Wildfire evacuation decision modeling using GPS data

Abstract

The threat of wildfires is increasing at an alarming rate due to climate change and the expansion of the wildland–urban interface. It is critical to improve understanding of people’s evacuation decisions during wildfire emergencies. Therefore, this study proposes a novel methodology to model evacuation rates using large-scale GPS data generated by mobile devices. We first overlay socio-demographic and built environment attributes—aggregated at the census-block-group-level—to the inferred home locations of the mobile device users. We then develop a linear regression model to examine how the socio-demographic and built environment variables affect evacuation rates across census block groups. We apply the GPS data (44.2 million signal records from over 5000 devices) collected during the 2019 Kincade Fire in Sonoma County, California to evaluate the proposed methodology. The results of the model are generally consistent with findings of a prior survey of the same fire event. We also include factors in our model that are rarely measured through previous methods and find several built environment factors such as distance to the fire, land parcel size, and residing in a high fire risk area to be correlated with evacuation rates. Another notable finding is that people living in urban block groups, block groups with a higher median age, and block groups with a higher average level of educational attainment are more likely to evacuate. This research shows that the use of GPS data is a valuable complement to existing methods in wildfire evacuation research, and provides new insights to improve evacuation planning.