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Community resilience in the face of natural hazards involves both a community's sensitivity to disaster and its potential to bounce back. A failure to integrate equity into resilience considerations results in unequal recovery and disproportionate impacts on vulnerable populations, which has long been a concern in the United States. This research investigated aspects of equity related to community resilience in the aftermath of Winter Storm Uri in Texas which led to extended power outages for more than 4 million households. County-level outage/recovery data was analyzed to explore potential significant links between various county attributes and their share of the outages during the recovery/restoration phase. Next, satellite imagery was used to examine data at a much higher geographical resolution focusing on census tracts in the city of Houston. The goal was to use computer vision to extract the extent of outages within census tracts and investigate their linkages to census tracts attributes. Results from various statistical procedures revealed statistically significant negative associations between counties' percentage of non-Hispanic whites and median household income with the ratio of outages. Additionally, at census tract level, variables including percentages of linguistically isolated population and public transport users exhibited positive associations with the group of census tracts that were affected by the outage as detected by computer vision analysis. Informed by these results, engineering solutions such as the applicability of grid modernization technologies, together with distributed and renewable energy resources, when controlled for the region's topographical characteristics, are proposed to enhance equitable power grid resiliency in the face of natural hazards.
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American Community Survey (ACS) Data Uncertainty and the Analysis of Segregation Dynamics
Abstract American Community Survey (ACS) data have become the workhorse for the empirical analysis of segregation in the U.S.A. during the past decade. The increased frequency the ACS offers over the 10-year Census, which is the main reason for its popularity, comes with an increased level of uncertainty in the published estimates due to the reduced sampling ratio of ACS (1:40 households) relative to the Census (1:6 households). This paper introduces a new approach to integrate ACS data uncertainty into the analysis of segregation. Our method relies on variance replicate estimates for the 5-year ACS and advances over existing approaches by explicitly taking into account the covariance between ACS estimates when developing sampling distributions for segregation indices. We illustrate our approach with a study of comparative segregation dynamics for 29 metropolitan statistical areas in California, using the 2010–2014 and 2015–2019. Our methods yield different results than the simulation technique described by Napierala and Denton (Demography 54(1):285–309, 2017). Taking the ACS estimate covariance into account yields larger error margins than those generated with the simulated approach when the number of census tracts is large and minority percentage is low, and the converse is true when the number of census tracts is small and minority percentage is high.
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- PAR ID:
- 10473536
- Publisher / Repository:
- Springer
- Date Published:
- Journal Name:
- Population Research and Policy Review
- Volume:
- 42
- Issue:
- 1
- ISSN:
- 0167-5923
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s11113-023-09754-6
