Analysis of the Effectiveness of Urban Land-Use-Change Models Based on the Measurement of Spatio-Temporal, Dynamic Urban Growth: A Cellular Automata Case Study
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Land-Use Data Processing
2.3. Indicators for Measuring Urban Growth
2.4. Logistic CA Urban Growth Simulation Model
3. Results and Discussion
3.1. Urban Growth Measurement
3.2. Urban Growth Simulation
3.3. Expansion Type Ratio and Spatial Dependence Play a Key Role in CA Model Applicability
3.4. Domain Adaption of the CA Model
4. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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No. | Period | 1990–2000 (T1) | 2000–2009 (T2) | ||
---|---|---|---|---|---|
Region | Growth Area (km2) | Growth Rate (%) | Growth Area (km2) | Growth Rate (%) | |
1 | Dongguan | 313.05 | 21.43 | 724.35 | 17.53 |
2 | Foshan | 244.31 | 16.89 | 736.20 | 21.03 |
3 | Conghua | 2.12 | 2.52 | 19.87 | 20.99 |
4 | Panyu & Nansha | 64.28 | 16.42 | 181.01 | 19.44 |
5 | Guangzhou | 139.39 | 7.20 | 299.41 | 9.99 |
6 | Huadu | 27.72 | 17.40 | 108.04 | 27.50 |
7 | Zengcheng | 30.42 | 20.03 | 76.99 | 18.76 |
8 | Shenzhen | 182.11 | 9.28 | 447.84 | 13.16 |
9 | Zhongshan | 95.30 | 15.53 | 290.07 | 20.58 |
Period | T1: 1990–2000 | T2: 2000–2009 | ||||
---|---|---|---|---|---|---|
District | Outlying | Edge-Expansion | Infilling | Outlying | Edge-Expansion | Infilling |
GZ | 9.29% | 74.78% | 15.93% | 2.02% | 37.39% | 60.59% |
PN | 16.14% | 73.32% | 10.54% | 6.81% | 59.24% | 33.96% |
HD | 18.62% | 78.63% | 2.75% | 5.29% | 47.17% | 47.54% |
ZC | 6.89% | 86.75% | 6.36% | 4.16% | 42.60% | 53.24% |
CH | 3.08% | 87.83% | 9.09% | 10.70% | 60.76% | 28.54% |
SZ | 10.93% | 72.44% | 16.62% | 3.26% | 50.22% | 46.51% |
DG | 20.16% | 71.00% | 8.84% | 2.61% | 46.96% | 50.44% |
FS | 12.42% | 71.80% | 15.78% | 5.27% | 48.31% | 46.43% |
ZS | 18.93% | 73.21% | 7.86% | 5.53% | 56.32% | 38.14% |
Trained Data Set | Data Source | Growth Area (km2) | Outlying Ratio | District Center Dependence | Major Road Dependence |
---|---|---|---|---|---|
D1 | PN, T2 | 181.01 | 6.81% | Single center (4.0–14.0 km) | Low |
D2 | ZC, T2 | 76.99 | 4.16% | Single center (7.0–16.0 km) | Low |
D3 | PN, T1 | 64.28 | 16.42% | Multi-center | Low |
D4 | DG, T2 | 724.35 | 2.61% | Multi-center | High |
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Liu, Y.; Hu, Y.; Long, S.; Liu, L.; Liu, X. Analysis of the Effectiveness of Urban Land-Use-Change Models Based on the Measurement of Spatio-Temporal, Dynamic Urban Growth: A Cellular Automata Case Study. Sustainability 2017, 9, 796. https://doi.org/10.3390/su9050796
Liu Y, Hu Y, Long S, Liu L, Liu X. Analysis of the Effectiveness of Urban Land-Use-Change Models Based on the Measurement of Spatio-Temporal, Dynamic Urban Growth: A Cellular Automata Case Study. Sustainability. 2017; 9(5):796. https://doi.org/10.3390/su9050796
Chicago/Turabian StyleLiu, Yilun, Yueming Hu, Shaoqiu Long, Luo Liu, and Xiaoping Liu. 2017. "Analysis of the Effectiveness of Urban Land-Use-Change Models Based on the Measurement of Spatio-Temporal, Dynamic Urban Growth: A Cellular Automata Case Study" Sustainability 9, no. 5: 796. https://doi.org/10.3390/su9050796