Measuring evacuation rates from mobility data during the McDougall Creek wildfire in British Columbia, Canada

Bibliographic Data

ID7731639
AuthorsHui Jeong Ha (0000-0002-5229-4528, Western University, corresponding author), Jed Long (0000-0003-3961-3085, Western University), Jed A Long
Year2025
Volume119
Pages102286
Publication date2025-07-01
Open AccessYes
TypeARTICLE
VenueComputers Environment and Urban Systems (JOURNAL)
Journal identifiersISSN: 0198-9715 • E-ISSN: 1873-7587
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.compenvurbsys.2025.102286
OpenAlexW4409181939
LanguageEN
Citations received2
References cited54

In recent years, the intensity and occurrence of wildfires have risen globally, driven by climate change triggering extreme dry weather conditions. This study focuses on the 2023 McDougall Creek wildfire in British Columbia, highlighting the vulnerability of urban communities to severe wildfires. Using aggregated and de-identified network mobility data from a Canadian telecommunications provider, we quantified neighborhood-level evacuation rates and examined inter-regional travel patterns during the wildfire event. We applied a spatial difference-in-difference (DID) model to understand how neighborhood characteristics influenced evacuation rates. Our findings suggest that formal evacuation orders were positively associated with evacuation rates. We also found that the distance to the wildfire perimeter was a strong and significant predictor of evacuation rates, while socio-demographic variables previously identified as strong predictors of evacuation rates were not significant in this particular context. The analysis of travel patterns before and during the wildfire event reveals distinct directional patterns and variations in inter-regional travel across spatial scales. This research contributes to the understanding of wildfire evacuation dynamics and the application of human mobility data into disaster management, enhancing our knowledge of the human response to natural disasters. • Measured wildfire evacuation rates using network mobility data at the neighborhood level. • Analyzed neighborhood characteristics influencing wildfire evacuation rates through a spatial difference-in-difference model. • Found proximity to the wildfire perimeter to be a strong predictor of evacuation rates. • Revealed distinct directional movement patterns in inter-regional travel during the wildfire event

Archaeology · Cartography · Geography · Physical geography · Evacuation and Crowd Dynamics · Fire effects on ecosystems · Urban Transport and Accessibility · Forestry

Tools

Open DOIOpen Access