An Open-Data Framework for Screening Flood-Footprint Population Proxies and Potential Hospital Accessibility Disruption

Urban disaster screening requires transparent methods that can integrate heterogeneous open datasets without implying unsupported causal or probabilistic relationships. This study presents an open-data framework for screening flood-footprint population proxies and potential disruption to hospital accessibility in Bucharest, Romania. The analysis covers 104 archived hexagonal spatial units and combines a 100-year flood-depth scenario from the Joint Research Centre, WorldPop 2020 population estimates, hospital-routing outputs derived from OpenStreetMap, and a publicly available seismic screening surface from the European Facilities for Earthquake Hazard and Risk. Flood and seismic information are retained as distinct screening dimensions because the available data do not support modelling their causal interaction, temporal sequence, joint probability, earthquake-related infrastructure damage, or hospital capacity. For spatial units that remain connected to a hospital, a transparent two-domain service-priority index is calculated using flood-footprint population proxy and the potential change in hospital accessibility under the flood scenario. Units for which no hospital route is available are reported separately as binary service-disconnection alerts rather than being assigned an arbitrary numerical penalty. The robustness and interpretability of the framework are examined through network monotonicity, score boundedness, Pareto dominance, penalty-free rank invariance, and rank-acceptability analysis. The results are communicated using a Pareto frontier, rank trajectories across the full weight simplex, indicator-contribution decomposition, and an exact hypergeometric assessment of class overlap. The proposed framework provides a transparent first-order planning tool for identifying locations that may require more detailed investigation. It should be interpreted as a screening approach rather than as a probabilistic risk, cascading-hazard, infrastructure-damage, or service-loss model.