An interdisciplinary data-science approach to managing natural hazards risk

TL;DR

This paper introduces a comprehensive data-driven framework for natural hazard risk management, demonstrated through fire hazard analysis in California, integrating diverse datasets to improve risk assessment and policy decisions.

Contribution

It presents a novel interdisciplinary framework that combines multiple complex datasets for natural hazard risk assessment, surpassing previous methods limited to specific data or aspects.

Findings

Integrating fire behavior, street network, and census data alters risk assessment outcomes.

The framework improves understanding of spatial and socio-demographic factors in fire risk.

Results suggest potential for flexible, large-scale hazard management solutions.

Abstract

Natural hazard risk management is a demanding interdisciplinary task. It requires domain knowledge, integration of robust computational methods, and effective use of complex datasets. However, existing solutions tend to focus on specific aspects, data, or methods, limiting their impact and applicability. Here, we present a general data-driven framework to support risk assessment and policy making illustrating its usage in the context of fire hazard by integrating three unique datasets of fire behavior, street network, and census data for the whole state of California. We show that integrating spatial complexity by including a fire behavior layer and a socio-demographic layer changes the universal function observed in previous optimization frameworks that only work with the accessibility of facilities. These results open avenues for the future development of flexible interdisciplinary frameworks in natural hazards management using complex large-scale data.