995
Views
0
CrossRef citations to date
0
Altmetric
Research Article

A spatial hierarchical learning module based cellular automata model for simulating urban expansion: case studies of three Chinese urban areas

ORCID Icon, , ORCID Icon, , ORCID Icon & ORCID Icon
Article: 2290352 | Received 13 Jun 2023, Accepted 29 Nov 2023, Published online: 11 Dec 2023

References

  • Aburas, M. M., Y. M. Ho, M. F. Ramli, and Z. H. Ash’aari. 2016. “The Simulation and Prediction of Spatio-Temporal Urban Growth Trends Using Cellular Automata Models: A Review.” International Journal of Applied Earth Observation and Geoinformation 52:380–22. https://doi.org/10.1016/j.jag.2016.07.007.
  • Angel, S., J. Parent, D. L. Civco, A. Blei, and D. Potere. 2011. “The Dimensions of Global Urban Expansion: Estimates and Projections for All Countries, 2000–2050.” Progress in Planning 75 (2): 53–107. https://doi.org/10.1016/j.progress.2011.04.001.
  • Chen, G., X. Li, X. Liu, Y. Chen, X. Liang, J. Leng, X. Xu, et al. 2020. “Global Projections of Future Urban Land Expansion Under Shared Socioeconomic Pathways.” Nature Communications 11 (1): 537. https://doi.org/10.1038/s41467-020-14386-x.
  • Chen, G., H. Zhuang, and X. Liu. 2023. “Cell-Level Coupling of a Mechanistic Model to Cellular Automata for Improving Land Simulation.” GIScience & Remote Sensing 60 (1): 21 66443. https://doi.org/10.1080/15481603.2023.2166443.
  • Cohen, J. 1960. “A Coefficient of Agreement for Nominal Scales.” Educational and Psychological Measurement 20 (1): 37–46. https://doi.org/10.1177/001316446002000104.
  • Dahal, K. R., and T. E. Chow. 2015. “Characterization of Neighborhood Sensitivity of an Irregular Cellular Automata Model of Urban Growth.” International Journal of Geographical Information Science 29 (3): 475–497. https://doi.org/10.1080/13658816.2014.987779.
  • Feng, Y., and Y. Liu. 2013. “A Heuristic Cellular Automata Approach for Modelling Urban Land-Use Change Based on Simulated Annealing.” International Journal of Geographical Information Science 27 (3): 449–466. https://doi.org/10.1080/13658816.2012.695377.
  • Feng, Y., and X. Tong. 2019. “Incorporation of Spatial Heterogeneity-Weighted Neighborhood into Cellular Automata for Dynamic Urban Growth Simulation.” GIScience & Remote Sensing 56 (7): 1024–1045. https://doi.org/10.1080/15481603.2019.1603187.
  • Fotheringham, A. S., and D. W. Wong. 1991. “The Modifiable Areal Unit Problem in Multivariate Statistical Analysis.” Environment and Planning A 23 (7): 1025–1044. https://doi.org/10.1068/a231025.
  • Gao, J., and B. C. O’Neill. 2020. “Mapping Global Urban Land for the 21st Century with Data-Driven Simulations and Shared Socioeconomic Pathways.” Nature Communications 11 (1): 2302. https://doi.org/10.1038/s41467-020-15788-7.
  • Geofabrik. 2021. “Data From: OpenStreetmap Data Downloads (Dataset)” . OpenStreetMap Contributors. Accessed January 1, 2022. http://download.geofabrik.de/asia/china.html.
  • Gong, J., Z. Hu, W. Chen, Y. Liu, and J. Wang. 2018. “Urban Expansion Dynamics and Modes in Metropolitan Guangzhou, China.” Land Use Policy 72:100–109. https://doi.org/10.1016/j.landusepol.2017.12.025.
  • Goodchild, M. F. 2004. “The Validity and Usefulness of Laws in Geographic Information Science and Geography.” Annals of the Association of American Geographers 94 (2): 300–303. https://doi.org/10.1111/j.1467-8306.2004.09402008.x.
  • Guan, X., H. Wei, S. Lu, Q. Dai, and H. Su. 2018. “Assessment on the Urbanization Strategy in China: Achievements, Challenges and Reflections.” Habitat International 71:97–109. https://doi.org/10.1016/j.habitatint.2017.11.009.
  • He, J., X. Li, Y. Yao, Y. Hong, and J. Zhang. 2018. “Mining Transition Rules of Cellular Automata for Simulating Urban Expansion by Using the Deep Learning Techniques.” International Journal of Geographical Information Science 32 (10): 2076–2097. https://doi.org/10.1080/13658816.2018.1480783.
  • Hochreiter, S., and J. Schmidhuber. 1997. “Long Short-Term Memory.” Neural Computation 9 (8): 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735.
  • Işınkaralar, O. 2023. “Simulation of Urban Growth’s Pressure on Urban Blue-Green Space Using the CORINE Database for Kocaeli, Türkiye.” Forestist. https://doi.org/10.5152/forestist.2023.22077.
  • Isinkaralar, O., and C. Varol. 2023. “A Cellular Automata-Based Approach for Spatio-Temporal Modeling of the City Center as a Complex System: The Case of Kastamonu, Türkiye.” Cities 132:104073. https://doi.org/10.1016/j.cities.2022.104073.
  • Isinkaralar, O., C. Varol, and D. Yilmaz. 2022. “Digital Mapping and Predicting the Urban Growth: Integrating Scenarios into Cellular Automata—Markov Chain Modeling.” Applied Geomatics 14 (4): 695–705. https://doi.org/10.1007/s12518-022-00464-w.
  • Jelinski, D. E., and J. Wu. 1996. “The Modifiable Areal Unit Problem and Implications for Landscape Ecology.” Landscape Ecology 11 (3): 129–140. https://doi.org/10.1007/BF02447512.
  • Kamusoko, C., and J. Gamba. 2015. “Simulating Urban Growth Using a Random Forest-Cellular Automata (RF-CA) model.” ISPRS International Journal of Geo-Information 4 (2): 447–470. https://doi.org/10.3390/ijgi4020447.
  • Liang, X., Q. Guan, K. C. Clarke, S. Liu, B. Wang, and Y. Yao. 2021. “Understanding the Drivers of Sustainable Land Expansion Using a Patch-Generating Land Use Simulation (PLUS) Model: A Case Study in Wuhan, China.” Computers, Environment and Urban Systems 85:101569. https://doi.org/10.1016/j.compenvurbsys.2020.101569.
  • Liao, J., L. Tang, G. Shao, X. Su, D. Chen, and T. Xu. 2016. “Incorporation of Extended Neighborhood Mechanisms and Its Impact on Urban Land-Use Cellular Automata Simulations.” Environmental Modelling & Software 75:163–175. https://doi.org/10.1016/j.envsoft.2015.10.014.
  • Li, Y., Y. Gong, Z. Zhang, and C. Feng. 2018. “On the Neighborhood Patterns of Urban Land Use Using Vector Grids.” Acta Geographica Sinica 73 (11): 2236–2249. https://doi.org/10.11821/dlxb201811014.
  • Liu, C. 2021. “Impervious Surface Extraction and Urban Expansion Analysis Based on Time Series.” Master diss, China University of Geosciences.
  • Liu, X., X. Liang, X. Li, X. Xu, J. Ou, Y. Chen, S. Li, S. Wang, and F. Pei. 2017. “A Future Land Use Simulation Model (FLUS) for Simulating Multiple Land Use Scenarios by Coupling Human and Natural Effects.” Landscape and Urban Planning 168:94–116. https://doi.org/10.1016/j.landurbplan.2017.09.019.
  • Li, X., Q. Yang, and X. Liu. 2008. “Discovering and Evaluating Urban Signatures for Simulating Compact Development Using Cellular Automata.” Landscape and Urban Planning 86 (2): 177–186. https://doi.org/10.1016/j.landurbplan.2008.02.005.
  • Li, X., and A. G. O. Yeh. 2002. “Neural-Network-Based Cellular Automata for Simulating Multiple Land Use Changes Using GIS.” International Journal of Geographical Information Science 16 (4): 323–343. https://doi.org/10.1080/13658810210137004.
  • Li, X., Y. Zhou, and W. Chen. 2020. “An Improved Urban Cellular Automata Model by Using the Trend-Adjusted Neighborhood.” Ecological Processes 9 (1): 1–13. https://doi.org/10.1186/s13717-020-00234-9.
  • Lv, J., Y. Wang, X. Liang, Y. Yao, T. Ma, and Q. Guan. 2021. “Simulating Urban Expansion by Incorporating an Integrated Gravitational Field Model into a Demand-Driven Random Forest-Cellular Automata Model.” Cities 109:103044. https://doi.org/10.1016/j.cities.2020.103044.
  • Ménard, A., and D. J. Marceau. 2005. “Exploration of Spatial Scale Sensitivity in Geographic Cellular Automata.” Environment and Planning B: Planning and Design 32 (5): 693–714. https://doi.org/10.1068/b31163.
  • National Bureau of Statistics of China. 2014. China Statistical Yearbook. Beijing: China Statistics Press.
  • O’sullivan, D., and P. M. Torrens. 2001. “Cellular Models of Urban Systems.” Paper presented at the Theory and Practical Issues on Cellular Automata: Proceedings of the Fourth International Conference on Cellular Automata for Research and Industry, Karlsruhe.
  • Pontius, R. G., W. Boersma, J. Castella, K. Clarke, T. D. Nijs, C. Dietzel, Z. Duan, et al. 2008. “Comparing the Input, Output, and Validation Maps for Several Models of Land Change.” The Annals of Regional Science 42 (1): 11–37. https://doi.org/10.1007/s00168-007-0138-2.
  • Rafiee, R., A. S. Mahiny, N. Khorasani, A. A. Darvishsefat, and A. Danekar. 2009. “Simulating Urban Growth in Mashad City, Iran Through the SLEUTH Model (UGM).” Cities 26 (1): 19–26. https://doi.org/10.1016/j.cities.2008.11.005.
  • Schaldach, R., J. Alcamo, J. Koch, C. Kölking, D. M. Lapola, J. Schüngel, and J. A. Priess. 2011. “An Integrated Approach to Modelling Land-Use Change on Continental and Global Scales.” Environmental Modelling & Software 26 (8): 1041–1051. https://doi.org/10.1016/j.envsoft.2011.02.013.
  • Seto, K. C., B. Güneralp, and L. R. Hutyra. 2012. “Global Forecasts of Urban Expansion to 2030 and Direct Impacts on Biodiversity and Carbon Pools.” Proceedings of the National Academy of Sciences 109 (40): 16083–16088. https://doi.org/10.1073/pnas.1211658109.
  • Shafizadeh-Moghadam, H., A. Asghari, M. Taleai, M. Helbich, and A. Tayyebi. 2017. “Sensitivity Analysis and Accuracy Assessment of the Land Transformation Model Using Cellular Automata.” GIScience & Remote Sensing 54 (5): 639–656. https://doi.org/10.1080/15481603.2017.1309125.
  • Shojaei, H., S. Nadi, H. Shafizadeh-Moghadam, A. Tayyebi, and J. V. Genderen. 2022. “An Efficient Built-Up Land Expansion Model Using a Modified U-Net.” International Journal of Digital Earth 15 (1): 148–163. https://doi.org/10.1080/17538947.2021.2017035.
  • Tachikawa, T., M. Kaku, A. Iwasaki, D. B. Gesch, M. J. Oimoen, Z. Zhang, J. J. Danielson, et al. 2011. Data from: ASTER Global Digital Elevation Model Version 2-Summary of Validation Results (dataset). National Astronautics and Space Administration. Accessed June 17, 2014. http://www.jspacesystems.or.jp/ersdac/GDEM/ver2Validation/Summary_GDEM2_validation_report_final.pdf.
  • Tobler, W. R. 1970. “A Computer Movie Simulating Urban Growth in the Detroit Region.” Economic Geography 46 (Suppl. 1): 234–240. https://doi.org/10.2307/143141.
  • Tobler, W. R. 1979. “Cellular Geography.” Philosophy in Geography 379–386. https://doi.org/10.1007/978-94-009-9394-5_18.
  • Verburg, P. H., T. C. M. Nijs, J. R. Eck, H. Visser, and K. Jong. 2004. “A Method to Analyse Neighbourhood Characteristics of Land Use Patterns.” Computers, Environment and Urban Systems 28 (6): 667–690. https://doi.org/10.1016/j.compenvurbsys.2003.07.001.
  • Verburg, P. H., and K. P. Overmars. 2009. “Combining Top-Down and Bottom-Up Dynamics in Land Use Modeling: Exploring the Future of Abandoned Farmlands in Europe with the Dyna-CLUE Model.” Landscape Ecology 24 (9): 1167–1181. https://doi.org/10.1007/s10980-009-9355-7.
  • Verburg, P. H., W. Soepboer, A. Veldkamp, R. Limpiada, V. Espaldon, and S. S. A. Mastura. 2002. “Modeling the Spatial Dynamics of Regional Land Use: The CLUE-S Model.” Environmental Management 30 (3): 391–405. https://doi.org/10.1007/s00267-002-2630-x.
  • Vliet, J., N. Naus, R. J. A. Lammeren, A. K. Bregt, J. Hurkens, and H. Delden. 2013. “Measuring the Neighbourhood Effect to Calibrate Land Use Models.” Computers, Environment and Urban Systems 41:55–64. https://doi.org/10.1016/j.compenvurbsys.2013.03.006.
  • Wang, Y., F. Chen, F. Wei, M. Yang, X. Gu, Q. Sun, and X. Wang. 2023. “Spatial and Temporal Characteristics and Evolutionary Prediction of Urban Health Development Efficiency in China: Based on Super-Efficiency SBM Model and Spatial Markov Chain Model.” Ecological Indicators 147:109985. https://doi.org/10.1016/j.ecolind.2023.109985.
  • Wang, H., J. Guo, B. Zhang, and H. Zeng. 2021. “Simulating Urban Land Growth by Incorporating Historical Information into a Cellular Automata Model.” Landscape and Urban Planning 214:104168. https://doi.org/10.1016/j.landurbplan.2021.104168.
  • Wang, Q., and X. Zang. 2018. “Research on the Origin, Characteristics and Planning Adaptability Strategy of Urban Block System.” City Planning 42 (9): 131–138.
  • White, R., and G. Engelen. 1993. “Cellular Automata and Fractal Urban Form: A Cellular Modelling Approach to the Evolution of Urban Land-Use Patterns.” Environment and Planning A 25 (8): 1175–1199. https://doi.org/10.1068/a251175.
  • White, R., and G. Engelen. 2000. “High-Resolution Integrated Modelling of the Spatial Dynamics of Urban and Regional Systems.” Computers, Environment and Urban Systems 24 (5): 383–400. https://doi.org/10.1016/S0198-9715(00)00012-0.
  • Wu, F. 2002. “Calibration of Stochastic Cellular Automata: The Application to Rural-Urban Land Conversions.” International Journal of Geographical Information Science 16 (8): 795–818. https://doi.org/10.1080/13658810210157769.
  • Wu, H., Z. Li, K. C. Clarke, W. Shi, L. Fang, A. Lin, and J. Zhou. 2019. “Examining the Sensitivity of Spatial Scale in Cellular Automata Markov Chain Simulation of Land Use Change.” International Journal of Geographical Information Science 33 (5): 1040–1061. https://doi.org/10.1080/13658816.2019.1568441.
  • Wu, X., X. Liu, D. Zhang, J. Zhang, J. He, and X. Xu. 2022. “Simulating Mixed Land-Use Change Under Multi-Label Concept by Integrating a Convolutional Neural Network and Cellular Automata: A Case Study of Huizhou, China.” GIScience & Remote Sensing 59 (1): 609–632. https://doi.org/10.1080/15481603.2022.2049493.
  • Wu, H., L. Zhou, X. Chi, Y. Li, and Y. Sun. 2012. “Quantifying and Analyzing Neighborhood Configuration Characteristics to Cellular Automata for Land Use Simulation Considering Data Source Error.” Earth Science Informatics 5 (2): 77–86. https://doi.org/10.1007/s12145-012-0097-8.
  • Xie, W., Q. Huang, C. He, and X. Zhao. 2018. “Projecting the Impacts of Urban Expansion on Simultaneous Losses of Ecosystem Services: A Case Study in Beijing, China.” Ecological Indicators 84:183–193. https://doi.org/10.1016/j.ecolind.2017.08.055.
  • Xie, Z., H. Wang, B. Zhang, and X. Huang. 2020. “Urban Expansion Cellular Automata Model Based on Multi-Structures Convolutional Neural Networks.” Acta Geodaetica et Cartographica Sinica 49 (3): 375–385. https://doi.org/10.11947/j.AGCS.2020.20190147.
  • Xu, X. 2017a. Data from: China GDP Spatial Distribution Kilometer Grid Data Set (dataset). Data Registration and Publishing System of Resource and Environmental Science Data Center, Chinese Academy of Sciences. http://www.resdc.cn/doi/doi.aspx?doiid=33.
  • Xu, X. 2017b. Data From: China Population Spatial Distribution Kilometer Grid Data Set (Dataset). Data Registration and Publishing System of Resource and Environmental Science Data Center, Chinese Academy of Sciences. http://www.resdc.cn/doi/doi.aspx?doiid=32.
  • Yang, J., and X. Huang. 2021. “The 30 M Annual Land Cover Dataset and Its Dynamics in China from 1990 to 2019.” Earth System Science Data 13 (8): 3907–3925. https://doi.org/10.5194/essd-13-3907-2021.
  • Yang, Q., X. Li, and X. Shi. 2008. “Cellular Automata for Simulating Land Use Changes Based on Support Vector Machines.” Computers & Geosciences 34 (6): 592–602. https://doi.org/10.1016/j.cageo.2007.08.003.
  • Yeh, A. G. O., and X. Li. 2003. “Simulation of Development Alternatives Using Neural Networks, Cellular Automata, and GIS for Urban Planning.” Photogrammetric Engineering & Remote Sensing 69 (9): 1043–1052. https://doi.org/10.14358/PERS.69.9.1043.
  • Yeh, A. G. O., X. Li, and C. Xia. 2021. “Cellular Automata Modeling for Urban and Regional Planning.” Urban Informatics 865–883. https://doi.org/10.1007/978-981-15-8983-6_45.
  • Yew, C. P. 2012. “Pseudo-Urbanization? Competitive Government Behavior and Urban Sprawl in China.” Journal of Contemporary China 21 (74): 281–298. https://doi.org/10.1080/10670564.2012.635931.
  • Yu, W., J. Shi, Y. Fang, A. Xiang, X. Li, C. Hu, and M. Ma. 2022. “Exploration of Urbanization Characteristics and Their Effect on the Urban Thermal Environment in Chengdu, China.” Building and Environment 219:109150. https://doi.org/10.1016/j.buildenv.2022.109150.
  • Zhai, Y., Y. Yao, Q. Guan, X. Liang, X. Li, Y. Pan, H. Yue, Z. Yuan, and J. Zhou. 2020. “Simulating Urban Land Use Change by Integrating a Convolutional Neural Network with Vector-Based Cellular Automata.” International Journal of Geographical Information Science 34 (7): 1475–1499. https://doi.org/10.1080/13658816.2020.1711915.
  • Zhang, D., X. Liu, X. Wu, Y. Yao, X. Wu, and Y. Chen. 2019. “Multiple Intra-Urban Land Use Simulations and Driving Factors Analysis: A Case Study in Huicheng, China.” GIScience & Remote Sensing 56 (2): 282–308. https://doi.org/10.1080/15481603.2018.1507074.
  • Zhao, B., X. Tan, X. Yang, Y. Shi, and M. Deng. 2023. “A Cellular Automata Model Incorporating Geographical Condition-Driven Effects and Graph Convolutional Network for Land Use Evolution Simulation.” Acta Geodaetica et Cartographica Sinica 52 (5): 831–842. https://doi.org/10.11947/j.AGCS.2023.20220145.