Revealing Characteristics of the Spatial Structure of Megacities at Multiple Scales with Jobs-Housing Big Data: A Case Study of Tianjin, China
Abstract
:1. Introduction
2. Literature Review
2.1. Sustainable Urban Development and Spatial Structure
2.2. Identification and Characteristics of Urban Spatial Structure
3. Materials and Methods
3.1. Study Area
3.2. Data
3.3. Methods
3.3.1. Identification of Static Characteristics
3.3.2. Identification of Dynamic Characteristics
4. Results
4.1. Static Polycentricity
4.1.1. Polycentricity in the Metropolitan Area
4.1.2. Polycentricity in the Central Area
4.2. Dynamic Commuting Communities
4.2.1. Commuting Communities in the Metropolitan Area
4.2.2. Commuting Communities in the Central Area
5. Discussion
5.1. Does Polycentricity Explain the Distribution of Jobs in Cities?
5.2. Jobs–Housing Balance Levels in Commuting Communities
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Centers | Number of Jobs | Job Density |
---|---|---|
Main center | 1,222,316 | 17,167/km2 |
Subcenter | 267,183 | 5951/km2 |
Centers | Number of Jobs | Job Density |
---|---|---|
Main center | 384,461 | 27,659/km2 |
BC subcenter | 31,985 | 6462/km2 |
DH subcenter | 18,012 | 24,674/km2 |
HY subcenter | 46,000 | 21,596/km2 |
TT subcenter | 29,211 | 16,319/km2 |
WH subcenter | 49,613 | 12,497/km2 |
Commuting Communities | Number of Residents | Number of Jobs |
---|---|---|
MC1 | 2,099,700 | 825,203 |
MC2 | 2,467,886 | 1,185,182 |
MC3 | 1,694,100 | 941,802 |
MC4 | 1,971,600 | 830,969 |
MP1 | 3,113,505 | 1,513,038 |
Commuting Communities | Number of Residents | Number of Jobs |
---|---|---|
CC1 | 158,370 | 90,899 |
CC2 | 266,677 | 229,683 |
CP1 | 1,288,781 | 445,169 |
CP2 | 894,811 | 394,929 |
CP3 | 608,605 | 232,616 |
CP4 | 1,147,716 | 551,986 |
CP5 | 1,155,372 | 381,729 |
Commuting Communities | Jobs-Housing Ratio | Intra-Travel Ratio |
---|---|---|
MC1 | 0.86 | 67.6% |
MC2 | 1.02 | 72.7% |
MC3 | 1.25 | 68.5% |
MC4 | 0.90 | 66.6% |
MP1 | 1.01 | 91.1% |
Commuting Communities | Jobs-Housing Ratio | Intra-Travel Ratio |
---|---|---|
CC1 | 1.25 | 32.8% |
CC2 | 2.66 | 45.1% |
CP1 | 0.73 | 67.7% |
CP2 | 0.93 | 65.6% |
CP3 | 0.81 | 52.7% |
CP4 | 1.05 | 71.4% |
CP5 | 0.67 | 60.8% |
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Dong, R.; Yan, F. Revealing Characteristics of the Spatial Structure of Megacities at Multiple Scales with Jobs-Housing Big Data: A Case Study of Tianjin, China. Land 2021, 10, 1144. https://doi.org/10.3390/land10111144
Dong R, Yan F. Revealing Characteristics of the Spatial Structure of Megacities at Multiple Scales with Jobs-Housing Big Data: A Case Study of Tianjin, China. Land. 2021; 10(11):1144. https://doi.org/10.3390/land10111144
Chicago/Turabian StyleDong, Ruixi, and Fengying Yan. 2021. "Revealing Characteristics of the Spatial Structure of Megacities at Multiple Scales with Jobs-Housing Big Data: A Case Study of Tianjin, China" Land 10, no. 11: 1144. https://doi.org/10.3390/land10111144
APA StyleDong, R., & Yan, F. (2021). Revealing Characteristics of the Spatial Structure of Megacities at Multiple Scales with Jobs-Housing Big Data: A Case Study of Tianjin, China. Land, 10(11), 1144. https://doi.org/10.3390/land10111144