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An EasyCA model with few steady variables and clone stamp strategy for simulation of urban growth in metropolitan areas

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  • Ai, Bin
  • Xie, Dixiang
  • Ma, Shifa
  • Jiang, Haiyan

Abstract

It is critical to predict urban growth for understanding of urbanization process. Traditional urban CA mainly focused on discovery of conversion rules from a series of biophysical and social-economic factors. Many varying factors are generally treated as constant, and the simulation result is none of great reference value for decision-making. In order to eliminate these limitations, an Easy Cellular Automata (EasyCA) was proposed with integration of seed searching and clone stamp growth strategy, it aims to provide feasible scenario analysis for decision-making. Only four steady variables namely distance to current urban patches, difficulty coefficient of land conversion, density of urban land and topographic conditions were applied to estimate urban suitability. Policy intervention was further considered for adjustment of urban suitability, and patch growth templates were collected as clone stamps for updating the status of cells during iteration. This EasyCA was then tested with example area of Guangzhou City located in the Pearl River Delta of China. Accuracy indices of averaged fuzzy kappa (FuzzyK) and figure of merit (FOM) were calculated with the values of 0.8419 and 0.2874, respectively, and both of them were higher than those from cell growth-based CA, indicating that EasyCA is efficient in utility and reliability. Comparison analysis verified that few steady variables can obtain reliable result of urban suitability, and it is greatly necessary to integrate policy intervention into estimation of urban growth probability. And the simulation pattern in 2035 is maximally close to actual status compared with the planning layout, it shows that EasyCA is potentially applicable and effective for planning practices in metropolitan areas.

Suggested Citation

  • Ai, Bin & Xie, Dixiang & Ma, Shifa & Jiang, Haiyan, 2022. "An EasyCA model with few steady variables and clone stamp strategy for simulation of urban growth in metropolitan areas," Ecological Modelling, Elsevier, vol. 468(C).
    • Handle: RePEc:eee:ecomod:v:468:y:2022:i:c:s0304380022000709
    DOI: 10.1016/j.ecolmodel.2022.109950
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    References listed on IDEAS

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    1. Mustafa, Ahmed & Cools, Mario & Saadi, Ismaïl & Teller, Jacques, 2017. "Coupling agent-based, cellular automata and logistic regression into a hybrid urban expansion model (HUEM)," Land Use Policy, Elsevier, vol. 69(C), pages 529-540.
    2. González-Méndez, Mauricio & Olaya, Camilo & Fasolino, Isidoro & Grimaldi, Michele & Obregón, Nelson, 2021. "Agent-Based Modeling for Urban Development Planning based on Human Needs. Conceptual Basis and Model Formulation," Land Use Policy, Elsevier, vol. 101(C).
    3. Guzman, Luis A. & Escobar, Francisco & Peña, Javier & Cardona, Rafael, 2020. "A cellular automata-based land-use model as an integrated spatial decision support system for urban planning in developing cities: The case of the Bogotá region," Land Use Policy, Elsevier, vol. 92(C).
    4. Robert Pontius & Wideke Boersma & Jean-Christophe Castella & Keith Clarke & Ton Nijs & Charles Dietzel & Zengqiang Duan & Eric Fotsing & Noah Goldstein & Kasper Kok & Eric Koomen & Christopher Lippitt, 2008. "Comparing the input, output, and validation maps for several models of land change," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 42(1), pages 11-37, March.
    5. Meng, Liting & Sun, Yan & Zhao, Shuqing, 2020. "Comparing the spatial and temporal dynamics of urban expansion in Guangzhou and Shenzhen from 1975 to 2015: A case study of pioneer cities in China’s rapid urbanization," Land Use Policy, Elsevier, vol. 97(C).
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    1. Boxuan Zhao & Shujie Li & Zhaoshun Liu, 2022. "Multi-Scenario Simulation and Prediction of Regional Habitat Quality Based on a System Dynamic and Patch-Generating Land-Use Simulation Coupling Model—A Case Study of Jilin Province," Sustainability, MDPI, vol. 14(9), pages 1-24, April.

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