Seasonal catchment areas using an attribute based fuzzy lattice data structure - de Klerk, Michelle; Fabris-Rotelli, Inger (South African Statistical Journal, 2025)
Seasonality impacts various industries and sectors, influencing agricultural cycles, economic planning, and healthcare resource allocation. We propose a novel approach using an attribute based fuzzy lattice data structure to create overlapping catchment areas using the fundamentals of label propagation and graph clustering. This approach considers both the link structure and attribute similarities between nodes in a network, where the nodes are points of interest in a road network. Nodes may be close or far apart based on connectivity and shared attributes, such as common interests or in a geographical application considering topography features. In this study, we incorporate static and seasonal attributes for geographical nodes, allowing us to explore seasonal catchment areas and provide a more realistic view of accessibility throughout the year. This integrated approach offers a comprehensive framework for assessing spatial accessibility and understanding seasonal variations in regions to enhance planning for essential services.