Integrating open data for high-resolution residential population disaggregation and flood exposure assessment in Brazil

Fine-scale exposure information is essential for natural hazard risk assessment, particularly in urban environments where vulnerability and hazard intensity can vary substantially within short distances. Building-level exposure data support a range of applications, including identification of priority areas for emergency response, estimation of shelter demand, and the development of more targeted early-warning and preparedness strategies. Although Brazil’s national census datasets are robust for demographic analysis, their spatial resolution, typically 200-meter grids or coarser, limits their use in detailed exposure assessments. In addition, high-resolution building datasets remain limited or unaffordable in many developing regions, especially outside major metropolitan centers, underscoring the need for reproducible methods based on openly available geospatial information. This study presents a methodology to disaggregate residential population from census grids to individual building footprints by integrating several complementary open datasets: (1) OpenBuildingsMap and GlobalBuildingAtlas footprints to obtain building geometry and attributes; (2) OpenStreetMap (OSM) for road network geometry and attributes; (3) the National Registry of Addresses for Statistical Purposes from the Brazilian Institute of Geography and Statistics (IBGE), used as georeferenced Points of Interest (POIs) classified by establishment type; and (4) population counts at 200-meter resolution from the 2020 IBGE Statistical Grid. Together, these datasets yield a scalable, transparent, and replicable exposure model tailored to Brazilian urban contexts. The proposed method adapted a weighted scoring framework in which residential building-level population allocation is driven by both physical building characteristics (floor area and height) and a POI-based residential attractiveness index. POI relevance weights were computed using Term Frequency–Inverse Document Frequency metrics and Pearson correlation between POI categories and population totals within each census grid. We applied a Gaussian Network Kernel Density Estimation along OSM road segments to propagate POI influence and derive an attractiveness score for each segment. Buildings were then linked to the nearest road segment, and their attractiveness scores were multiplied by their physical attributes to obtain a composite allocation weight. Two population distribution strategies were evaluated: the proposed POI-integrated method and a baseline model relying solely on building physical characteristics. The distribution was assessed through a flood-exposure analysis for a potential dam-breach scenario downstream of the Ibirité Dam, located in Minas Gerais, Brazil. We focused on the population potentially affected within the Self-Rescue Zone (SRZ), an area requiring immediate evacuation in the event of a failure. Across 169 directly affected 200-meter resolution census grids, a total of 8,578 residents were identified. Within the SRZ, the POI-integrated method estimated 3,124 residents, compared with 3,212 residents under the baseline approach. Results indicate that incorporating POI-based attractiveness produces more realistic spatial population patterns, particularly in mixed-use neighborhoods and areas with heterogeneous building typologies, enabling more accurate classification of flood hazard exposure. Future work includes sensitivity analysis, field validation, comparison with alternative disaggregation approaches, incorporation of demographic attributes, expansion to other occupancy types, and evaluation of methodologies to improve building-footprint geometry and attribute accuracy.