Details
| Original language | English |
|---|---|
| Article number | 23 |
| Journal | EPJ Data Science |
| Volume | 14 |
| Issue number | 1 |
| Publication status | Published - 18 Mar 2025 |
Abstract
Wildfires are becoming more frequent and intense, leading to increased evacuation events that disrupt mobility and socioeconomic structures, impacting access to resources, employment, and housing. Understanding the interplay between these factors is crucial for developing effective mitigation and adaptation strategies. We analyse evacuation patterns during the wildfires that occurred in Valparaíso, Chile, on February 2-3, 2024, using high-definition mobile phone records. Applying a causal inference approach combining regression discontinuity and difference-in-differences, we focus on socioeconomic stratification to isolate the wildfire impact on different groups. We find that many people spent nights away from home, with the lowest socioeconomic group staying away the longest. Overall, people reduced their mean and median night-to-night travel distances during the evacuation. Movements initially became irregular but later concentrated in areas of similar socioeconomic status. Finally, we demonstrate a comparability potential of the mobile phone records to the Facebook Disaster Maps, although the latter have a coarse time resolution and are generated only after the wildfire onset. Our results highlight the role of socioeconomic differences in evacuation dynamics, offering valuable insights for response planning.
Keywords
- Computational social science, Emergency response, Evacuation patterns, Human displacement, Mobile phone data, Natural disasters, Social media data, Socioeconomic inequalities, Wildfires
ASJC Scopus subject areas
- Mathematics(all)
- Modelling and Simulation
- Computer Science(all)
- Computer Science Applications
- Mathematics(all)
- Computational Mathematics
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In: EPJ Data Science, Vol. 14, No. 1, 23, 18.03.2025.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Evacuation patterns and socioeconomic stratification in the context of wildfires
AU - Naushirvanov, T.
AU - Kalimeri, K.
AU - Omodei, E.
AU - Karsai, M.
AU - Ferres, L.
AU - Elejalde Sierra, Erick
N1 - Publisher Copyright: © The Author(s) 2025.
PY - 2025/3/18
Y1 - 2025/3/18
N2 - Wildfires are becoming more frequent and intense, leading to increased evacuation events that disrupt mobility and socioeconomic structures, impacting access to resources, employment, and housing. Understanding the interplay between these factors is crucial for developing effective mitigation and adaptation strategies. We analyse evacuation patterns during the wildfires that occurred in Valparaíso, Chile, on February 2-3, 2024, using high-definition mobile phone records. Applying a causal inference approach combining regression discontinuity and difference-in-differences, we focus on socioeconomic stratification to isolate the wildfire impact on different groups. We find that many people spent nights away from home, with the lowest socioeconomic group staying away the longest. Overall, people reduced their mean and median night-to-night travel distances during the evacuation. Movements initially became irregular but later concentrated in areas of similar socioeconomic status. Finally, we demonstrate a comparability potential of the mobile phone records to the Facebook Disaster Maps, although the latter have a coarse time resolution and are generated only after the wildfire onset. Our results highlight the role of socioeconomic differences in evacuation dynamics, offering valuable insights for response planning.
AB - Wildfires are becoming more frequent and intense, leading to increased evacuation events that disrupt mobility and socioeconomic structures, impacting access to resources, employment, and housing. Understanding the interplay between these factors is crucial for developing effective mitigation and adaptation strategies. We analyse evacuation patterns during the wildfires that occurred in Valparaíso, Chile, on February 2-3, 2024, using high-definition mobile phone records. Applying a causal inference approach combining regression discontinuity and difference-in-differences, we focus on socioeconomic stratification to isolate the wildfire impact on different groups. We find that many people spent nights away from home, with the lowest socioeconomic group staying away the longest. Overall, people reduced their mean and median night-to-night travel distances during the evacuation. Movements initially became irregular but later concentrated in areas of similar socioeconomic status. Finally, we demonstrate a comparability potential of the mobile phone records to the Facebook Disaster Maps, although the latter have a coarse time resolution and are generated only after the wildfire onset. Our results highlight the role of socioeconomic differences in evacuation dynamics, offering valuable insights for response planning.
KW - Computational social science
KW - Emergency response
KW - Evacuation patterns
KW - Human displacement
KW - Mobile phone data
KW - Natural disasters
KW - Social media data
KW - Socioeconomic inequalities
KW - Wildfires
U2 - 10.1140/epjds/s13688-025-00540-2
DO - 10.1140/epjds/s13688-025-00540-2
M3 - Article
AN - SCOPUS:105000292994
VL - 14
JO - EPJ Data Science
JF - EPJ Data Science
SN - 2193-1127
IS - 1
M1 - 23
ER -