Spatial catchment areas using fuzzy lattice data structures

Abstract

This thesis presents a comprehensive framework for defining and optimising service catchment areas through innovative approaches, addressing accessibility and resource allocation challenges, particularly in low-resource settings. The first methodology introduces fuzzy lattice catchment areas, using a semi-supervised, probabilistic approach to create overlapping service zones. By enabling communities to access multiple points of interest (POIs) within their range and incorporating drive-time thresholds, this approach ensures a more equitable distribution of demand and supply, minimising spatial imbalances. Building on this, the second methodology extends the fuzzy lattice framework by integrating attribute based connections, combining structural and contextual attributes to more accurately capture spatial dynamics. This dual consideration allows for a refined propagation of demand across networks, addressing limitations in traditional connectivity only models. The final methodology applies attribute based spatial segmentation, creating tailored macro-regions that align with local environmental and socio-economic factors. By leveraging probabilistic clustering, it optimises service placements and identifies both spatially accessible and disjoint regions. Collectively, these approaches advance the field of spatial planning by offering flexible, data driven solutions that adapt to regional characteristics, enhancing service accessibility and equitable resource distribution. The applications demonstrate significant potential across healthcare, urban planning, and beyond, providing a robust foundation for addressing evolving accessibility challenges.