A spatiotemporal decay model of human mobility when facing large-scale crises

Area of Science:

• Mobility studies
• Extreme event analysis
• Socioeconomic impacts

Background:

• Large-scale extreme events (pandemics, wildfires, storms) significantly alter human mobility patterns.
• These mobility changes impact event consequences and governmental response effectiveness.
• Understanding these shifts is crucial for disaster management and public health.

Purpose of the Study:

• To analyze and model human mobility changes during six large-scale extreme events.
• To identify consistent patterns in mobility shifts across diverse crises.
• To investigate socioeconomic disparities in mobility responses to the COVID-19 pandemic.

Main Methods:

• Utilized anonymized mobility data from over 90 million individuals in the US.
• Analyzed mobility data across six distinct large-scale crises.
• Developed a novel model to capture heterogeneous human mobility changes.
• Applied five different metrics to assess mobility changes across various spatial resolutions.

Main Results:

• Human mobility changes consistently exhibit a hyperbolic decline, termed "spatiotemporal decay."
• This pattern holds across different metrics and spatial resolutions.
• During COVID-19, individuals from wealthier areas reduced mobility faster and longer.
• Residents from lower-income areas showed a faster, greater hyperbolic decay in mobility.

Conclusions:

• The "spatiotemporal decay" model effectively captures mobility shifts post-extreme events.
• Socioeconomic disparities in mobility changes may correlate with differing COVID-19 rates.
• The developed model offers a powerful tool for understanding and forecasting post-emergency mobility patterns to improve response strategies.