Spatio-Temporal Coupling Characteristics and the Driving Mechanism of Population-Land-Industry Urbanization in the Yangtze River Economic Belt
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Source
2.3. Methods
2.3.1. The Coupling Coordination Degree Model of PLIU
2.3.2. The Driving Mechanism of PLIU Coordinated Development
2.3.3. Spatial Metrological Analysis Based on ArcGIS and GeoDa
Spatial Autocorrelation Analysis
Spatial Regression Model of PLIU Coordinated Development
3. Results
3.1. Spatio-temporal Characteristics of PLIU Coupling Degree
3.2. Spatial-Temporal Characteristics of PLIU Coordination Degree
3.3. The Spatial Correlation Characteristics of PLIU Coordinated Development
3.3.1. Global Autocorrelation Analysis
3.3.2. Local Spatial Autocorrelation
3.4. The Driving Mechanism of PLIU Coordinated Development
4. Conclusions and Discussion
4.1. Conclusions
4.2. Discussion
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Coupling Degree | Coupling Types | Coupling Characteristics |
---|---|---|
C ∈ [0,04) | Low coupling period | PLIU systems start to play games with each other, which is in a low-level coupling period and is in an irrelevant state with disordered development. |
C ∈ [0.4,0.6) | Antagonism period | The interaction of the PLIU system has been strengthened, and the structure of the urbanization system needs to be further optimized. |
C ∈ [0.6,0.8) | The running-in period | The PLIU system begins to balance and coordinate, but land urbanization is still in the dominant position, while the development of population and industrial urbanization lags behind. |
C ∈ [0.8,1] | Coordinated coupling period | The benign coupling trend between population, land and industrial urbanization is gradually strengthened and develops in an orderly direction, which is the stage of high-level coupling. |
Coordination Degree | Coordination Types | Characteristics |
---|---|---|
D ∈ [0,0.3) | Incoordination | The PLIU system is in a state of disharmony, and the structure is in a state of disorder. |
D ∈ [0.3,0.45) | Low coordination | The PLIU system starts to develop towards a coordinated coupling direction, but the coordination degree is low. |
D ∈ [0.45,0.6) | Moderate coordination | The PLIU system is in a state of collaborative evolution, but the response sensitivity is relatively slow. |
D ∈ [0.6,0.75] | Higher coordination | The PLIU system is in a high level of synchronous cooperation, and the inter-system structure has been greatly improved. |
D ∈ [0.75,1] | High coordination | The PLIU systems promote each other, and the urbanization system realizes coordinated and consistent development. |
Influence Factors | Indexes | Units | Index Significance |
---|---|---|---|
Economic development level | C1 GDP per capita | Ten thousand yuan/person | Representing the average level of economic development |
C2 GDP per unit area of land | One hundred million yuan/hm2 | Representing the level of economic development and the density of output | |
C3 The proportion of tertiary industry in GDP | % | Representing the state of the economic structure | |
C4 Per capita financial income | Ten thousand yuan/person | Representing government revenue | |
The government’s decision-making behavior | C5 Fixed assets investment | One hundred million yuan/hm2 | Representing the strength of government investment |
C6 Number of teachers per 10,000 | Person | Representing the intensity of investment in education | |
C7 Number of hospital beds per 10,000 people | Zhang | Representing the intensity of investment in health care | |
Urban location conditions | C8 Green coverage rate in urban built-up areas | % | Representing the urban ecological environment |
C9 Urban road area per capita | m2/person | Representing the urban traffic condition | |
C10 City dummy variable | 0–6 | Representing the urban geographical location conditions |
Spatial Dependence Test | MI/DF | VALUE | PROB |
---|---|---|---|
Moran’s I (error) | 0.2087 | 3.5927 | 0.0000 |
Lagrange multiplier (lag) | 1 | 2.2551 | 0.1331 |
Robust LM (lag) | 1 | 0.2120 | 0.6452 |
Lagrange multiplier (error) | 1 | 9.8901 | 0.0016 |
Robust LM (error) | 1 | 7.8470 | 0.0050 |
Lagrange multiplier (SARMA) | 2 | 10.1021 | 0.0064 |
Variable | Coefficient | Std. Error | z-Value | Probability |
---|---|---|---|---|
CONSTANT | −1.4087 | −1.4087 | −10.1239 | 0.0000 |
C1 GDP per capita | 0.0529 | 0.0090 | 5.8210 | 0.0000 |
C4 Per capita financial income | 0.0206 | 0.0334 | 0.6193 | 0.5357 |
C6 Number of teachers per 10,000 people | 0.0009 | 0.0005 | 1.5435 | 0.1227 |
C8 Green coverage rate in urban built-up areas | 0.0078 | 0.0035 | 2.1881 | 0.0286 |
C9 Urban road area per capita | 0.0133 | 0.0077 | 1.7301 | 0.0836 |
C10 Urban virtual variables | 0.0463 | 0.0233 | 1.9841 | 0.0472 |
LAMBDA | 0.3488 | 0.1148 | 3.0376 | 0.0023 |
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Huang, L.; Yang, P.; Zhang, B.; Hu, W. Spatio-Temporal Coupling Characteristics and the Driving Mechanism of Population-Land-Industry Urbanization in the Yangtze River Economic Belt. Land 2021, 10, 400. https://doi.org/10.3390/land10040400
Huang L, Yang P, Zhang B, Hu W. Spatio-Temporal Coupling Characteristics and the Driving Mechanism of Population-Land-Industry Urbanization in the Yangtze River Economic Belt. Land. 2021; 10(4):400. https://doi.org/10.3390/land10040400
Chicago/Turabian StyleHuang, Liejia, Peng Yang, Boqing Zhang, and Weiyan Hu. 2021. "Spatio-Temporal Coupling Characteristics and the Driving Mechanism of Population-Land-Industry Urbanization in the Yangtze River Economic Belt" Land 10, no. 4: 400. https://doi.org/10.3390/land10040400
APA StyleHuang, L., Yang, P., Zhang, B., & Hu, W. (2021). Spatio-Temporal Coupling Characteristics and the Driving Mechanism of Population-Land-Industry Urbanization in the Yangtze River Economic Belt. Land, 10(4), 400. https://doi.org/10.3390/land10040400