Measuring inequality in community resilience to natural disasters using large-scale mobility data

Area of Science:

• Disaster science
• Urban planning
• Sociology

Background:

• Conceptual definitions of disaster resilience lack objective, scalable measures.
• Spatiotemporal dynamics of community response and recovery are critical but understudied.
• Measuring community resilience requires empirical data on localized impacts.

Purpose of the Study:

• To empirically define and measure community resilience capacity using mobility data.
• To analyze spatiotemporal patterns of disaster response and recovery.
• To identify socioeconomic and racial disparities in community resilience.

Main Methods:

• Analysis of anonymized mobile device mobility data (800,000+ devices, ~35% of Houston population) during Hurricane Harvey (2017).
• Quantification of community resilience capacity based on changes in mobility behavior before, during, and after the disaster.
• Assessment of impact magnitude and time-to-recovery using mobility metrics.

Main Results:

• Community resilience capacity was empirically defined as a function of impact magnitude and time-to-recovery.
• Significant socioeconomic and racial disparities were identified in community resilience capacity.
• Evacuation patterns also showed clear socioeconomic and racial disparities.

Conclusions:

• Mobility data provides a novel, data-driven approach to measuring community resilience.
• Disaster recovery is not equitable, with vulnerable neighborhoods experiencing longer recovery times.
• Findings support data-driven public sector decisions for equitable resource allocation to at-risk communities.