Geospatial Dasymetric Modeling and Cluster Analysis with Stability Confidence Measures for Identifying Parcel-Level Naturally Occurring Retirement Communities

The identification of senior residential concentrations requires geospatial methods that combine fine-scale population modeling with robust uncertainty assessment. This study introduces NORC-SIMCLUST, a framework that integrates dasymetric disaggregation of senior households with density-based clustering and stability confidence measures derived from simulation runs and parameter sweeps. The method creates synthetic microdata by allocating census block senior household counts to residential parcels using housing-unit information, then estimates cluster stability through repeated simulations. By addressing data sparsity and spatial analysis pitfalls inherent in aggregated areal approaches, our work improves reliability and enables the detection of both horizontal and vertical NORCs—an underexplored geospatial challenge. A case study in Colorado Springs, USA, demonstrates enhanced detection reliability and confidence assessment compared to conventional heuristics. This work advances geospatial analytics for aging-in-place research and planning by providing a scalable, reproducible pipeline for demographic simulation, spatial clustering, and uncertainty analysis.