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Forecasting Urban Land Use Dynamics Through Patch-Generating Land Use Simulation and Markov Chain Integration: A Multi-Scenario Predictive Framework

Author

Listed:
  • Ahmed Marey

    (Centre for Zero Energy Building Studies, Department of Building, Civil and Environmental Engineering, Concordia University, Montreal, QC H3G 1M8, Canada)

  • Liangzhu (Leon) Wang

    (Centre for Zero Energy Building Studies, Department of Building, Civil and Environmental Engineering, Concordia University, Montreal, QC H3G 1M8, Canada)

  • Sherif Goubran

    (Department of Architecture, School of Sciences and Engineering, The American University in Cairo, New Cairo 11835, Egypt)

  • Abhishek Gaur

    (National Research Council Canada, Construction Research Centre, Ottawa, ON K1A 0R6, Canada)

  • Henry Lu

    (National Research Council Canada, Construction Research Centre, Ottawa, ON K1A 0R6, Canada)

  • Sylvie Leroyer

    (Meteorological Research Division, Environmental Numerical Prediction, Environment and Climate Change Canada, Montreal, QC G1J 0C3, Canada)

  • Stephane Belair

    (Meteorological Research Division, Environmental Numerical Prediction, Environment and Climate Change Canada, Montreal, QC G1J 0C3, Canada)

Abstract

Rapid urbanization and changing land use dynamics require robust tools for projecting and analyzing future land use scenarios to support sustainable urban development. This study introduces an integrated modeling framework that combines the Patch-generating Land Use Simulation (PLUS) model with Markov Chain (MC) analysis to simulate land use and land cover (LULC) changes for Montreal Island, Canada. This framework leverages historical data, scenario-based adjustments, and spatial drivers, providing urban planners and policymakers with a tool to evaluate the potential impacts of land use policies. Three scenarios—sustainable, industrial, and baseline—are developed to illustrate distinct pathways for Montreal’s urban development, each reflecting different policy priorities and economic emphases. The integrated MC-PLUS model achieved a high accuracy level, with an overall accuracy of 0.970 and a Kappa coefficient of 0.963 when validated against actual land use data from 2020. The findings indicate that sustainable policies foster more contiguous green spaces, enhancing ecological connectivity, while industrial-focused policies promote the clustering of commercial and industrial zones, often at the expense of green spaces. This study underscores the model’s potential as a valuable decision-support tool in urban planning, allowing for the scenario-driven exploration of LULC dynamics with high spatial precision. Future applications and enhancements could expand its relevance across diverse urban contexts globally.

Suggested Citation

  • Ahmed Marey & Liangzhu (Leon) Wang & Sherif Goubran & Abhishek Gaur & Henry Lu & Sylvie Leroyer & Stephane Belair, 2024. "Forecasting Urban Land Use Dynamics Through Patch-Generating Land Use Simulation and Markov Chain Integration: A Multi-Scenario Predictive Framework," Sustainability, MDPI, vol. 16(23), pages 1-19, November.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:23:p:10255-:d:1527616
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    References listed on IDEAS

    as
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