Urban Growth Simulation Based on a Multi-Dimension Classification of Growth Types: Implications for China’s Territory Spatial Planning
Abstract
:1. Introduction
2. Literature Review
3. Method Design
3.1. Study Area
3.2. Data Source
4. Results
4.1. Classification Results
4.2. Multilevel Logit Model
4.3. Urban Growth Simulation in Xi’an
5. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Type | Data | Units | Year | Data Source and Processing |
---|---|---|---|---|
Land use | Land-use cover | / | 2010,2020 | Data from GlobeLand30. 1 for urban area and 0 for non-urban area |
Natural topography | Slope | Degree | 2020 | Data from digital elevation model provided by Geospatial Data Cloud (https://www.gscloud.cn/home) (accessed on 1 July 2022) |
Aspect | / | 2020 | ||
Transportation facilities proximity | Distance to train station | m | 2020 | Data from 1: 1,000,000 public version of basic geographic information data provided by National Catalogue Service for Geographic Information (https://www.webmap.cn) (accessed on 1 July 2022) Proximity was calculated by Euclidean distance in ARC GIS 10.7. |
Distance to coach station | m | 2020 | ||
Distance to airport | m | 2020 | ||
Distance to subway station | m | 2020 | ||
Distance to city road | m | 2020 | ||
Distance to railroad | m | 2020 | ||
Distance to highway | m | 2020 | ||
Urban facilities proximity | Distance to colleges and universities | m | 2020 | Data from Gaode Open Platform (https://lbs.amap.com/) (accessed on 1 July 2022) Proximity was calculated by Euclidean distance in ARC GIS 10.7. |
Distance to shopping centers | m | 2020 | ||
Distance to companies | m | 2020 | ||
Distance to hospitals | m | 2020 | ||
Urban structure | Distance to local government agencies | m | 2020 | Data from Gaode Open Platform (https://lbs.amap.com/) (accessed on 1 July 2022) Proximity was calculated by Euclidean distance in ARC GIS 10.7. |
Distance to center | m | 2020 | ||
Economic factors | GDP per square kilometers | Yuan/km2 | 2010 | Data from China GDP spatial distribution km grid dataset [62]. |
Restricted factors | Water resources | / | 2019 | Statistical Bulletin of Water Resources of Xi’an |
Altitude | m | 2020 | Data from Digital Elevation Model provided by Geospatial Data Cloud (https://www.gscloud.cn/home) (accessed on 1 July 2022) | |
Geological disasters | / | 2016 | National Geological Disaster Prevention and Control 13th Five-Year Plan | |
Cultural relic protection units at provincial and national level | / | 2020 | Xi’an Historical and Cultural City Protection Plan (2019–2035) |
Cluster | OT | DZ | NIS | SIS | EIS | WS | NS | WOS | EOS |
---|---|---|---|---|---|---|---|---|---|
Mean | Mean | Mean | Mean | Mean | Mean | Mean | Mean | Mean | |
Land changed | 88.88% | 63.81% | 39.88% | 9.98% | 3.55% | 0.87% | 4.61% | 0.03% | 0.34% |
Urban land in2010 | 86.55% | 48.44% | 56.91% | 17.96% | 12.09% | 3.28% | 11.93% | 0.55% | 1.67% |
Urban land in2020| | 97.85% | 79.33% | 70.84% | 25.18% | 14.24% | 3.85% | 15.21% | 0.55% | 1.92% |
Slope | 2.73 | 2.30 | 2.26 | 4.77 | 11.57 | 19.90 | 3.61 | 24.63 | 15.26 |
Aspect | 184.28 | 187.15 | 176.46 | 181.38 | 186.48 | 178.56 | 174.97 | 175.24 | 182.40 |
Distance to train station | 5319.51 | 8031.08 | 5530.86 | 15,705.85 | 9404.12 | 38,932.66 | 13,048.35 | 84,873.23 | 25,926.89 |
Distance to airport | 27,504.24 | 29,604.12 | 18,865.97 | 36,544.54 | 47,386.43 | 52,152.24 | 32,497.70 | 82,795.05 | 67,345.78 |
Distance to subway station | 769.90 | 1627.12 | 2640.03 | 7291.27 | 12,410.54 | 27,682.97 | 13,034.84 | 70,899.99 | 31,884.62 |
Distance to city road | 750.77 | 1027.00 | 1581.17 | 1496.43 | 2816.20 | 5069.36 | 1191.93 | 7110.39 | 2756.58 |
Distance to railroad | 3659.30 | 4519.74 | 1388.41 | 5479.90 | 4548.82 | 6949.57 | 6515.03 | 32,832.64 | 12,460.07 |
Distance to highway | 2877.12 | 1364.51 | 1546.70 | 2764.84 | 6025.00 | 7146.71 | 2878.98 | 37,231.72 | 6051.43 |
Distance to colleges and university | 774.66 | 1440.38 | 2110.46 | 4421.63 | 8065.32 | 9599.70 | 4599.22 | 29,290.74 | 14,530.31 |
Distance to shopping center | 312.08 | 596.94 | 838.47 | 1502.10 | 3248.28 | 5073.79 | 1740.99 | 15,344.59 | 4823.38 |
Distance to companies | 121.58 | 215.42 | 242.14 | 696.26 | 2028.99 | 2718.09 | 768.44 | 7938.00 | 3053.74 |
Distance to hospitals | 690.36 | 1515.61 | 1692.65 | 3958.42 | 5792.40 | 11,617.91 | 2793.98 | 22,339.42 | 10,431.43 |
Distance to local government agencies | 727.78 | 1963.91 | 2025.63 | 4269.77 | 9618.54 | 15,679.66 | 5169.62 | 31,663.20 | 27,395.15 |
Distance to center | 20,022.64 | 16,849.66 | 28,103.68 | 13,810.31 | 36,005.25 | 23,043.75 | 51,379.60 | 66,180.72 | 63,614.50 |
GDP per square kilometers | 157,019.10 | 77,716.75 | 32,388.96 | 6681.43 | 78,704.52 | 100,156.80 | 37,986.21 | 237,074.90 | 253,584.40 |
Coefficient | Standard Error | 95% Confidence Interval | ||
---|---|---|---|---|
Fixed effects | ||||
Slope | −0.62 *** | 0.07 | −0.76 | −0.48 |
Aspect | 0.00 | 0.01 | −0.03 | 0.02 |
Distance to train station | −2.41 *** | 0.08 | −2.57 | −2.24 |
Distance to city road | 0.03 | 0.06 | −0.09 | 0.14 |
Distance to railroad | 0.39 *** | 0.06 | 0.28 | 0.50 |
Distance to companies | −19.69 *** | 0.30 | −20.28 | −19.10 |
Distance to local government agencies | −2.34 *** | 0.08 | −2.49 | −2.18 |
Distance to center | −1.09 *** | 0.05 | −1.19 | −1.00 |
GDP per square kilometers | 1288.66 *** | 57.01 | 1176.93 | 1400.40 |
constant | −9.60 *** | 0.88 | −11.33 | −7.88 |
Random effects | Estimate | standard error | 95% confidence interval | |
var(constant) | 3.94 | 1.91 | 1.52 | 10.19 |
2020 Land Use | 2020 CM-CA | 2010–2020 CM-CA | 2020–2030 CM-CA | |||
---|---|---|---|---|---|---|
Cluster | Non-Urban Area (km2) | Urban Area (km2) | Urban Area Percentage | Urban Area Percentage | Urban Growth Rate | Urban Growth Rate |
OT | 345.42 | 15,657.39 | 97.84% | 86.22% | −0.0033 | −0.0004 |
DZ | 990.63 | 3810.6 | 79.37% | 47.11% | −0.0132 | −0.0051 |
NIS | 8397.45 | 20,377.44 | 70.82% | 70.35% | 0.1344 | 0.0681 |
EIS | 95,548.77 | 15,870.42 | 14.24% | 16.86% | 0.0477 | 0.0505 |
SIS | 49,361.22 | 16,629.84 | 25.20% | 25.60% | 0.0764 | 0.0718 |
NS | 38,716.65 | 6943.59 | 15.21% | 16.35% | 0.0442 | 0.0493 |
WS | 158,312.7 | 6348.06 | 3.86% | 3.69% | 0.0041 | 0.0057 |
WOS | 302,944.7 | 1666.89 | 0.55% | 0.53% | −0.0002 | −0.0002 |
EOS | 263,164.5 | 5161.5 | 1.92% | 1.97% | 0.0031 | 0.0031 |
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Miao, S.; Xiao, Y.; Tang, L. Urban Growth Simulation Based on a Multi-Dimension Classification of Growth Types: Implications for China’s Territory Spatial Planning. Land 2022, 11, 2210. https://doi.org/10.3390/land11122210
Miao S, Xiao Y, Tang L. Urban Growth Simulation Based on a Multi-Dimension Classification of Growth Types: Implications for China’s Territory Spatial Planning. Land. 2022; 11(12):2210. https://doi.org/10.3390/land11122210
Chicago/Turabian StyleMiao, Siyu, Yang Xiao, and Ling Tang. 2022. "Urban Growth Simulation Based on a Multi-Dimension Classification of Growth Types: Implications for China’s Territory Spatial Planning" Land 11, no. 12: 2210. https://doi.org/10.3390/land11122210