Evacuation patterns and socioeconomic stratification in the context of wildfires in Chile

TL;DR

This study analyzes evacuation patterns during Chilean wildfires using mobile phone data, revealing socioeconomic disparities in evacuation duration and movement behaviors, which are crucial for improving disaster response strategies.

Contribution

It introduces a novel causal inference approach combining regression discontinuity and difference-in-differences to study socioeconomic impacts on evacuation behaviors during wildfires.

Findings

Lower socioeconomic groups stayed away longer during evacuations.

Evacuation movements became more random initially, then aligned with socioeconomic status.

Socioeconomic disparities influence evacuation duration and movement patterns.

Abstract

Climate change is altering the frequency and intensity of wildfires, leading to increased evacuation events that disrupt human mobility and socioeconomic structures. These disruptions affect access to resources, employment, and housing, amplifying existing vulnerabilities within communities. Understanding the interplay between climate change, wildfires, evacuation patterns, and socioeconomic factors is crucial for developing effective mitigation and adaptation strategies. To contribute to this challenge, we use high-definition mobile phone records to analyse evacuation patterns during the wildfires in Valpara\'iso, Chile, that took place between February 2-3, 2024. This data allows us to track the movements of individuals in the disaster area, providing insight into how people respond to large-scale evacuations in the context of severe wildfires. We apply a causal inference approach that combines regression discontinuity and difference-in-differences methodologies to observe evacuation behaviours during wildfires, with a focus on socioeconomic stratification. This approach allows us to isolate the impact of the wildfires on different socioeconomic groups by comparing the evacuation patterns of affected populations before and after the event, while accounting for underlying trends and discontinuities at the threshold of the disaster. We find that many people spent nights away from home, with those in the lowest socioeconomic segment stayed away the longest. In general, people reduced their travel distance during the evacuation, and the lowest socioeconomic group moved the least. Initially, movements became more random, as people sought refuge in a rush, but eventually gravitated towards areas with similar socioeconomic status. Our results show that socioeconomic differences play a role in evacuation dynamics, providing useful insights for response planning.