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Research Article

Enhancing outlying growth simulation in urban cellular automata via intelligent extraction-fusion of land suitability and neighborhood effects: a case study of Wuhan, China

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Received 10 Sep 2023, Accepted 07 Apr 2024, Published online: 24 Apr 2024

References

  • Batty, M. 1998. “Urban Evolution on the Desktop: Simulation with the Use of Extended Cellular Automata.” Environment and Planning A 30 (11): 1943–1967. https://doi.org/10.1068/a301943.
  • Cao, M., S. J. Bennett, Q. Shen, and R. Xu. 2016. “A Bat-Inspired Approach to Define Transition Rules for a Cellular Automaton Model Used to Simulate Urban Expansion.” International Journal of Geographical Information Science 30 (10): 1961–1979. https://doi.org/10.1080/13658816.2016.1151521.
  • Cen, Y., P. Zhang, and L. Shi. 2008. “Spatial-Temporal Characteristics of LUCC in Wuhan Area Using Satellite Data.” Paper presented at International Conference on Earth Observation Data Processing and Analysis, Wuhan, China, December. https://doi.org/10.1117/12.815940.
  • 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). https://doi.org/10.1038/s41467-020-14386-x.
  • Chen, S., Y. Feng, X. Tong, S. Liu, H. Xie, C. Gao, and Z. Lei. 2020. “Modeling ESV Losses Caused by Urban Expansion Using Cellular Automata and Geographically Weighted Regression.” Science of the Total Environment 712:136509. https://doi.org/10.1016/j.scitotenv.2020.136509.
  • Clarke, K. C., and L. J. Gaydos. 1998. “Loose-Coupling a Cellular Automaton Model and GIS: Long-Term Urban Growth Prediction for San Francisco and Washington/Baltimore.” International Journal of Geographical Information Science 12 (7): 699–714. https://doi.org/10.1080/136588198241617.
  • Clarke, K. C., S. Hoppen, and L. Gaydos. 1997. “A Self-Modifying Cellular Automaton Model of Historical Urbanization in the San Francisco Bay Area.” Environment and Planning B: Planning and Design 24 (2): 247–261. https://doi.org/10.1068/b240247.
  • 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.
  • Darwish, A., S. Elghazali, and A. Shakweer. 2007. “The Effect of Ring Road Construction on Urban Land Cover Change: Greater Cairo Case Study.” Paper presented at Proceedings of the 2007 Urban Remote Sensing Joint Event, Paris, France, April. https://doi.org/10.1109/URS.2007.371763.
  • Feng, Y., Y. Liu, X. Tong, M. Liu, and S. Deng. 2011. “Modeling Dynamic Urban Growth Using Cellular Automata and Particle Swarm Optimization Rules.” Landscape and Urban Planning 102 (3): 188–196. https://doi.org/10.1016/j.landurbplan.2011.04.004.
  • Feng, Y., and X. Tong. 2020. “A New Cellular Automata Framework of Urban Growth Modeling by Incorporating Statistical and Heuristic Methods.” International Journal of Geographical Information Science 34 (1): 74–97. https://doi.org/10.1080/13658816.2019.1648813.
  • Feng, Y., R. Wang, X. Tong, and H. Shafizadeh-Moghadam. 2019. “How Much Can Temporally Stationary Factors Explain Cellular Automata-Based Simulations of Past and Future Urban Growth?” Computers, Environment and Urban Systems 76:150–162. https://doi.org/10.1016/j.compenvurbsys.2019.04.010.
  • Feng, Y., P. Wu, X. Tong, P. Li, R. Wang, Y. Zhou, J. Wang, and J. Zhao. 2022. “The Effects of Factor Generalization Scales on the Reproduction of Dynamic Urban Growth.” Geo-Spatial Information Science 25 (3): 457–475. https://doi.org/10.1080/10095020.2022.2025748.
  • Fonstad, M. A. 2006. “Cellular Automata as Analysis and Synthesis Engines at the Geomorphology–Ecology Interface.” Geomorphology 77 (3–4): 217–234. https://doi.org/10.1016/j.geomorph.2006.01.006.
  • Gao, C., Y. Feng, X. Tong, Z. Lei, S. Chen, and S. Zhai. 2020. “Modeling Urban Growth Using Spatially Heterogeneous Cellular Automata Models: Comparison of Spatial Lag, Spatial Error and GWR.” Computers, Environment and Urban Systems 81:101459. https://doi.org/10.1016/j.compenvurbsys.2020.101459.
  • Guan, X., W. Xing, J. Li, and H. Wu. 2023. “HGAT-VCA: Integrating High-Order Graph Attention Network with Vector Cellular Automata for Urban Growth Simulation.” Computers, Environment and Urban Systems 99. https://doi.org/10.1016/j.compenvurbsys.2022.101900.
  • He, J., X. Li, Y. Yao, Y. Hong, and Z. Jinbao. 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.
  • Hu, Z., J. Peng, Y. Hou, and J. Shan. 2017. “Evaluation of Recently Released Open Global Digital Elevation Models of Hubei, China.” Remote Sensing 9 (3): 262. https://doi.org/10.3390/rs9030262.
  • Isinkaralar, O., K. Isinkaralar, and E. P. Bayraktar. 2023. Monitoring the Spatial Distribution Pattern According to Urban Land Use and Health Risk Assessment on Potential Toxic Metal Contamination via Street Dust in Ankara, Türkiye. Environmental Monitoring and Assessment 195 (9). https://doi.org/10.1007/s10661-023-11705-9.
  • Isinkaralar, O., K. Isinkaralar, and D. Yilmaz. 2023. “Climate-Related Spatial Reduction Risk of Agricultural Lands on the Mediterranean Coast in Türkiye and Scenario-Based Modelling of Urban Growth.” Environment Development and Sustainability 25 (11): 13199–13217. https://doi.org/10.1007/s10668-023-03774-0.
  • 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.
  • 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.
  • Kasraian, D., K. Maat, and B. V. Wee. 2019. “The Impact of Urban Proximity, Transport Accessibility and Policy on Urban Growth: A Longitudinal Analysis Over Five Decades.” Environment and Planning B: Urban Analytics and City Science 46 (6): 1000–1017. https://doi.org/10.1177/2399808317740355.
  • Kelobonye, K., J. C. Xia, M. S. H. Swapan, G. McCarney, and H. Zhou. 2019. “Drivers of Change in Urban Growth Patterns: A Transport Perspective from Perth, Western Australia.” Urban Science 3 (2): 40. https://doi.org/10.3390/urbansci3020040.
  • Li, Q., Y. Feng, X. Tong, Y. Zhou, P. Wu, H. Xie, Y. Jin, et al. 2022. “Firefly Algorithm-Based Cellular Automata for Reproducing Urban Growth and Predicting Future Scenarios.” Sustainable Cities and Society 76:103444. https://doi.org/10.1016/j.scs.2021.103444.
  • Li, X., Q. Yang, and X. Liu. 2007. “Genetic Algorithms for Determining the Parameters of Cellular Automata in Urban Simulation.” Science in China Series D: Earth Sciences 50 (12): 1857–1866. https://doi.org/10.1007/s11430-007-0127-4.
  • Li, X., and A. G. O. Yeh. 2000. “Modelling Sustainable Urban Development by the Integration of Constrained Cellular Automata and GIS.” International Journal of Geographical Information Science 14 (2): 131–152. https://doi.org/10.1080/136588100240886.
  • 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.
  • Liu, D., K. C. Clarke, and N. Chen. 2020. “Integrating Spatial Nonstationarity into SLEUTH for Urban Growth Modeling: A Case Study in the Wuhan Metropolitan Area.” Computers, Environment and Urban Systems 84:101545. https://doi.org/10.1016/j.compenvurbsys.2020.101545.
  • Liu, X., X. Li, Y. Chen, Z. Tan, S. Li, and B. Ai. 2010. “A New Landscape Index for Quantifying Urban Expansion Using Multi-Temporal Remotely Sensed Data.” Landscape Ecology 25 (5): 671–682. https://doi.org/10.1007/s10980-010-9454-5.
  • Liu, X., X. Li, L. Liu, J. He, and B. Ai. 2008. “A Bottom-Up Approach to Discover Transition Rules of Cellular Automata Using Ant Intelligence.” International Journal of Geographical Information Science 22 (11–12): 1247–1269. https://doi.org/10.1080/13658810701757510.
  • 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.
  • Liu, X., L. Ma, X. Li, B. Ai, S. Li, and Z. He. 2014. “Simulating Urban Growth by Integrating Landscape Expansion Index (LEI) and Cellular Automata.” International Journal of Geographical Information Science 28 (1): 148–163. https://doi.org/10.1080/13658816.2013.831097.
  • Liu, Y., Q. He, R. Tan, Y. Liu, and C. Yin. 2016. “Modeling Different Urban Growth Patterns Based on the Evolution of Urban Form: A Case Study from Huangpi, Central China.” Applied Geography 66:109–118. https://doi.org/10.1016/j.apgeog.2015.11.012.
  • Lu, H., and H. Gan. 2022. “Evaluation and Prevention and Control Measures of Urban Public Transport Exposure Risk Under the Influence of COVID-19—Taking Wuhan As an Example.” PLOS ONE 17 (6): e0267878. https://doi.org/10.1371/journal.pone.0267878.
  • Lundberg, S. M., and S. I. Lee. 2017. “A unified approach to interpreting model predictions.” Paper presented at Proceedings of the Advances in Neural Information Processing Systems, Long Beach, USA, December 4-9.
  • Masoumi, Z., and J. V. Genderen. 2023. “Artificial Intelligence for Sustainable Development of Smart Cities and Urban Land-Use Management.” Geo-Spatial Information Science 1–25. https://doi.org/10.1080/10095020.2023.2184729.
  • Meng, Y., M. S. Wong, M. P. Kwan, J. Pearce, and Z. Feng. 2023. “Assessing Multi-Spatial Driving Factors of Urban Land Use Transformation in Megacities: A Case Study of Guangdong–Hong Kong–Macao Greater Bay Area from 2000 to 2018.” Geo-Spatial Information Science 1–17. https://doi.org/10.1080/10095020.2023.2255033.
  • Moghadam, H. S., and M. Helbich. 2013. “Spatiotemporal Urbanization Processes in the Megacity of Mumbai, India: A Markov Chains-Cellular Automata Urban Growth Model.” Applied Geography 40:140–149. https://doi.org/10.1016/j.apgeog.2013.01.009.
  • Mustafa, A., A. Heppenstall, H. Omrani, I. Saadi, M. Cools, and J. Teller. 2018. “Modelling Built-Up Expansion and Densification with Multinomial Logistic Regression, Cellular Automata and Genetic Algorithm.” Computers, Environment and Urban Systems 67:147–156. https://doi.org/10.1016/j.compenvurbsys.2017.09.009.
  • Pal, S., and S. K. Ghosh. 2017. “Rule Based End-To-End Learning Framework for Urban Growth Prediction.” arXiv preprint arXiv:1711.10801.
  • Qian, Y., W. Xing, X. Guan, T. Yang, and H. Wu. 2020. “Coupling Cellular Automata with Area Partitioning and Spatiotemporal Convolution for Dynamic Land Use Change Simulation.” Science of the Total Environment 722. https://doi.org/10.1016/j.scitotenv.2020.137738.
  • Southworth, M., and P. M. Owens. 1993. “The Evolving Metropolis: Studies of Community, Neighborhood, and Street Form at the Urban Edge.” Journal of the American Planning Association 59 (3): 271–287. https://doi.org/10.1080/01944369308975880.
  • Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin. 2017. “Attention Is All You Need.” Paper presented at Proceedings of the Advances in Neural Information Processing Systems, Long Beach, USA, December 4-9.
  • Wang, J., M. Hadjikakou, R. J. Hewitt, and B. A. Bryan. 2022. “Simulating Large-Scale Urban Land-Use Patterns and Dynamics Using the U-Net Deep Learning Architecture.” Computers, Environment and Urban Systems 97:101855. https://doi.org/10.1016/j.compenvurbsys.2022.101855.
  • Wang, W., L. Jiao, T. Dong, Z. Xu, and G. Xu. 2019. “Simulating Urban Dynamics by Coupling Top-Down and Bottom-Up Strategies.” International Journal of Geographical Information Science 33 (11): 2259–2283. https://doi.org/10.1080/13658816.2019.1647540.
  • 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.
  • Woo, S., J. Park, J. Y. Lee, and I. S. Kweon. 2018. “Cbam: Convolutional Block Attention Module.” Paper presented at Proceedings of the European Conference on Computer Vision, Munich, Germany, September 8-14.
  • 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., 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.
  • Xing, W., Y. Qian, X. Guan, T. Yang, and H. Wu. 2020. “A Novel Cellular Automata Model Integrated with Deep Learning for Dynamic Spatio-Temporal Land Use Change Simulation.” Computers & Geosciences 137:104430. https://doi.org/10.1016/j.cageo.2020.104430.
  • Xu, X., D. Zhang, X. Liu, J. Ou, and X. Wu. 2022. “Simulating Multiple Urban Land Use Changes by Integrating Transportation Accessibility and a Vector-Based Cellular Automata: A Case Study on City of Toronto.” Geo-Spatial Information Science 25 (3): 439–456. https://doi.org/10.1080/10095020.2022.2043730.
  • Yamashita, R., M. Nishio, R. K. G. Do, and K. Togashi. 2018. “Convolutional Neural Networks: An Overview and Application in Radiology.” Insights into Imaging 9 (4): 611–629. https://doi.org/10.1007/s13244-018-0639-9.
  • Yan, X., Y. Feng, X. Tong, P. Li, Y. Zhou, P. Wu, H. Xie, et al. 2021. “Reducing Spatial Autocorrelation in the Dynamic Simulation of Urban Growth Using Eigenvector Spatial Filtering.” International Journal of Applied Earth Observation and Geoinformation 102:102434. https://doi.org/10.1016/j.jag.2021.102434.
  • Yan, X., J. Zhou, F. Sheng, and Q. Niu. 2022. “Influences of Built Environment at Residential and Work Locations on Commuting Distance: Evidence from Wuhan, China.” ISPRS International Journal of Geo-Information 11 (2): 124. https://doi.org/10.3390/ijgi11020124.
  • 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.
  • Zeng, H., H. Wang, B. Zhang, and Q. Wang. 2023. “A Methodology to Quantify the Neighborhood Decay Effect of Urban Cellular Automata Models.” International Journal of Geographical Information Science 37 (6): 1236–1263. https://doi.org/10.1080/13658816.2023.2186412.
  • 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, B., S. Hu, H. Wang, and H. Zeng. 2023. “A Size-Adaptive Strategy to Characterize Spatially Heterogeneous Neighborhood Effects in Cellular Automata Simulation of Urban Growth.” Landscape and Urban Planning 229. https://doi.org/10.1016/j.landurbplan.2022.104604.
  • Zhang, B., and C. Xia. 2022. “The Effects of Sample Size and Sample Prevalence on Cellular Automata Simulation of Urban Growth.” International Journal of Geographical Information Science 36 (1): 158–187. https://doi.org/10.1080/13658816.2021.1931237.
  • Zhao, D., and T. F. Sing. 2017. “Air Pollution, Economic Spillovers, and Urban Growth in China.” The Annals of Regional Science 58 (2): 321–340. https://doi.org/10.1007/s00168-016-0783-4.
  • Zhu, Q., M. Zeng, P. Jia, M. Guo, X. Liang, and Q. Guan. 2023. “Measuring the Urban Sprawl Based on Economic-Dominated Perspective: The Case of 31 Municipalities and Provincial Capitals.” Geo-Spatial Information Science 1–18. https://doi.org/10.1080/10095020.2023.2202201.