Wildfire Evacuation Analysis Using Facebook Data: Evidence from Palisades and Eaton Fires

2026-01-03

AI Summary

Amid increasing wildfire frequency, accurately characterizing evacuation behavior in wildland-urban interface (WUI) communities remains challenging, limiting emergency response effectiveness. This study leverages high-resolution Facebook mobility data to develop a fine-grained analytical framework capturing evacuation compliance rates, departure timing, travel distances, and destination types. It introduces the Damage-Evacuation Discrepancy Index (DEDI) to identify vulnerable areas exhibiting high fire damage yet low evacuation rates. Applied to the 2025 Palisades and Eaton fires, the analysis reveals that residents closer to ignition sources evacuated earlier, while nighttime evacuations or delayed official orders significantly reduced compliance. DEDI hotspots correlate with elevated social vulnerability, and most evacuees relocated to private residences, whereas long-distance evacuees predominantly chose hotels and public facilities. Integrating spatiotemporal behavioral modeling with social vulnerability assessment, this work offers a novel paradigm for precision emergency planning.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Application Domains: Mobility, Driving & Flight

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

The growing frequency and intensity of wildfires pose serious threats to communities in wildland-urban interface regions. Understanding evacuation behavior is critical for effective emergency planning. This study analyzes evacuation during the 2025 Palisades and Eaton Fires using high-resolution Facebook data. We propose a comprehensive framework to derive wildfire evacuation-related metrics, including compliance rate, departure timing, delay, origin-destination flows, travel distance, and destination types. A new metric, Damage-Evacuation Disparity Index (DEDI), identifies areas with severe structural damage but low evacuation compliance. Results reveal spatiotemporal heterogeneity: residents closer to the fire evacuated earlier, whereas late or nighttime orders led to lower compliance and longer delays. Contrasting patterns between East and West Altadena further illustrate this disparity. DEDI-identified communities exhibited higher social vulnerability and fire risk. Most evacuations concluded in residential areas, while longer trips concentrated in hotels and public facilities. These findings showcase the Facebook data's potential for data-driven wildfire evacuation planning.

Problem

• wildfire evacuation
• evacuation behavior
• social vulnerability
• wildland-urban interface
• evacuation compliance

Innovation

• Facebook mobility data
• wildfire evacuation
• Damage-Evacuation Disparity Index (DEDI)
• spatiotemporal heterogeneity
• social vulnerability