Community-scale Big Data Reveals Disparate Impacts of the Texas Winter Storm of 2021 and its Managed Power Outage

TL;DR

This study uses community-scale big data to analyze the unequal impacts of Texas Winter Storm Uri, revealing disparities in power outages, burst pipes, and food access among vulnerable populations, and highlighting implications for infrastructure management.

Contribution

It demonstrates the use of community-scale big data to assess and understand disparities in impacts during extreme weather events, providing insights for more equitable infrastructure responses.

Findings

Low-income and minority groups experienced longer power outages.

Disparities in burst pipes and food access were more severe for vulnerable populations.

Community-scale data enables rapid impact assessment after extreme weather events.

Abstract

Aggregated community-scale data could be harnessed to allow insights into the disparate impacts of managed power outages, burst pipes, and food inaccessibility during extreme weather events. During Winter Storm Uri in February 2021, Texas power-generating plant operators resorted to rolling blackouts to prevent wider collapse of the whole power grid when power demand overwhelmed supply. In this study, we collected community-scale big data (e.g., digital trace and crowdsourced data) in the context of the winter storm, which brought historically low temperatures, snow, and ice to the entire state of Texas. By analyzing this data, we can infer the extent of disparities in storm-related impacts on vulnerable populations. In addition to assessing disparate impacts of the managed power outage, this study also examined the extent of burst pipes and disrupted access to food. Using statistical and trend classification analyses, the results highlight the spatial and temporal patterns of impacts for subpopulations in Harris County, Texas, within which the city of Houston is located. In particular, findings show significant disparity in the extent and duration of power outages experienced by low-income and minority groups. This finding suggests that the existence of inequality in the management and implementation of the power outage. Also, the extent of burst pipes and disrupted food access were more severe for low-income and minority groups. These findings suggest implications for understanding the disparate impacts of extreme weather events on vulnerable populations; in particular, the findings could provide insights allowing infrastructure operators to enhance social equality considerations during managed service disruptions in such events. The results and findings demonstrate the value of community-scale big data sources for rapid impact assessment in the aftermath of extreme weather events.