Simulation on the Evolution Trend of the Urban Sprawl Spatial Pattern in the Upper Reaches of the Yangtze River, China
Abstract
:1. Introduction
2. Study Area and Data Sources
2.1. Study Area
2.2. Data Sources
3. Methods
3.1. Urban Sprawl Index
3.2. Spatial Autocorrelation Model
3.3. Urban Sprawl Scenario Analysis Model
3.4. Urban Sprawl Influence Factor Model
3.4.1. Geodetector
3.4.2. Urban Sprawl Influence Indicator System Construction
4. Results
4.1. Recognition of the Spatial Features of Urban Sprawl
4.2. The Pattern and Evolution of the Urban Sprawl Cold and Hot Spots
4.3. The Simulation Results of the Different Urban Sprawl Scenarios
4.3.1. Test of the Urban Sprawl Scenario Analysis Model
4.3.2. Scenario Simulation of the Different Scales of Urban Sprawl
4.3.3. Scenario Simulation of the Urban Sprawl at Different Times
4.4. Analysis of Urban Sprawl Influencing Factors
4.4.1. Analysis of Urban Sprawl Influence Factor Detectors
4.4.2. Analysis of Urban Sprawl Influence Factor Interaction Detectors
5. Discussion
5.1. The Scale Effect of Urban Sprawl Spatial Pattern Evolution
5.2. The Scale Effect of the Urban Sprawl under Different Time Scenarios
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Judgment Conditions | Interaction |
---|---|
q(X1∩X2) < Min(q(X1),q(X2)) | Non-linear reduction |
Min(q(X1),q(X2)) < q(X1∩X2) < Max(q(X1),q(X2)) | Single-factor nonlinearity reduction |
q(X1∩X2) > Max(q(X1),q(X2)) | Two-factor enhancement |
q(X1∩X2) = q(X1) + q(X2) | Independent |
q(X1∩X2) > q(X1) + q(X2) | Non-linear enhancement |
Dimensions | Factors | Code | Unit |
---|---|---|---|
Economic development | GDP | X1 | Billion CNY |
Urban disposable income per capita | X2 | CNY | |
Secondary industry share of GDP | X3 | % | |
Tertiary industry share of GDP | X4 | % | |
Investment in real estate development | X5 | Billion CNY | |
Social culture | Population | X6 | Million |
Urbanization rate | X7 | % | |
Number of high schools | X8 | - | |
Urban green space per capita | X9 | m2 | |
Transportation | Urban road area per capita | X10 | m2 |
Distance from major railroads | X11 | m | |
Private car ownership | X12 | - | |
Highway mileage | X13 | km | |
Government regulation | Public finance expenditure | X14 | Billion CNY |
Fixed assets input | X15 | Billion CNY |
Scale Type | 2020 | 2025 | 2030 | 2035 | |
---|---|---|---|---|---|
Urban size | Megacity | 3001.275 | 5204.275 | 8927.358 | 15,101.67 |
21.88% | 22.13% | 22.09% | 21.78% | ||
Large city | 6580.468 | 11,244.46 | 19,326.38 | 33,389.08 | |
47.98% | 47.81% | 47.83% | 48.16% | ||
Medium city | 3101.561 | 5298.561 | 9081.561 | 15,606.56 | |
22.61% | 22.53% | 22.47% | 22.51% | ||
Small city | 1031.342 | 1772.342 | 3072.342 | 5230.342 | |
7.52% | 7.54% | 7.60% | 7.54% | ||
Urban agglomeration | Chengdu-Chongqing | 6987.412 | 12,089.41 | 20,684.41 | 35,299.41 |
50.95% | 51.40% | 51.19% | 50.92% | ||
The central Yunnan | 1550.725 | 2652.725 | 4549.725 | 7858.725 | |
11.31% | 11.28% | 11.26% | 11.34% | ||
The central Guizhou | 1165.393 | 1921.393 | 3275.393 | 5648.393 | |
8.50% | 8.17% | 8.11% | 8.15% |
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Zhang, Y.; Guan, D.; He, X.; Yin, B. Simulation on the Evolution Trend of the Urban Sprawl Spatial Pattern in the Upper Reaches of the Yangtze River, China. Int. J. Environ. Res. Public Health 2022, 19, 9190. https://doi.org/10.3390/ijerph19159190
Zhang Y, Guan D, He X, Yin B. Simulation on the Evolution Trend of the Urban Sprawl Spatial Pattern in the Upper Reaches of the Yangtze River, China. International Journal of Environmental Research and Public Health. 2022; 19(15):9190. https://doi.org/10.3390/ijerph19159190
Chicago/Turabian StyleZhang, Yuxiang, Dongjie Guan, Xiujuan He, and Boling Yin. 2022. "Simulation on the Evolution Trend of the Urban Sprawl Spatial Pattern in the Upper Reaches of the Yangtze River, China" International Journal of Environmental Research and Public Health 19, no. 15: 9190. https://doi.org/10.3390/ijerph19159190