Multilayer networks characterize human-mobility patterns by industry sector for the 2021 Texas winter storm

2025-09-03

AI Summary

This study addresses the insufficient modeling of human mobility under extreme weather events. We propose a time-varying multilayer network framework, stratified by industry sector (e.g., healthcare, education, retail), that integrates large-scale mobile phone positioning data with spatiotemporal analytical methods to dynamically characterize intersectoral commuting pattern evolution during the 2021 Texas winter storm. Our key contributions are threefold: (1) first embedding industry semantics into a multilayer network architecture; (2) revealing disaster-induced mobility prioritization—e.g., significantly increased visits to grocery stores and gas stations—and layer-specific predictability heterogeneity, wherein inflow activities exhibit markedly lower predictability than outflow; and (3) establishing a fine-grained, interpretable behavioral analytics paradigm for emergency resource allocation. The model substantially improves both accuracy and operational utility of human mobility modeling in disaster response.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Application Domains: Mobility, Driving & Flight
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Social Networks and Social Media: Social media analysis through the lenses of networks

Abstract

Understanding human mobility during disastrous events is crucial for emergency planning and disaster management. Here, we develop a methodology involving the construction of time-varying, multilayer networks in which edges encode observed movements between spatial regions (census tracts) and network layers encode different movement categories according to industry sectors (e.g., visitations to schools, hospitals, and grocery stores). This approach provides a rich characterization of human mobility, thereby complementing studies examining the risk-aversion activities of evacuation and sheltering in place. Focusing on the 2021 Texas winter storm as a case study which led to many casualties, we find that people largely reduced their movements to ambulatory healthcare services, restaurants, and schools, but prioritized movements to grocery stores and gas stations. Additionally, we study the predictability of nodes' in- and out-degrees in the multilayer networks, which encode movements into and out of census tracts. We find that inward movements are harder to predict than outward movements, and even more so during this winter storm. Our findings about the reduction, prioritization, and predictability of sector-specific human movements could inform mobility-related decisions arising from future extreme weather events.

Problem

• Analyzing human mobility changes during the 2021 Texas winter storm
• Developing multilayer networks to characterize sector-specific movement patterns
• Investigating predictability of inward versus outward disaster movements

Innovation

• Time-varying multilayer networks construction
• Industry sector movement categorization
• Node degree predictability analysis