Non-locality and Spillover Effects of Residential Flood Damage on Community Recovery: Insights from High-resolution Flood Claim and Mobility Data

TL;DR

This study uses high-resolution flood damage and mobility data from Hurricane Harvey to analyze how residential flood damage impacts community recovery locally and regionally, revealing significant spillover effects and spatial heterogeneity.

Contribution

It combines detailed flood damage and mobility data with spatial models to quantify non-local effects of residential damage on community recovery, advancing understanding of spatial diffusion in disaster resilience.

Findings

Residential damage significantly slows community recovery.

Spillover effects decay with distance from damaged areas.

Urban structure influences spatial decay of damage effects.

Abstract

Examining the relationship between vulnerability of the built environment and community recovery is crucial for understanding disaster resilience. Yet, this relationship is rather neglected in the existing literature due to previous limitations in the availability of empirical datasets needed for such analysis. In this study, we combine fine-resolution flood damage claims data (composed of both insured and uninsured losses) and human mobility data (composed of millions of movement trajectories) during the 2017 Hurricane Harvey in Harris County, Texas, to specify the extent to which vulnerability of the built environment (i.e., flood property damage) affects community recovery (based on the speed of human mobility recovery) locally and regionally. We examine this relationship using a spatial lag, spatial reach, and spatial decay models to measure the extent of spillover effects of residential damage on community recovery. The findings show that: first, the severity of residential damage significantly affects the speed of community recovery. A greater extent of residential damage suppresses community recovery not only locally but also in the surrounding areas. Second, the spatial spillover effect of residential damage on community recovery speed decays with distance from the highly damaged areas. Third, spatial areas display heterogeneous spatial decay coefficients, which are associated with urban structure features such as the density of points-of-interest facilities and roads. These findings provide a novel data-driven characterization of the spatial diffusion of residential flood damage effects on community recovery and move us closer to a better understanding of complex spatial processes that shape community resilience to hazards. This study also provides valuable insights for emergency managers and public officials seeking to mitigate the non-local effects of residential damage.