Saving lives in wildfire disasters with GPS data

Authored by Lovreglio R, Zhao X, Xu Y, Nilsson D, Kuligowski E, Cova T, Yan X
Like

Share this post

Choose a social network to share with, or copy the shortened URL to share elsewhere

This is a representation of how your post may appear on social media. The actual post will vary between social networks

Every year the threat of wildfire dramatically increases. This increase in wildfire intensity, frequency and resulting social harm is largely connected to climate change as well as suburban and exurban growth in wildland areas.

Our research team is working to improve wildfire life-safety and enhance the resilience of exposed communities by investigating how the public responds during wildfire disasters, and in particular how they make decisions regarding if/when they will evacuate, where they will go to reach safety, and which route they will take. One of the biggest obstacles to understanding wildfire evacuation behavior is a lack of data on household response during fire events.

Traditionally, data on household evacuation in wildfires has been collected using methods like surveys, interviews, and focus groups. Although these methods have generated many insights into evacuation  behavior, they have primarily focused on understanding the evacuation decision, itself: i.e., whether people will evacuate or not. A limited number of wildfire studies focus on the travel-related evacuation decisions, such as household departure time and routing, likely because of the difficulty in gathering accurate data using survey methods. As these methods rely on evacuees to recall their actions months or even years after the events, specific and accurate details involving timing or actual routes can be difficult to obtain due to the burden and inherent complexity in asking respondents to describe the spatial and temporal trip-dimensions during the fire event.

In light of these challenges, our research team investigated the use of mobile GPS data, which can provide valuable insights in understanding household evacuation processes during wildfires. This data can be purchased from GPS data providers, who in turn deliver a dataset of users’ movements which are anonymized and coded to ensure confidentiality. In this study of the 2019 Kincade Fire evacuation in  California, U.S., GPS data was purchased from Gravy Analytics, an enterprise location technology company that has partnered with the University of Florida to solve real-world problems using big data. The Gravy Analytics GPS data enables researchers to observe the movement of mobile devices such as smartphones over a period of time with a resolution that allows us to investigate user travel-behavior during a wildfire.

Using GPS data captured before and during the Kincade fire, our team has been able to understand household origins, routes, and destinations (places of safety). This involved analyzing millions of location signals (pings) from mobile devices such as smartphones and smartwatches. As analyzing GPS  data for wildfire evacuations is a new approach, our group developed algorithms to infer the evacuation and travel decisions made by thousands of evacuees. As such, our research aims to standardize the approach used to analyze GPS data for future comparison of observed evacuation behavior across different wildfire disasters. Promising results by the research team can be found in Zhao et al., 2022 for the 2019 Kincade Fire in Sonoma County (see Figure 1).

We believe that providing open-access to GPS datasets can aid in the creation of many new and life-saving applications in disaster management. As part of this project, the team has released their GPS dataset of the 2019 Kincade Fire evacuation while maintaining evacuee anonymity by releasing only those datapoints located along highways within Sonoma County, California (see Figure 2). In doing so, evacuees ‘home locations’ and ‘destinations’ were not released. Having free access to this data is a valuable resource for the evacuation research community, in that it can promote further investigation into evacuation behavior, and the creation, development and validation of evacuation simulation tools. These insights can eventually lead to improved community-based evacuation plans prior to and decisions made during future fire events, especially for those located in wildfire-prone areas around the world.

Please sign in or register for FREE

If you are a registered user on Research Communities by Springer Nature, please sign in

Subscribe to the Topic

Electrical and Electronic Engineering
Technology and Engineering > Electrical and Electronic Engineering

Related Collections

With collections, you can get published faster and increase your visibility.

Medical imaging data for digital diagnostics

This Collection presents a series of articles describing annotated datasets of medical images and video. All medical specialities are considered and data can be derived from study participants, tissue samples, electronic health records (EHRs) or other sources.

Publishing Model: Open Access

Deadline: Dec 20, 2023

Remote sensing data for changes in land use

This Collection comprises a series of articles presenting data on changes to land use in urban areas, farmland, forests, and natural environments, as determined using remote sensing techniques.

Publishing Model: Open Access

Deadline: Jan 31, 2024