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

A novel framework for classifying urban sprawl types, combining high-resolution spatial analysis and machine learning, reveals that transport infrastructure—particularly road density and proximity to main roads—is the dominant driver of urban expansion patterns. The study identifies five distinct sprawl typologies (infill, extension, linear, clustered, and leapfrog) across Colombo, Kandy (Sri Lanka), and Hong Kong using a CRITIC-driven approach that prioritizes metrics based on discriminative power and independence. Validated against morphological elements and building density, this method offers precise, locally adaptive classification with robust predictive performance, supporting evidence-based urban planning for sustainable development.

Hasan, F., & Liu, X. (2025). A novel CRITIC-driven framework for fine-scale urban sprawl typology classification: evidence from Colombo, Kandy, and Hong Kong. Computational Urban Science, 5(1). https://doi.org/10.1007/s43762-025-00227-4