Decoupling Urban Food Accessibility Resilience during Disasters through Time-Series Analysis of Human Mobility and Power Outages

2025-11-18

AI Summary

This study investigates the dynamic temporal coupling between power outages and urban food access during hurricane disasters. Method: Leveraging high-resolution outage data and mobile phone signaling trajectories during Hurricane Beryl, we quantify the lagged relationship at a daily scale—revealing that food access capacity peaks approximately two days after outage onset. Integrating time-series analysis, dynamic time warping (DTW) clustering, and multi-source geospatial data, we develop a cross-scale “Outage–Access Index” to identify 294 critical food facilities. Contribution/Results: Empirical analysis demonstrates that road connectivity—not household income—is the dominant explanatory factor for access loss. Based on these findings, we propose a two-tier “community–facility” resilience intervention framework, prioritizing microgrid deployment and targeted hardening of critical food infrastructure. This provides actionable, time-sensitive guidance for infrastructure resilience planning in hazard-prone urban systems.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Distributed Search
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Social Networks and Social Media: Social media analysis through the lenses of networks

Abstract

Disaster-induced power outages create cascading disruptions across urban lifelines, yet the timed coupling between grid failure and essential service access remains poorly quantified. Focusing on Hurricane Beryl in Houston (2024), this study integrates approximately 173000 15-minute outage records with over 1.25 million visits to 3187 food facilities to quantify how infrastructure performance and human access co-evolve. We construct daily indices for outage characteristics (intensity, duration) and food access metrics (redundancy, frequency, proximity), estimate cross-system lags through lagged correlations over zero to seven days, and identify recovery patterns using DTW k-means clustering. Overlaying these clusters yields compound power-access typologies and enables facility-level criticality screening. The analysis reveals a consistent two-day lag: food access reaches its nadir on July 8 at landfall while outage severity peaks around July 10, with negative correlations strongest at a two-day lag and losing significance by day four. We identify four compound typologies from high/low outage crossed with high/low access disruption levels. Road network sparsity, more than income, determines the depth and persistence of access loss. Through this analysis, we enumerate 294 critical food facilities in the study area requiring targeted continuity measures including backup power, microgrids, and feeder prioritization. The novelty lies in measuring interdependency at daily operational resolution while bridging scales from communities to individual facilities, converting dynamic coupling patterns into actionable interventions for phase-sensitive restoration and equity-aware preparedness. The framework is transferable to other lifelines and hazards, offering a generalizable template for diagnosing and mitigating cascading effects on community access during disaster recovery.

Problem

• Quantifying how power outages and food access co-evolve during disasters
• Identifying critical food facilities requiring targeted continuity measures
• Measuring infrastructure-human mobility interdependency at daily operational resolution

Innovation

• Integrates power outage records with food facility visits
• Uses lagged correlations and DTW clustering for analysis
• Converts coupling patterns into actionable intervention strategies