Multi-Scenario Prediction of Intra-Urban Land Use Change Using a Cellular Automata-Random Forest Model
Abstract
:1. Introduction
2. Study Area and Data Processing
2.1. Study Area
2.2. Land Use
2.3. Driving Factors
3. Methods
3.1. CA-RF Model
3.2. Validation
4. Results
4.1. Simulation and Validation
4.2. Scenario I
4.3. Scenario II
5. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
References
- Gu, W.; Guo, J.; Fan, K.; Chan, E.H.W. Dynamic Land Use Change and Sustainable Urban Development in a Third-Tier City within Yangtze Delta. Procedia Environ. Sci. 2016, 36, 98–105. [Google Scholar] [CrossRef] [Green Version]
- Musakwa, W.; Niekerk, A.V. Implications of Land Use Change for the Sustainability of Urban Areas: A Case Study of Stellenbosch, South Africa. Cities 2013, 32, 143–156. [Google Scholar] [CrossRef]
- Zhang, H.; Uwasu, M.; Hara, K.; Yabar, H. Land Use Change Patterns and Sustainable Urban Development in China. J. Asian Archit. Build. Eng. 2010, 9, 131–138. [Google Scholar] [CrossRef]
- Karakus, C.B.; Cerit, O.; Kavak, K.S. Determination of Land Use/Cover Changes and Land Use Potentials of Sivas City and Its Surroundings Using Geographical Information Systems (GIS) and Remote Sensing (RS). Procedia Earth Planet. Sci. 2015, 15, 454–461. [Google Scholar] [CrossRef] [Green Version]
- Verburg, P.H.; Schot, P.P.; Dijst, M.J.; Veldkamp, A. Land Use Change Modelling: Current Practice and Research Priorities. GeoJournal 2004, 61, 309–324. [Google Scholar] [CrossRef]
- Yang, Y.; Bao, W.; Liu, Y. Scenario Simulation of Land System Change in the Beijing-Tianjin-Hebei Region. Land Use Policy 2020, 96, 104677. [Google Scholar] [CrossRef]
- Aburas, M.M.; Ahamad, M.S.S.; Omar, N.Q. Spatio-Temporal Simulation and Prediction of Land-Use Change Using Conventional and Machine Learning Models: A Review. Environ. Monit. Assess. 2019, 191, 205. [Google Scholar] [CrossRef]
- Mustafa, A.; Cools, M.; Saadi, I.; Teller, J. Coupling Agent-Based, Cellular Automata and Logistic Regression into a Hybrid Urban Expansion Model (HUEM). Land Use Policy 2017, 69, 529–540. [Google Scholar] [CrossRef] [Green Version]
- Chang-Martínez, L.A.; Mas, J.-F.; Valle, N.T.; Torres, P.S.U.; Folan, W.J. Modeling Historical Land Cover and Land Use: A Review FromContemporary Modeling. ISPRS Int. J. Geo-Inf. 2015, 4, 1791–1812. [Google Scholar] [CrossRef] [Green Version]
- Qian, Y.; Xing, W.; Guan, X.; Yang, T.; Wu, H. Coupling Cellular Automata with Area Partitioning and Spatiotemporal Convolution for Dynamic Land Use Change Simulation. Sci. Total Environ. 2020, 722, 137738. [Google Scholar] [CrossRef] [PubMed]
- Zhou, L.; Dang, X.; Sun, Q.; Wang, S. Multi-Scenario Simulation of Urban Land Change in Shanghai by Random Forest and CA-Markov Model. Sustain. Cities Soc. 2020, 55, 102045. [Google Scholar] [CrossRef]
- Xing, W.; Qian, Y.; Guan, X.; Yang, T.; Wu, H. A Novel Cellular Automata Model Integrated with Deep Learning for Dynamic Spatio-Temporal Land Use Change Simulation. Comput. Geosci. 2020, 137, 104430. [Google Scholar] [CrossRef]
- Sun, J.; Zhang, L.; Peng, C.; Peng, Z.; Xu, M. CA-Based Urban Land Use Prediction Model: A Case Study on Orange County, Florida, U.S. J. Transp. Syst. Eng. Inf. Technol. 2012, 12, 85–92. [Google Scholar] [CrossRef]
- Tayyebi, A.; Delavar, M.R.; Yazdanpanah, M.J.; Pijanowski, B.C.; Saeedi, S.; Tayyebi, A.H. A Spatial Logistic Regression Model for Simulating Land Use Patterns: A Case Study of the Shiraz Metropolitan Area of Iran. In Advances in Earth Observation of Global Change; Chuvieco, E., Li, J., Yang, X., Eds.; Springer: Dordrecht, The Netherlands, 2010; pp. 27–42. ISBN 978-90-481-9085-0. [Google Scholar]
- Feng, Y.; Liu, Y.; Tong, X. Comparison of Metaheuristic Cellular Automata Models: A Case Study of Dynamic Land Use Simulation in the Yangtze River Delta. Comput. Environ. Urban Syst. 2018, 70, 138–150. [Google Scholar] [CrossRef]
- Gharaibeh, A.; Shaamala, A.; Obeidat, R.; Al-Kofahi, S. Improving Land-Use Change Modeling by Integrating ANN with Cellular Automata-Markov Chain Model. Heliyon 2020, 6, e05092. [Google Scholar] [CrossRef]
- Li, T.; Li, W. Multiple Land Use Change Simulation with Monte Carlo Approach and CA-ANN Model, a Case Study in Shenzen, China. Environ. Syst. Res. 2015, 4, 1–10. [Google Scholar] [CrossRef] [Green Version]
- Qiang, Y.; Lam, N.S.N. Modeling Land Use and Land Cover Changes in a Vulnerable Coastal Region Using Artificial Neural Networks and Cellular Automata. Environ. Monit. Assess. 2015, 187, 57. [Google Scholar] [CrossRef] [PubMed]
- Li, X.; Yeh, A.G.-O. Neural-Network-Based Cellular Automata for Simulating Multiple Land Use Changes Using GIS. Int. J. Geogr. Inf. Sci. 2002, 16, 323–343. [Google Scholar] [CrossRef]
- Liu, Y.; Feng, Y. Simulating the Impact of Economic and Environmental Strategies on Future Urban Growth Scenarios in Ningbo, China. Sustainability 2016, 8, 1045. [Google Scholar] [CrossRef] [Green Version]
- Mustafa, A.; Rienow, A.; Saadi, I.; Cools, M.; Teller, J. Comparing Support Vector Machines with Logistic Regression for Calibrating Cellular Automata Land Use Change Models. Eur. J. Remote Sens. 2018, 51, 391–401. [Google Scholar] [CrossRef]
- Yang, Q.; Li, X.; Shi, X. Cellular Automata for Simulating Land Use Changes Based on Support Vector Machines. Comput. Geosci. 2008, 34, 592–602. [Google Scholar] [CrossRef]
- Zhang, D.; Liu, X.; Yao, Y.; Zhang, J. Simulating Spatio-Temporal Change of Multiple Land Use Types in Dongguan by Using Random Forest Based on Cellular Automata. Geogr. Geo-Inf. Sci. 2016, 32, 32–37. [Google Scholar]
- Kamusoko, C.; Gamba, J. Simulating Urban Growth Using a Random Forest-Cellular Automata (RF-CA) Model. ISPRS Int. J. Geo-Inf. 2015, 4, 447–470. [Google Scholar] [CrossRef]
- Breiman, L. Random Forests. Mach. Learn. 2001, 45, 5–32. [Google Scholar] [CrossRef] [Green Version]
- Zhang, D.; Liu, X.; Wu, X.; Yao, Y.; Wu, X.; Chen, Y. Multiple Intra-Urban Land Use Simulations and Driving Factors Analysis: A Case Study in Huicheng, China. GISci. Remote Sens. 2019, 56, 282–308. [Google Scholar] [CrossRef]
- Kim, Y.; Newman, G.; Güneralp, B. A Review of Driving Factors, Scenarios, and Topics in Urban Land Change Models. Land 2020, 9, 246. [Google Scholar] [CrossRef]
- Domingo, D.; Palka, G.; Hersperger, A.M. Effect of Zoning Plans on Urban Land-Use Change: A Multi-Scenario Simulation for Supporting Sustainable Urban Growth. Sustain. Cities Soc. 2021, 69, 102833. [Google Scholar] [CrossRef]
- Zhang, H.; Zeng, Y.; Bian, L.; Yu, X. Modelling Urban Expansion Using a Multi Agent-Based Model in the City of Changsha. J. Geogr. Sci. 2010, 20, 540–556. [Google Scholar] [CrossRef]
- Li, L.; Sato, Y.; Zhu, H. Simulating Spatial Urban Expansion Based on a Physical Process. Landsc. Urban Plan. 2003, 64, 67–76. [Google Scholar] [CrossRef]
- Gong, J.; Hu, Z.; Chen, W.; Liu, Y.; Wang, J. Urban Expansion Dynamics and Modes in Metropolitan Guangzhou, China. Land Use Policy 2018, 72, 100–109. [Google Scholar] [CrossRef]
- Feng, Y.; Cai, Z.; Tong, X.; Wang, J.; Gao, C.; Chen, S.; Lei, Z. Urban Growth Modeling and Future Scenario Projection Using Cellular Automata (CA) Models and the R Package Optimx. ISPRS Int. J. Geo-Inf. 2018, 7, 387. [Google Scholar] [CrossRef] [Green Version]
- Liu, Y.; Li, L.; Chen, L.; Zhou, X.; Cui, Y.; Li, H.; Liu, W. Urban Growth Simulation in Different Scenarios Using the SLEUTH Model: A Case Study of Hefei, East China. PLoS ONE 2019, 14, e0224998. [Google Scholar] [CrossRef] [Green Version]
- Gounaridis, D.; Chorianopoulos, I.; Symeonakis, E.; Koukoulas, S. A Random Forest-Cellular Automata Modelling Approach to Explore Future Land Use/Cover Change in Attica (Greece), under Different Socio-Economic Realities and Scales. Sci. Total Environ. 2019, 646, 320–335. [Google Scholar] [CrossRef] [PubMed]
- Matsusaka City Urban Development Master Plan. Available online: https://www.city.matsusaka.mie.jp/site/toshikeikaku/toshimasunew.html (accessed on 1 July 2021).
- Shiraoka City Urban Development Master Plan. Available online: http://www.city.shiraoka.lg.jp/9442.htm (accessed on 1 July 2021).
- Location Normalization Plan of Kumamoto City. Available online: https://www.city.kumamoto.jp/common/UploadFileDsp.aspx?c_id=5&id=9398&sub_id=4&flid=80022 (accessed on 12 May 2021).
- Li, C.; Wu, K.; Wu, J. Urban Land Use Change and Its Socio-Economic Driving Forces in China: A Case Study in Beijing, Tianjin and Hebei Region. Environ. Dev. Sustain. 2018, 20, 1405–1419. [Google Scholar] [CrossRef]
- Chen, G.; Li, X.; Liu, X.; Chen, Y.; Liang, X.; Leng, J.; Xu, X.; Liao, W.; Qiu, Y.; Wu, Q.; et al. Global Projections of Future Urban Land Expansion under Shared Socioeconomic Pathways. Nat. Commun. 2020, 11, 537. [Google Scholar] [CrossRef] [Green Version]
- Geospatial Information Authority of Japan. Available online: https://www.gsi.go.jp/ENGLISH/index.html (accessed on 12 May 2021).
- Basic Resident Register Population of Kumamoto City. Available online: http://tokei.city.kumamoto.jp/content/ASP/Jinkou/default.asp (accessed on 12 May 2021).
- National Land Numerical Information. Available online: https://nlftp.mlit.go.jp/ksj/ (accessed on 12 May 2021).
- Japan Seismic Hazard Information Station. Available online: https://www.j-shis.bosai.go.jp/ (accessed on 12 May 2021).
- Feng, Y.; Tong, X. Using Exploratory Regression to Identify Optimal Driving Factors for Cellular Automaton Modeling of Land Use Change. Environ. Monit. Assess. 2017, 189, 1–17. [Google Scholar] [CrossRef]
- White, R.; Engelen, G. Cellular Automata and Fractal Urban Form: A Cellular Modelling Approach to the Evolution of Urban Land-Use Patterns. Environ. Plan. A 1993, 25, 1175–1199. [Google Scholar] [CrossRef] [Green Version]
- Scikit-Learn: Machine Learning in Python—Scikit-Learn 0.24.2 Documentation. Available online: https://scikit-learn.org/stable/ (accessed on 12 May 2021).
- Li, Q.; Wang, L.; Gul, H.N.; Li, D. Simulation and Optimization of Land Use Pattern to Embed Ecological Suitability in an Oasis Region: A Case Study of Ganzhou District, Gansu Province, China. J. Environ. Manag. 2021, 287, 112321. [Google Scholar] [CrossRef]
- Kang, J.; Fang, L.; Li, S.; Wang, X. Parallel Cellular Automata Markov Model for Land Use Change Prediction over MapReduce Framework. ISPRS Int. J. Geo-Inf. 2019, 8, 454. [Google Scholar] [CrossRef] [Green Version]
- Sim, J.; Wright, C.C. The Kappa Statistic in Reliability Studies: Use, Interpretation, and Sample Size Requirements. Phys. Ther. 2005, 85, 257–268. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Saputra, M.H.; Lee, H.S. Prediction of Land Use and Land Cover Changes for North Sumatra, Indonesia, Using an Artificial-Neural-Network-Based Cellular Automaton. Sustainability 2019, 11, 3024. [Google Scholar] [CrossRef] [Green Version]
- Wang, R.; Hou, H.; Murayama, Y. Scenario-Based Simulation of Tianjin City Using a Cellular Automata–Markov Model. Sustainability 2018, 10, 2633. [Google Scholar] [CrossRef] [Green Version]
- Li, F.; Li, Z.; Chen, H.; Chen, Z.; Li, M. An Agent-Based Learning-Embedded Model (ABM-Learning) for Urban Land Use Planning: A Case Study of Residential Land Growth Simulation in Shenzhen, China. Land Use Policy 2020, 95, 104620. [Google Scholar] [CrossRef]
- Jokar Arsanjani, J.; Helbich, M.; Kainz, W.; Darvishi Boloorani, A. Integration of Logistic Regression, Markov Chain and Cellular Automata Models to Simulate Urban Expansion. Int. J. Appl. Earth Obs. Geoinf. 2013, 21, 265–275. [Google Scholar] [CrossRef]
- Feng, Y.; Liu, M.; Chen, L.; Liu, Y. Simulation of Dynamic Urban Growth with Partial Least Squares Regression-Based Cellular Automata in a GIS Environment. ISPRS Int. J. Geo-Inf. 2016, 5, 243. [Google Scholar] [CrossRef] [Green Version]
- Chen, B.K.; Jalal, H.; Hashimoto, H.; Suen, S.; Eggleston, K.; Hurley, M.; Schoemaker, L.; Bhattacharya, J. Forecasting Trends in Disability in a Super-Aging Society: Adapting the Future Elderly Model to Japan. J. Econ. Ageing 2016, 8, 42–51. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wang, R.; Derdouri, A.; Murayama, Y. Spatiotemporal Simulation of Future Land Use/Cover Change Scenarios in the Tokyo Metropolitan Area. Sustainability 2018, 10, 2056. [Google Scholar] [CrossRef] [Green Version]
No | Land Use | Description |
---|---|---|
1 | Non-Urban | Paddy, crop, vegetable fields, forest, wasteland, riverbed, etc. |
2 | Water | The surface of rivers, lakes, reservoirs, irrigation channels, moats, etc. |
3 | Low-Rise Residential (LRR) | Residential areas of 1–2 floors |
4 | Medium High-Rise Residential (MHRR) | Residential areas with more than 3 floors |
5 | Industry | Land for factory facilities |
6 | Commercial | Commercial facilities land |
7 | Public Facility (PF) | City hall, hospitals, schools, etc. |
8 | Public Open Space (POS) | Parks, green spaces, squares, sports grounds, cemeteries, etc. |
9 | Transportation Facility (TF) | Roads, stations, etc. |
10 | Vacant Land (VL) | Parking lots, vacant lots, etc. |
Category | Data | Year | Sources |
---|---|---|---|
Land use | Land use | 2006 | Kumamoto City Basic Survey |
2012 | |||
2018 | |||
Natural environmental factors | Elevation | 2018 | Geospatial Information Authority of Japan [40] (https://www.gsi.go.jp/kiban/ (accessed on 12 May 2021)) |
Slope | 2018 | ||
Socioeconomic factors | Population density | 2018 | Basic Resident Register population of Kumamoto City [41] (http://tokei.city.kumamoto.jp/content/ASP/Jinkou/default.asp (accessed on 12 May 2021)) |
Distance to main roads | 2018 | Kumamoto City Basic Survey | |
Distance to rail stations | 2018 | Kumamoto City Basic Survey | |
Distance to bus stops | 2018 | Kumamoto City Basic Survey | |
Distance to commercial and financial facilities | 2019 | National Land Numerical Information [42] (https://nlftp.mlit.go.jp (accessed on 12 May 2021)) | |
Distance to parks | 2011 | ||
Distance to city hall | 2014 | ||
Distance to schools | 2013 | ||
Distance to hospitals | 2015 | ||
Land price | 2018 | Kumamoto City Basic Survey | |
Policy factors | Distance to the UPA | 2018 | Kumamoto City Basic Survey |
Distance to the RPA | - | Location Normalization Plan of Kumamoto City | |
Distance to the UFPA | - | ||
Disaster-related factors | Flood | 2012 | National Land Numerical Information [42] (https://nlftp.mlit.go.jp (accessed on 12 May 2021)) |
Sediment | 2019 | ||
Earthquake | 2016 | Japan Seismic Hazard Information Station [43] (https://www.j-shis.bosai.go.jp/ (accessed on 12 May 2021)) | |
Constrained factor | UCA | 2018 | Kumamoto City Basic Survey |
Observed Land Use | Simulated Land Use in Scenario I | |||||
---|---|---|---|---|---|---|
2006 | 2012 | 2006–2012 | 2012–2018 | 2018–2024 | 2024–2030 | |
Non-urban | 23,159.25 | 22,609.08 | −550.2 | −537.03 | −522.4 | −502.38 |
Water | 1257.93 | 1257.93 | 0 | 0 | 0 | 0 |
LRR | 5048.37 | 5318.64 | 270.27 | 266.49 | 261.54 | 256.32 |
MHRR | 1704.06 | 1774.71 | 70.65 | 74.25 | 76.14 | 71.37 |
Industry | 655.47 | 660.06 | 4.59 | 4.5 | 4.32 | 4.14 |
Commercial | 947.7 | 959.49 | 11.79 | 15.75 | 18.72 | 20.88 |
PF | 1538.1 | 1556.82 | 18.72 | 19.71 | 20.52 | 21.15 |
POS | 964.53 | 978.57 | 14.04 | 13.86 | 13.59 | 13.41 |
TF | 2264.31 | 2299.05 | 34.74 | 35.73 | 36.63 | 37.26 |
VL | 1494 | 1619.37 | 125.37 | 106.74 | 90.99 | 77.85 |
Observed Land Use | Simulated Land Use in Scenario II | ||||
---|---|---|---|---|---|
2012 | 2018 | 2012–2018 | 2018–2024 | 2024–2030 | |
Non-urban | 22,609.08 | 22,168.26 | −440.82 | −388.98 | −189.99 |
Water | 1257.93 | 1257.93 | 0 | 0 | 0 |
LRR | 5318.64 | 5449.77 | 131.13 | 125.82 | 120.42 |
MHRR | 1774.71 | 1855.17 | 80.46 | 57.06 | 74.52 |
Industry | 660.06 | 664.56 | 4.5 | 0.09 | −6.84 |
Commercial | 959.49 | 975.6 | 16.11 | 15.21 | 6.21 |
PF | 1556.82 | 1566.09 | 9.27 | 8.91 | −8.82 |
POS | 978.57 | 995.13 | 16.56 | 4.05 | −1.89 |
TF | 2299.05 | 2306.43 | 7.38 | 7.38 | 4.05 |
VL | 1619.37 | 1794.78 | 175.41 | 170.46 | 2.34 |
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Liu, H.; Homma, R.; Liu, Q.; Fang, C. Multi-Scenario Prediction of Intra-Urban Land Use Change Using a Cellular Automata-Random Forest Model. ISPRS Int. J. Geo-Inf. 2021, 10, 503. https://doi.org/10.3390/ijgi10080503
Liu H, Homma R, Liu Q, Fang C. Multi-Scenario Prediction of Intra-Urban Land Use Change Using a Cellular Automata-Random Forest Model. ISPRS International Journal of Geo-Information. 2021; 10(8):503. https://doi.org/10.3390/ijgi10080503
Chicago/Turabian StyleLiu, Hang, Riken Homma, Qiang Liu, and Congying Fang. 2021. "Multi-Scenario Prediction of Intra-Urban Land Use Change Using a Cellular Automata-Random Forest Model" ISPRS International Journal of Geo-Information 10, no. 8: 503. https://doi.org/10.3390/ijgi10080503
APA StyleLiu, H., Homma, R., Liu, Q., & Fang, C. (2021). Multi-Scenario Prediction of Intra-Urban Land Use Change Using a Cellular Automata-Random Forest Model. ISPRS International Journal of Geo-Information, 10(8), 503. https://doi.org/10.3390/ijgi10080503