Identifying Hospital Deserts in Texas Before and During the COVID-19 Outbreak

In this study, we proposed a GIS-based approach to analyzing hospital visitors from January to June 2019 and January to June 2020 with the goal of revealing significant changes in the visitor demographics. The target dates were chosen to observe the effect of the first wave of COVID-19 on the visitor count in hospitals. The results indicated that American Indian and Pacific Islander groups were the only ones that sometimes showed no shift in visitor levels between the studied years. For 19 of the 28 hospitals in Austin, TX, the average distance traveled to those hospitals from home increased in 2020 compared with 2019. A hospital desert index was devised to identify the areas in which the demand for hospitals is greater than the current hospital supply. The hospital desert index considers the travel time, location, bed supply, and population. The cities located along the outskirts of metropolitan regions and rural towns showed more hospital deserts than dense city centers.

Identifying Hospital Deserts in Texas Before and During the COVID-19 Outbreak.

In this study, we proposed a GIS-based approach to analyzing hospital visitors from January to June 2019 and January to June 2020 with the goal of revealing significant changes in the visitor demographics. The target dates were chosen to observe the effect of the first wave of COVID-19 on the visitor count in hospitals. The results indicated that American Indian and Pacific Islander groups were the only ones that sometimes showed no shift in visitor levels between the studied years. For 19 of the 28 hospitals in Austin, TX, the average distance traveled to those hospitals from home increased in 2020 compared with 2019. A hospital desert index was devised to identify the areas in which the demand for hospitals is greater than the current hospital supply. The hospital desert index considers the travel time, location, bed supply, and population. The cities located along the outskirts of metropolitan regions and rural towns showed more hospital deserts than dense city centers.

Disparities in the Impacts of the COVID-19 Pandemic on Public Transit Ridership in Austin, Texas, U.S.A.

The COVID-19 pandemic has disrupted day-to-day lives and infrastructure across the United States, including public transit systems, which saw precipitous declines in ridership beginning in March 2020. This study aimed to explore the disparities in ridership decline across census tracts in Austin, TX and whether demographic and spatial characteristics exist that are related to these declines. Transit ridership data from the Capital Metropolitan Transportation Authority were used in conjunction with American Community Survey data to understand the spatial distribution of ridership changes caused by the pandemic. Using a multivariate clustering analysis as well as geographically weighted regression models, the analysis indicated that areas of the city with older populations as well as higher percentages of Black and Hispanic populations were associated with less severe declines in ridership, whereas areas with higher unemployment saw steeper declines. The percentage of Hispanic residents appeared to affect ridership most clearly in the center of Austin. These findings support and expand on previous research that found that the impacts of the pandemic on transit ridership have emphasized the disparities in transit usage and dependence across the United States and within cities.

The impact of COVID-19 on home value in major Texas cities

PurposeThis study aims to analyze the impact of COVID-19 on housing price within four major metropolitan areas in Texas: Austin, Dallas, Houston and San Antonio. The analysis intends to understand economic and mobility drivers behind the housing market under the inclusion of fixed and random effects.Design/methodology/approachThis study used a linear mixed effects model to assess the socioeconomic and housing and transport-related factors contributing to median home prices in four major cities in Texas and to capture unobserved factors operating at spatial and temporal level during the COVID-19 pandemic.FindingsThe regression results indicated that an increase in new COVID-19 cases resulted in an increase in housing price. Additionally, housing price had a significant and negative relationship with the following variables: business cycle index, mortgage rate, percent of single-family homes, population density and foot traffic. Interestingly, unemployment claims did not have a significant impact on housing price, contrary to previous COVID-19 housing market related literature.Originality/valuePrevious literature analyzed the housing market within the first phase of COVID-19, whereas this study analyzed the effects of the COVID-19 throughout the entirety of 2020. The mixed model includes spatial and temporal analyses as well as provides insight into how quantitative-based mobility behavior impacted housing price, rather than relying on qualitative indicators such as shutdown order implementation.

Housing market price movements under tech industry expansion during COVID-19

PurposeThis study aims to analyze the impact of technology-based corporation relocation on housing price indices during COVID-19 within the metropolitan areas of Austin, Texas and Seattle/Bellevue, Washington.The corporations under observation were Tesla and Amazon, respectively. The analysis intends to understand economic drivers behind the housing market and the radius of its effect while including fixed and random effects.Design/methodology/approachThis study used a difference-in-difference (DID) method to evaluate changes in housing price index near and further away from Tesla's and Amazon's new corporate locations. The DID method allows for the capture of unique regional characteristics, as it requires a treatment and control group: housing price index and 5-mile and 10-mile search radii centered from the new corporate location.FindingsThe results indicated that corporate relocation announcements had a positive effect on housing price index post-pandemic. Specifically, the effect of Tesla's relocation in Austin on the housing price index was not concentrated near the relocation site, but beyond the 5- and 10-mile radii. For Seattle/Bellevue, the effect of Amazon's relocation announcement on housing price index was concentrated near the relocation site as well as beyond a 10-mile radius. Interestingly, these findings suggest housing markets incorporate speculation of prospective economic expansion linked with a corporate relocation.Originality/valuePrevious literature assessed COVID-19 housing market conditions and the economic effects of corporate relocation separately, whereas this study analyzed the housing price effects of corporate relocation during COVID-19. The DID method includes spatial and temporal analyses that allow for the impact of housing price to be observed across specified radii rather than a city-wide impact analysis.

Exploring temporal varying demographic and economic disparities in COVID-19 infections in four U.S. areas: based on OLS, GWR, and random forest models

Although studies have previously investigated the spatial factors of COVID-19, most of them were conducted at a low resolution and chose to limit their study areas to high-density urbanized regions. Hence, this study aims to investigate the economic-demographic disparities in COVID-19 infections and their spatial-temporal patterns in areas with different population densities in the United States. In particular, we examined the relationships between demographic and economic factors and COVID-19 density using ordinary least squares, geographically weighted regression analyses, and random forest based on zip code-level data of four regions in the United States. Our results indicated that the demographic and economic disparities are significant. Moreover, several areas with disadvantaged groups were found to be at high risk of COVID19 infection, and their infection risk changed at different pandemic periods. The findings of this study can contribute to the planning of public health services, such as the adoption of smarter and comprehensive policies for allocating economic recovery resources and vaccines during a public health crisis.

Exploring the factors affecting travel behaviors during the second phase of the COVID-19 pandemic in the United States

The COVID-19 outbreak significantly disrupted urban mobility across the world and affected people’s travel behaviors. This paper aims to explore the relationship between socio-demographic and health factors and changes in travel behavior during the second phase of this outbreak. We proposed two measures to assess these changes: (i) whether an individual reduced the number of trips to stores during the second phase of the pandemic and (ii) whether an individual reduced the number of trips by public transport during this period. Two binary logit models were estimated based on survey data from the United States Census Bureau. The results indicate that all variables, including age, gender, educational status, marital status, work loss, difficulty with expenses, household size, work type, income, health status, and anxiousness were significantly associated with changes in travel behavior. [ABSTRACT FROM AUTHOR] Copyright of Transportation Letters is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)