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<p>Accurate classification of urban sprawl is vital for sustainable urban planning, yet most regional-scale approaches overlook local spatial heterogeneity and lack robust validation. This study presents a comprehensive framework that integrates high-resolution sliding-window analysis, advanced spatial metrics, Morphological Spatial Pattern Analysis (MSPA) and building density for validation, and machine learning-based feature importance assessment. The framework is applied to both developing cities (Colombo and Kandy, Sri Lanka) and a developed city (Hong Kong) for the years 2005, 2015, and 2025. Twenty spatial metrics are computed within 510 m × 510 m windows, with the optimal window size determined through sensitivity analysis, and Pearson correlation used for dimensionality reduction. Urban sprawl typologies are extracted via K-means clustering, with the optimal cluster number determined by the Gap Statistic and clustering quality evaluated using Silhouette scores. Metric weighting is performed using CRITIC (Criteria Importance Through Intercriteria Correlation), which prioritizes metrics based on their discriminative power and independence. Five distinct sprawl types: infill, extension, linear, clustered, and leapfrog, are identified and validated against MSPA-derived morphological elements and building density. Random Forest and Cliff’s Δ analyses highlight transport infrastructure, especially road density and proximity to main roads, as the primary drivers of sprawl, alongside population density and topography. The framework demonstrates robust predictive performance and offers a scalable, locally adaptive tool for precise urban sprawl classification, supporting evidence-based planning and policy.</p>

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A novel CRITIC-driven framework for fine-scale urban sprawl typology classification: evidence from Colombo, Kandy, and Hong Kong

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月17日

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  • Farasath Hasan,
  • Xintao Liu

摘要

Accurate classification of urban sprawl is vital for sustainable urban planning, yet most regional-scale approaches overlook local spatial heterogeneity and lack robust validation. This study presents a comprehensive framework that integrates high-resolution sliding-window analysis, advanced spatial metrics, Morphological Spatial Pattern Analysis (MSPA) and building density for validation, and machine learning-based feature importance assessment. The framework is applied to both developing cities (Colombo and Kandy, Sri Lanka) and a developed city (Hong Kong) for the years 2005, 2015, and 2025. Twenty spatial metrics are computed within 510 m × 510 m windows, with the optimal window size determined through sensitivity analysis, and Pearson correlation used for dimensionality reduction. Urban sprawl typologies are extracted via K-means clustering, with the optimal cluster number determined by the Gap Statistic and clustering quality evaluated using Silhouette scores. Metric weighting is performed using CRITIC (Criteria Importance Through Intercriteria Correlation), which prioritizes metrics based on their discriminative power and independence. Five distinct sprawl types: infill, extension, linear, clustered, and leapfrog, are identified and validated against MSPA-derived morphological elements and building density. Random Forest and Cliff’s Δ analyses highlight transport infrastructure, especially road density and proximity to main roads, as the primary drivers of sprawl, alongside population density and topography. The framework demonstrates robust predictive performance and offers a scalable, locally adaptive tool for precise urban sprawl classification, supporting evidence-based planning and policy.