Introduction Human mobility has been a central issue in the discussion from the beginning of COVID-19. While the body of literature on the relationship of COVID transmission and mobility is large, studies mostly captured a relatively short timeframe. Moreover, spatial non-stationarity has garnered less attention in these explorative models. Therefore, the major concern of this study is to see the relationship of mobility and COVID on a broader temporal scale and after mitigating this methodological gap. Objective In response to this concern, this study first explores the spatiotemporal pattern of mobility indicators. Secondly, it attempts to understand how mobility is related to COVID infection rate and how this relationship has been changed over time and space after controlling several sociodemographic characteristics, spatial heterogeneity, and policy-related changes during different phases of Coronavirus. Data and method This study uses GPS-based mobility data for a wider time frame of six months (March 20-August’20) divided into four tiers and carries analysis for all the US counties (N = 3142). Space-time cube is used to generate the spatiotemporal pattern. For the second objective, Ordinary Least Square (OLS), Spatial Error Model (SEM), and Geographically Weighted Regression (GWR) were used. Result The spatial-temporal pattern suggests that the trip rate, out-of-county trip rate, and miles/person traveled were mostly plummeted till the first wave reached its peak, and subsequently, all of these mobility matrices started to rise. From spatial models, infection rates were found negatively correlated with miles traveled and out-of-county trips. Highly COVID infected areas mostly had more people working from home, low percentages of aged people and educated people, and high percentages of poor people. Conclusion This study, with necessary policy implications, provides a comprehensive understanding of the shifting pattern of mobility and COVID. Spatial models outperform OLS with better fits and non-clustered residuals.

This study explores how bike-share usage varied over the six months of the coronavirus pandemic (March'20-August'20) in five US cities. First, it finds that in most of the months, the changes (both increase or decrease) of bike-share usage from the preceding months are significantly higher in central tracts than peripheral tracts. This finding has more statistical significance for cities that are popular for biking (e.g. DC, Boston, Portland). Second, biking is found to be positively associated with cities' non-work trip rate and people's COVID exposure and it was diminished with more people maintaining social distance and staying at home.

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Introduction In the United States, health care has long been viewed as a ‘right,’ and residents of the state of Ohio are no exception. The Ohio Department of Health ensures that this right exists for all residents of Ohio. Socio-spatial characteristics, however, can have an impact on access to health care, particularly among vulnerable groups. This article seeks to measure the spatial accessibility to healthcare services by public transport in the six largest cities of Ohio based on population and to compare the accessibility of healthcare to vulnerable demographic groups. To the authors’ knowledge, this is the first study to analyze the accessibility and equity of hospitals by public transit across different cities in Ohio, allowing the identification of common patterns, difficulties, and knowledge gaps. Methodology Using a two-step floating catchment area technique, the spatial accessibility to general medical and surgical hospitals through public transportation was estimated, considering both service-to-population ratios and travel time to these health services. The average accessibility of all census tracts and the average accessibility of the 20% of most susceptible census tracts were determined for each city. Using Spearman’s rank correlation coefficient between accessibility and vulnerability, an indicator was then devised to evaluate vertical equity. Findings Within cities (except Cleveland), people of vulnerable census tracts have less access to hospitals via public transportation. These cities (Columbus, Cincinnati, Toledo, Akron, and Dayton) fail in terms of vertical equity and average accessibility. According to this, vulnerable census tracts in these cities have the lowest accessibility levels. Conclusion This study emphasizes the issues connected with the suburbanization of poverty in Ohio’s large cities and the need to provide adequate public transportation to reach hospitals on the periphery. In addition, this study shed light on the need for additional empirical research to inform the implementation of guidelines for healthcare accessibility in Ohio. Researchers, planners, and policymakers who want to make healthcare more accessible for everyone should take note of the findings in this study.