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Incorporation of Spatially Heterogeneous Area Partitioning into Vector-Based Cellular Automata for Simulating Urban Land-Use Changes

Author

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  • Jie Zhu

    (College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China
    Anhui Province Key Laboratory of Physical Geographic Environment, Chuzhou 239004, China)

  • Mengyao Zhu

    (College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China)

  • Jiaming Na

    (College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China
    Anhui Province Key Laboratory of Physical Geographic Environment, Chuzhou 239004, China)

  • Ziqi Lang

    (College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China)

  • Yi Lu

    (Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China
    City Futures Research Centre, School of Built Environment, University of New South Wales, Sydney, NSW 2052, Australia)

  • Jing Yang

    (Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China
    School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China)

Abstract

In cellular automata (CA) modeling, spatial heterogeneity can be delineated by geographical area partitioning. The dual constrained space clustering method is a prevalent approach for providing an objective and effective representation of differences within urban regions. However, previous studies faced issues by ignoring spatial heterogeneity, which could lead to an over- or under-estimation of the simulation results. Accordingly, this study attempts to incorporate spatially heterogeneous area partitioning into vector-based cellular automata (VCA), producing more accurate and reliable simulations of urban land-use change. First, an area partition strategy with DSC algorithm was employed to generate multiple relatively homogeneous sub-regions, which can effectively capture the spatial heterogeneity in the distribution of land-use change factors. Second, UrbanVCA, a brand-new VCA-based framework, was utilized for simulating land-use changes in distinct urban partitions. Finally, the constructed partitioned VCA model was applied to simulate rapid urban development in Jiangyin city from 2012 to 2017. The results indicated that the combination of DSC clustering and UrbanVCA model could obtain satisfying results as the average FoM values for the partitions and the entire study area exceeded 0.22. Furthermore, a comparative analysis of results from traditional area-partitioned CA models revealed that the proposed area partitioning approach had the potential to yield more accurate simulation outcomes as the FoM values were higher and SHDI and LSI metrics were closer to real-world observations, indicating its good performance in simulating fragmented urban landscapes.

Suggested Citation

  • Jie Zhu & Mengyao Zhu & Jiaming Na & Ziqi Lang & Yi Lu & Jing Yang, 2023. "Incorporation of Spatially Heterogeneous Area Partitioning into Vector-Based Cellular Automata for Simulating Urban Land-Use Changes," Land, MDPI, vol. 12(10), pages 1-22, October.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:10:p:1893-:d:1256033
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    References listed on IDEAS

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    1. 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.
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    1. Yebin Chen & Zhicheng Shi & Yaxing Li & Dezhi Han & Minmin Li & Zhigang Zhao, 2024. "Land Use Thematic Maps Recommendation Based on Pan-Map Visualization Dimension Theory," Land, MDPI, vol. 13(9), pages 1-16, August.

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