Wildfire Evacuation Analysis Using Facebook Data: Evidence from Palisades and Eaton Fires
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866918270904303616 |
|---|---|
| author | Jiang, Shangkun Lovreglio, Ruggiero Cova, Thomas J. Park, Sangung Xu, Susu Zhao, Xilei |
| author_facet | Jiang, Shangkun Lovreglio, Ruggiero Cova, Thomas J. Park, Sangung Xu, Susu Zhao, Xilei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_01052 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Wildfire Evacuation Analysis Using Facebook Data: Evidence from Palisades and Eaton Fires Jiang, Shangkun Lovreglio, Ruggiero Cova, Thomas J. Park, Sangung Xu, Susu Zhao, Xilei Applications 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. |
| title | Wildfire Evacuation Analysis Using Facebook Data: Evidence from Palisades and Eaton Fires |
| topic | Applications |
| url | https://arxiv.org/abs/2601.01052 |