This research establishes a methodological framework for quantifying community resilience based on fluctuations in a population's activity during a natural disaster. Visits to points-of-interests (POIs) over time serve as a proxy for activities to capture the combined effects of perturbations in lifestyles, the built environment and the status of business. This study used digital trace data related to unique visits to POIs in the Houston metropolitan area during Hurricane Harvey in 2017. Resilience metrics in the form of systemic impact, duration of impact, and general resilience (GR) values were examined for the region along with their spatial distributions. The results show that certain categories, such as religious organizations and building material and supplies dealers had better resilience metrics—low systemic impact, short duration of impact, and high GR. Other categories such as medical facilities and entertainment had worse resilience metrics—high systemic impact, long duration of impact and low GR. Spatial analyses revealed that areas in the community with lower levels of resilience metrics also experienced extensive flooding. This insight demonstrates the validity of the approach proposed in this study for quantifying and analysing data for community resilience patterns using digital trace/location-intelligence data related to population activities. While this study focused on the Houston metropolitan area and only analysed one natural hazard, the same approach could be applied to other communities and disaster contexts. Such resilience metrics bring valuable insight into prioritizing resource allocation in the recovery process.
The Coronavirus Disease 2019 (COVID-19) has been reported to disproportionately impact racial/ethnic minorities in the USA, both in terms of infections and deaths. This racial disparity in the COVID-19 outcomes may result from the segregation of minorities in neighborhoods with health-compromising conditions. We, thus, anticipate that neighborhoods would be especially vulnerable to COVID-19 if they are of present-day economic and racial disadvantage and were redlined historically. To test this expectation, we examined the change of both confirmed COVID-19 cases and deaths from April to July, 2020, in zip code tabulation areas (ZCTAs) in the New York City using multilevel regression analysis. The results indicate that ZCTAs with a higher proportion of black and Hispanic populations are associated with a higher percentage of COVID-19 infection. Historically low-graded neighborhoods show a higher risk for COVID-19 infection, even for ZCTAs with present-day economic and racial privilege. These associations change over time as the pandemic unfolds. Racial/ethnic minorities are bearing the brunt of the COVID-19 pandemic’s health impact. The current evidence shows that the pre-existing social structure in the form of racial residential segregation could be partially responsible for the disparities observed, highlighting an urgent need to stress historical segregation and to build a less segregated and more equal society.
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