Measuring Spatial Accessibility in the Context of COVID-19 (1 of 2)
- Jayajit Chakraborty will showcase the findings or outcomes of their project thus far as a Geospatial Fellow for advancing COVID-19 research and education.
- Daoqin Tong will showcase the findings or outcomes of their project thus far as a Geospatial Fellow for advancing COVID-19 research and education.
Jayajit Chakraborty The University of Texas at El Paso
Vulnerability to COVID-19 for People with Disabilities: Spatial and Social Inequalities in the U.S.
Although people with disabilities (PwDs) are more likely to be adversely impacted by COVID-19 than non-disabled individuals and face additional challenges, research on the spatial distribution of COVID-19 burdens has paid limited attention to PwDs. My project addresses this gap by conducting a comprehensive national-scale study of the relationship between COVID-19 vulnerability and disability status, using several spatial analytic and statistical modeling techniques. The main research objectives are to: (1) examine whether PwDs and those with specific types of difficulties are significantly overrepresented in areas more vulnerable to the COVID-19 pandemic; and (2) determine whether socially disadvantaged PwDs (based on their race, ethnicity, age, sex, poverty, and employment status) are significantly overrepresented in areas more vulnerable to the COVID-19 pandemic. The project provides new insights on the association between COVID-19 vulnerability and disability characteristics, and builds a foundation for more detailed analyses of the disproportionate impacts of the pandemic on PwDs—a topic that has significant implications for health equity, social justice, and related public policies. The findings also emphasize the necessity to expand notions of vulnerability associated with COVID-19, and the urgent need for COVID-19 data collection systems to incorporate disability information. In addition, I am developing an interactive geospatial dashboard that will serve as an educational tool for understanding the spatial variability, diversity, and vulnerability of PwDs, as well as provide an online resource that can be used to identify, map, and plan support for socially vulnerable PwDs during public health emergencies and disaster events.
Jayajit Chakraborty is a Professor of Geography in the Department of Sociology & Anthropology at the University of Texas at El Paso, and the Founding Director of the Socio-Environmental & Geospatial Analysis (SEGA) Lab which conducts multidisciplinary and community-engaged research on topics at the intersection of social and environmental sciences. He has a PhD in Geography and MS in Urban and Regional Planning, both from the University of Iowa. Dr. Chakraborty’s research activities encompass a wide range of environmental health and environmental justice issues, and he is particularly interested in applying GIScience and spatial statistical techniques for analyzing environmental and social injustices. His funded and published research has examined various hazards, risks, and disasters in multiple US urban areas, the US-Mexico border, Australia, and India. Dr. Chakraborty has authored more than 100 publications and recently coedited The Routledge Handbook of Environmental Justice. His research has been supported by grants from the US National Science Foundation (NSF), US Environmental Protection Agency (EPA), US Department of Transportation, US Department of Treasury, Australian Research Council, and many other agencies. Dr. Chakraborty has served on advisory committees and review panels for the EPA, NSF, National Institutes of Health, and National Academies of Sciences, Engineering, and Medicine.
Daoqin Tong Arizona State University
A Transmutation of Food Access During the COVID-19 Pandemic
The COVID-19 pandemic has fueled a range of devastating consequences to the U.S. economy. Soaring unemployment rates have pushed millions of families into food insecurity. Meanwhile, the social distancing requirement has also changed the way that people access food with many people replacing in-store visits with online food shopping and pickup. This study combines a large food access spatial dataset and relevant socioeconomic and demographic data to examine how economic stress and the new rules for obtaining food impact food access during COVID-19. We map food access patterns before and during COVID-19 and examine the factors associated with the food access pattern change. We examine such changes in several large metropolitan areas including Phoenix and Los Angeles. The study provides important insights into efforts aimed at enhancing food security and policies to help prepare for future shocks. Research findings concerning food access challenges of vulnerable population groups including low-income and minority will help food assistance programs, non-profit organizations and government agencies in these regions (and beyond) to plan food service provision and formulate effective interventions.
Daoqin Tong is an Associate Professor in the School of Geographical Sciences and Urban Planning at Arizona State University. Her research interests include spatial data analysis, spatial optimization, and geographic information science to improve spatial decision-making. She developed models and solution algorithms to solve real-world problems spanning location analysis, food access, transportation, and public health. Recently, she has examined issues related to urban sustainability. She developed analytical approaches to examine the potential of sustainable urban agriculture through the integration of renewable water resources. She has also worked on the optimal placement and design of green infrastructures to mitigate the impacts of urban flooding. Another ongoing project explores how pollen can be used for tracking the movement of objects and people through space and time. Her work has been funded by multiple government agencies (e.g., NSF, DOD, and DOT) and local organizations.
Measuring Spatial Accessibility in the Context of COVID-19 (1 of 2)
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Date: Mon, June 14, 2021
Time: 4:00 - 5:00 pm U.S. Central Time
Status: Event Ended
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