.87 | 43.44 | 0.37 | 0.38 | 18.97 |
Pingshan | 0.46 | 28 | 6 | 3 | 0.14 | 2.22 | 38.14 | 0.31 | 4.84 | 83.31 |
Guangming | 1.06 | 28 | 4 | 4 | 0.62 | 1.31 | 28.80 | 0.58 | 1.23 | 27.14 |
Dapeng | 0.10 | 21 | 12 | 3 | 0.46 | 1.57 | 54.11 | 4.40 | 15.00 | 517.84 |
Distance Type (km) | Mean | P10 | Q1 | Q2 | Q3 | P90 |
---|---|---|---|---|---|---|
Euclidean distance | 23.52 | 6.30 | 12.57 | 21.63 | 32.31 | 42.65 |
Walking distance | 29.96 | 8.17 | 16.52 | 27.44 | 40.61 | 54.01 |
Bicycling distance | 31.74 | 8.85 | 17.69 | 29.19 | 42.85 | 57.12 |
Driving distance | 32.02 | 9.1 | 17.37 | 29.39 | 43.82 | 57.57 |
Distance | Euclidean | Walking | Bicycling | Driving |
---|---|---|---|---|
Distances between communities and parks (N) | 599,760 | 599,760 | 599,760 | 599,760 |
Euclidean distance | 1 | 0.986 ** | 0.985 ** | 0.981 ** |
Walking distance | 0.986 ** | 1 | 0.997 ** | 0.973 ** |
Bicycling distance | 0.985 ** | 0.997 ** | 1 | 0.972 ** |
Driving distance | 0.981 ** | 0.973 ** | 0.972 ** | 1 |
Level of Parks | Travel Distance Threshold | Distance Mode | Mean | Standard Deviation | Underserved Community (Numbers) | Underserved Areas (km2) | Underserved Population |
---|---|---|---|---|---|---|---|
Community park | 2 km | Euclidean | 0.82 | 1.85 | 79 | 328.38 | 2,336,132 |
Walking | 0.79 | 1.90 | 126 | 471.72 | 3,723,610 | ||
Bicycling | 0.78 | 1.89 | 137 | 541.78 | 4,125,854 | ||
Driving | 0.77 | 1.98 | 181 | 675.48 | 5,487,025 | ||
City park | 5 km | Euclidean | 9.98 | 28.90 | 35 | 165.08 | 1,515,039 |
Walking | 13.28 | 79.68 | 97 | 545.06 | 3,620,555 | ||
Bicycling | 11.70 | 49.19 | 100 | 566.75 | 3,766,218 | ||
Driving | 11.17 | 44.51 | 148 | 716.66 | 5,395,964 | ||
Natural park | 10 km | Euclidean | 54.82 | 211.38 | 5 | 24.59 | 77,714 |
Walking | 52.47 | 214.58 | 45 | 66.23 | 781,082 | ||
Bicycling | 52.27 | 215.24 | 59 | 88.92 | 1,092,732 | ||
Driving | 54.34 | 225.48 | 74 | 121.67 | 1,288,487 |
Type of Distance | High Ai—High PopDi | High Ai—Low PopDi | Low Ai—Low PopDi | Low Ai—High PopDi | |
---|---|---|---|---|---|
Count | Euclidean | 151 23.97% | 158 25.08% | 71 11.27% | 250 39.68% |
Walking | 114 18.10% | 138 21.90% | 91 14.44% | 287 45.56% | |
Bicycling | 113 17.94% | 136 21.59% | 93 14.76% | 288 45.71% | |
Driving | 114 18.10% | 125 19.84% | 104 16.51% | 287 45.56% | |
Population | Euclidean | 4,246,057 28.42% | 2,014,124 13.48% | 1,380,564 9.24% | 7,300,112 48.86% |
Walking | 3,383,533 22.65% | 1,520,657 10.18% | 1,874,031 12.54% | 8,162,636 54.63% | |
Bicycling | 3,435,872 23.00% | 1,699,450 11.37% | 1,695,238 11.35% | 8,110,297 54.28% | |
Driving | 3,038,667 20.34% | 1,512,910 10.13% | 1,881,778 12.59% | 8,507,502 56.94% |
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Share and Cite
Li, L.; Du, Q.; Ren, F.; Ma, X. Assessing Spatial Accessibility to Hierarchical Urban Parks by Multi-Types of Travel Distance in Shenzhen, China. Int. J. Environ. Res. Public Health 2019, 16, 1038. https://doi.org/10.3390/ijerph16061038
Li L, Du Q, Ren F, Ma X. Assessing Spatial Accessibility to Hierarchical Urban Parks by Multi-Types of Travel Distance in Shenzhen, China. International Journal of Environmental Research and Public Health. 2019; 16(6):1038. https://doi.org/10.3390/ijerph16061038
Chicago/Turabian StyleLi, Langjiao, Qingyun Du, Fu Ren, and Xiangyuan Ma. 2019. "Assessing Spatial Accessibility to Hierarchical Urban Parks by Multi-Types of Travel Distance in Shenzhen, China" International Journal of Environmental Research and Public Health 16, no. 6: 1038. https://doi.org/10.3390/ijerph16061038