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<p>Rapid urban growth in developing countries has accelerated land use and land cover (LULC) transformations, ecological stress, and spatial fragmentation. The landscape of South Twenty-Four Parganas is characterized by heterogeneous&#xa0;urban growth patterns and unevenly distributed&#xa0;infrastructural amenities and services, while also encompassing ecologically sensitive&#xa0;areas. Despite increasing LULC modelling efforts in India, previous research&#xa0;are retrospective and inadequately&#xa0;integrate predictive simulation with spatial dispersion diagnostics. This study addresses this&#xa0;gap by analyzing spatiotemporal urban growth dynamics and forecasts future expansion using an integrated Cellular Automata (CA)-Shannon Entropy framework. Landsat data from 1994, 2004, 2014, and 2024 were classified using supervised Maximum Likelihood Classification to create LULC maps. The Shannon Entropy Index and multi-buffer analysis were used to quantify urban dispersion and spatial&#xa0;layout, respectively. CA-Markov modelling was used to simulate urban expansion for 2034, 2044, and 2054 based on key spatial drivers. The findings reveal that built-up areas are gradually shifting from compact urban cores to peri-urban&#xa0;zones, as evidenced by growing entropy values and increased landscape fragmentation. Future forecasts show continuous growth along transportation corridors, into agricultural land, and into ecologically vulnerable low-lying areas. The findings indicate urban expansion in South Twenty Four&#xa0;Parganas has become increasingly dispersed, highlighting the limitations of conventional urban assessments. This research presents a comprehensive methodological framework for capturing both the process and pattern of urban expansion by combining entropy-based spatial diagnostics and CA-based simulation. The study provides novel district-scale evidence and policy-relevant insights for sustainable land-use planning in rapidly urbanizing coastal regions of the Global South.</p>

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Spatiotemporal analysis and prediction of urban growth dynamics in south twenty four parganas district of india: integrating cellular automata and shannon entropy index

  • 期刊论文

  • 发表日期：

    2026年4月8日

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  • Sudarshana Sinha,
  • Ankhi Banerjee,
  • Archana Patnaik

摘要

Rapid urban growth in developing countries has accelerated land use and land cover (LULC) transformations, ecological stress, and spatial fragmentation. The landscape of South Twenty-Four Parganas is characterized by heterogeneous urban growth patterns and unevenly distributed infrastructural amenities and services, while also encompassing ecologically sensitive areas. Despite increasing LULC modelling efforts in India, previous research are retrospective and inadequately integrate predictive simulation with spatial dispersion diagnostics. This study addresses this gap by analyzing spatiotemporal urban growth dynamics and forecasts future expansion using an integrated Cellular Automata (CA)-Shannon Entropy framework. Landsat data from 1994, 2004, 2014, and 2024 were classified using supervised Maximum Likelihood Classification to create LULC maps. The Shannon Entropy Index and multi-buffer analysis were used to quantify urban dispersion and spatial layout, respectively. CA-Markov modelling was used to simulate urban expansion for 2034, 2044, and 2054 based on key spatial drivers. The findings reveal that built-up areas are gradually shifting from compact urban cores to peri-urban zones, as evidenced by growing entropy values and increased landscape fragmentation. Future forecasts show continuous growth along transportation corridors, into agricultural land, and into ecologically vulnerable low-lying areas. The findings indicate urban expansion in South Twenty Four Parganas has become increasingly dispersed, highlighting the limitations of conventional urban assessments. This research presents a comprehensive methodological framework for capturing both the process and pattern of urban expansion by combining entropy-based spatial diagnostics and CA-based simulation. The study provides novel district-scale evidence and policy-relevant insights for sustainable land-use planning in rapidly urbanizing coastal regions of the Global South.