Modeling urban land markets in data-scarce cities: a spatial big data mining approach to building density patterns in Kigali

Purpose The purpose of this paper is to address the critical lack of traditional data in rapidly urbanizing, data-scarce cities by proposing a novel spatial big data mining framework that leverages building density as a reliable proxy for urban land market patterns. Design/methodology/approach This study used building density to infer urban land market patterns in Kigali, Rwanda. The core analysis confirmed significant spatial clustering (Moran’s I = 0.9780) and multi-metric validation of five clustering algorithms selected the k-means model (k = 5) for robust urban segmentation. Findings The clustering delineated five distinct housing density zones, confirming a clear spatial gradient consistent with the classical bid-rent theory and monocentric city model. The high-density core (density: 0.34) comprises 9.93% of the land area, while extensive low-density zones dominate the periphery, empirically validating the applicability of traditional urban economic models in this data-scarce African context. Practical implications This study provides urban planners and policymakers with an evidence-based map of land market pressure. This granular segmentation enables targeted land-use planning, optimized infrastructure investment and the development of equitable policies for managing urban growth and densification in the future. Originality/value This study used building footprints density to infer land market patterns in Kigali, offering replicable methodology for data-driven spatial analysis in the Global South.