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

Timur Naushirvanov, Erick Elejalde, Kyriaki Kalimeri, Elisa Omodei, Márton Karsai, Leo Ferres

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 Valparaíso, 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.

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

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 Valparaíso, 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.
Paper Structure (5 sections, 4 equations, 15 figures, 11 tables)

This paper contains 5 sections, 4 equations, 15 figures, 11 tables.

Table of Contents

  1. Comparison of Population
  2. Distribution of Towers
  3. Assortativity
  4. Comparison of Telefónica and Meta: Z-Scores
  5. Regression Tables

Figures (15)

  • Figure 1: Fraction of individuals whose night location was different from their home location.a) Fractions of individuals by three group types (Not Affected, Potentially Affected, and Likely Evacuated), with a 95% confidence interval. 95% CI obtained through bootstrapping the fraction of moved people by iteratively resampling the original dataset (1,000 times) with replacement, each time generating a sample representing around 10% of the population. b) Fractions of likely evacuated people as given in data and inferred with the regression discontinuity in time model. c) Fractions of likely evacuated and not affected people as given in data and inferred with the difference in differences model.
  • Figure 1: Correlations between census zone populations as represented in Telefónica data and official statistics.
  • Figure 2: Median travel distance between the towers (only for individuals who moved to another tower).a) Median travel distances by three group types (Not Affected, Potentially Affected, and Likely Evacuated), with a 95% confidence interval. b) Median travel distances of likely evacuated people as given in data and inferred with the regression discontinuity in time model. c) Median travel distances of likely evacuated and not affected people as given in data and inferred with the difference in differences model.
  • Figure 2: Correlations between census zone populations per each socioeconomic status as represented in Telefónica data and official statistics. The Medium SE class is better represented in Telefónica data, while the Low SE class is the most underrepresented (when compared with the official statistics).
  • Figure 3: Fraction of individuals whose night location was different from their home location by socioeconomic status.a)-c) Fractions of individuals by three group types (Not Affected, Potentially Affected, and Likely Evacuated) and by socioeconomic status (Low, Medium, High), with a 95% confidence interval. 95% CI obtained through bootstrapping the fraction of moved people by iteratively resampling the original dataset (1,000 times) with replacement, each time generating a sample representing around 10% of the population. d) Fractions of likely evacuated people (by socioeconomic status) as given in data and inferred with the regression discontinuity in time model. e) Fractions of likely evacuated and not affected people (by socioeconomic status) as given in data and inferred with the difference-in-differences model.
  • ...and 10 more figures