The Spatiotemporal Matching Relationship between Metro Networks and Urban Population from an Evolutionary Perspective: Passive Adaptation or Active Guidance?
Abstract
:1. Introduction
2. Literature Review
2.1. Research Content and Index Selection
2.2. Analysis Method and Model Selection
2.3. Gaps in the Current Research
3. Materials and Methods
- Draw a map of the urban metro networks based on the initial metro layout and construct a topology diagram.
- Construct a topology connection matrix based on the network topology data in Space-L. If two nodes in the networks are connected, assign a corresponding value to the topological connection matrix of 1; otherwise, assign it as 0.
- Calculate the eigenvalues of Space-L networks and use the topological adjacency matrix to calculate the degree, betweenness, and closeness values.
3.1. Metro Topological Parameter Set
- Degree
- Betweenness
- Closeness
3.2. Spatial Autocorrelation Model
3.3. Time-Lagged Regression Model
3.4. Compositive Coordination Index
- Fitness between metro middle and population gravity center
- Fractal dimension identity between metro and population
- Direction coordination between metro and population
3.5. Coupling Coherence Model
4. Results
4.1. Spatiotemporal Evolution of Metro Networks and Urban Population
4.1.1. Metro Topology Networks
4.1.2. Urban Population
4.2. Action Relationship Verification
4.3. Spatiotemporal Evolution of Matching Relationship
4.3.1. Global Matching Relationship
- Fitness between metro middle and population gravity center
- Fractal dimension identity between metro and population
- Direction coordination between metro and population
4.3.2. Spatial Heterogeneity of Matching Relationship
5. Discussion
6. Conclusions
- The network distribution had a trend of centrifugal dispersion with Moran’s I values from 0.78 in 2011 to 0.51 in 2021, respectively. The population distribution remained highly spatially dependent with Moran’s I values greater than 0.7. There existed a specific positive time-lagged interrelationship between urban metro and population distribution in the process of urban development due to the regression coefficient being more than 0.
- Along with the increased complexity of the metro topology networks, the compositive coordination index between the metro networks and the population increased from 0.29 to 0.90. The fractal identity was 1.19 until 2021, indicating that the role of the metro networks gradually crossed from “passive adaptation” to “active guidance”.
- From 2011 to 2021, there was obvious spatial heterogeneity for matching relationships. The coupling coherence degree in the core areas increased from 0.26 in 2011 to 0.41 in 2021, while the value of the outlying areas was only 0.15 until 2021.
- In the future planning policy-making of Xi’an, differentiated spatial planning strategies were proposed for core, surrounding, and outlying areas. The planning program should abandon the sprawl expansion of metro construction.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Research Object | Indexes | Analysis Methods |
---|---|---|
Metro networks | Degree | Space-L complex networks |
Betweenness | ||
Closeness | ||
Moran’s I value | Spatial autocorrelation | |
Urban population | Permanent population density | Spatial quantization |
Moran’s I value | Spatial autocorrelation | |
Action relationship verification | Regression coefficient | Time-lagged regression |
Spatial matching relationship | Compositive coordination index | Fitness between metro network middle and population center (W) |
) | ||
) | ||
Coupling coherence degree (D) | Coupling coherence |
Year | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 |
---|---|---|---|---|---|---|---|---|---|---|---|
Value | 0.782 *** | 0.782 *** | 0.459 *** | 0.459 *** | 0.459 *** | 0.800 *** | 0.800 *** | 0.391 *** | 0.274 *** | 0.510 *** | 0.510 *** |
Year | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 |
---|---|---|---|---|---|---|---|---|---|---|---|
Value | 0.759 *** | 0.737 *** | 0.741 *** | 0.745 *** | 0.741 *** | 0.742 *** | 0.740 *** | 0.742 *** | 0.742 *** | 0.742 *** | 0.742 *** |
Variable | Current Period | Lag Order 1 | 2 | 3 |
---|---|---|---|---|
Population | 0.614 ** | 1.301 ** | 1.508 ** | 0.802 |
(5.40) | (6.76) | (3.85) | (0.68) | |
LFR | 107.570 * | 100.600 | 104.982 | −253.900 |
(2.44) | (1.86) | (0.79) | (0.81) | |
Constant | −1695.39 | −1856.75 | −1983.13 | 3543.74 |
(2.67) | (2.35) | (0.98) | (0.72) | |
R-squared | 0.82 | 0.87 | 0.74 | 0.53 |
Year | Coordinates of the Population Center | Distance (km) | Fitness Values |
---|---|---|---|
2011 | (108.950, 34.265) | 1.02 | 0.937 |
2016 | (108.955, 34.265) | 1.40 | 0.914 |
2021 | (108.952, 34.264) | 1.18 | 0.927 |
Time | Permanent Population | Metro Networks |
---|---|---|
2011 | ||
2016 | ||
2021 |
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Lei, K.; Hou, Q.; Duan, Y.; Xi, Y.; Chen, S.; Miao, Y.; Tong, H.; Hu, Z. The Spatiotemporal Matching Relationship between Metro Networks and Urban Population from an Evolutionary Perspective: Passive Adaptation or Active Guidance? Land 2024, 13, 1200. https://doi.org/10.3390/land13081200
Lei K, Hou Q, Duan Y, Xi Y, Chen S, Miao Y, Tong H, Hu Z. The Spatiotemporal Matching Relationship between Metro Networks and Urban Population from an Evolutionary Perspective: Passive Adaptation or Active Guidance? Land. 2024; 13(8):1200. https://doi.org/10.3390/land13081200
Chicago/Turabian StyleLei, Kexin, Quanhua Hou, Yaqiong Duan, Yafei Xi, Su Chen, Yitong Miao, Haiyan Tong, and Ziye Hu. 2024. "The Spatiotemporal Matching Relationship between Metro Networks and Urban Population from an Evolutionary Perspective: Passive Adaptation or Active Guidance?" Land 13, no. 8: 1200. https://doi.org/10.3390/land13081200