Attribute based spatial segmentation for optimising POI placement

Abstract

Effective spatial planning and resource optimisation require precise demarcation of potential spatial accessible areas and optimal placement of points of interest (POIs). Our approach introduces a novel attribute based spatial segmentation methodology that utilises an iterative clustering approach to create unique macro-regions, each associated with key structural and attribute specific properties. By integrating a probabilistic attribute based structure with k-means clustering, we adaptively segment spatial regions to balance area based attributes and topological characteristics. The full geographical network is segmented into attribute based macro-regions for all spatially accessible and spatially disjoint regions. Attribute based spatial segmentation offers insights into why certain areas may be spatially disjoint and if it is identified as potential spatially accessible areas to determine which POIs can be placed to maximise accessibility. This approach transforms city planning and resource allocation by aligning POI placement with regional needs and characteristics.