A data-driven method for identifying the locations of hurricane evacuations from mobile phone location data

Area of Science:

• Disaster Management
• Mobility Data Analysis
• Human Geography

Background:

• Effective evacuation management is crucial for community safety during disasters.
• Traditional methods for studying evacuation mobility patterns, like surveys, have limitations in scale and scope.
• Mobile phone location data offers a powerful tool for analyzing large-scale population movements during emergencies.

Purpose of the Study:

• To develop and validate a data-driven method for identifying hurricane evacuation areas using mobile phone location data.
• To overcome limitations of previous approaches that relied on researcher judgment and could misidentify non-evacuation behaviors.
• To provide a more accurate understanding of evacuee mobility patterns for improved disaster planning.

Main Methods:

• Inferred home locations of mobile phone users from location data.
• Calculated departure times and analyzed deviations from historical trends to identify potential evacuations.
• Applied the method to mobile phone data from Hurricanes Matthew and Irma for case studies.
• Validated inferred home locations and evacuation trends against existing research.

Main Results:

• Successfully developed a data-driven approach to identify areas experiencing evacuations.
• Demonstrated the method's ability to detect changes in departure behavior before and after hurricanes.
• Validated the accuracy and representativeness of inferred home locations and evacuation patterns.

Conclusions:

• The proposed data-driven method offers a more reliable way to identify hurricane evacuations compared to traditional approaches.
• Accurate identification of evacuation areas and timing enhances the effectiveness of disaster response and resource allocation.
• This methodology provides valuable insights into population mobility during extreme weather events, aiding future preparedness.