A decision process for optimizing multi-hazard shelter location using global data

Abstract

Mitigating the effects of natural hazards through infrastructure planning requires integration of diverse types of information from a range of fields, including engineering, geography, social science, and geology. Challenges in data availability and previously siloed data have hindered the ability to obtain the information necessary to support decision making for disaster risk management. This is particularly challenging for areas susceptible to multiple types of natural hazards, especially in low-income communities that lack the resources for data collection. The data revolution is altering this landscape, due to the increased availability of remotely sensed data and global data repositories. This work seeks to leverage these advancements to develop a framework using open global datasets for identifying optimal locations for disaster relief shelters. The goal of this study is to empower low-income regions and make resilience more equitable by providing a multi-hazard shelter planning framework that is accessible to all decision-makers. The tool described integrates spatial multi-criteria decision analysis methods with a network analysis procedure to inform decisions regarding disaster shelter planning and siting.