A spatiotemporal data collection of viral cases for COVID-19 rapid response

Lingbo Liu, Xiaokang Fu, Tobias Kötter, Kevin Sturm, Carsten Haubold, Weihe Wendy Guan, Shuming Bao, and Fahui Wang. 12/28/2023. “Geospatial Analytics Extension for KNIME .” SoftwareX, 25, Pp. 101627. Publisher's Version Abstract

The Geospatial Analytics Extension for KNIME (GAEK) is an innovative tool designed to integrate visual programming with geospatial analytics, streamlining GIS education and research in social sciences. GAEK simplifies access for users with an intuitive, visual interface for complex spatial analysis tasks and contributes to the organization of the GIS Knowledge Tree through its geospatial analytics nodes. This paper discusses GAEK's architecture, functionalities, and its transformative impact on GIS applications. While GAEK significantly enhances user experience and research reproducibility, future updates aim to expand its functionality and optimize its bundled environment.

Lingbo Liu and Fahui Wang. 10/15/2023. Computational Methods and GIS Applications in Social Science-Lab Manual. 1st ed. Boca Raton: CRC Press. Publisher's Version Abstract

This lab manual is a companion to the third edition of the textbook Computational Methods and GIS Applications in Social Science. It uses the open-source platform KNIME to illustrate a step-by-step implementation of each case study in the book. KNIME is a workflow-based platform supporting visual programming and multiple scripting language such as R, Python, and Java. The intuitive, structural workflow not only helps students better understand the methodology of each case study in the book, but also enables them to easily replicate, transplant and expand the workflow for further exploration with new data or models. This lab manual could also be used as a GIS automation reference for advanced users in spatial analysis.

FEATURES

The first hands-on, open-source KNIME lab manual written in tutorial style and focused on GIS applications in social science
Includes 22 case studies from the United States and China that parallel the methods developed in the textbook
Provides clear step-by-step explanations on how to use the open-source platform KNIME to understand basic and advanced analytical methods through real-life case studies
Enables readers to easily replicate and expand their work with new data and models
A valuable guide for students and practitioners worldwide engaged in efforts to develop GIS automation in spatial analysis

This lab manual is intended for upper-level undergraduate and graduate students taking courses in quantitative geography, spatial analysis, GIS applications in socioeconomic studies, GIS applications in business, and location theory, as well as researchers in the similar fields of geography, city and regional planning, sociology, and public administration.

Fahui Wang and Lingbo Liu. 8/16/2023. Computational Methods and GIS Applications in Social Science. 3rd ed. Boca Raton: CRC Press. Publisher's Version Abstract

This textbook integrates GIS, spatial analysis, and computational methods for solving real-world problems in various policy-relevant social science applications. Thoroughly updated, the third edition showcases the best practices of computational spatial social science and includes numerous case studies with step-by-step instructions in ArcGIS Pro and open-source platform KNIME. Readers sharpen their GIS skills by applying GIS techniques in detecting crime hotspots, measuring accessibility of primary care physicians, forecasting the impact of hospital closures on local community, or siting the best locations for business.

FEATURES

Fully updated using the latest version of ArcGIS Pro and open-source platform KNIME
Features two brand-new chapters on agent-based modeling and big data analytics
Provides newly automated tools for regionalization, functional region delineation, accessibility measures, planning for maximum equality in accessibility, and agent-based crime simulation
Includes many compelling examples and real-world case studies related to social science, urban planning, and public policy
Provides a website for downloading data and programs for implementing all case studies included in the book and the KNIME lab manual

Intended for students taking upper-level undergraduate and graduate-level courses in quantitative geography, spatial analysis, and GIS applications, as well as researchers and professionals in fields such as geography, city and regional planning, crime analysis, public health, and public administration.

Xinming Xia, Yi Zhang, Wenting Jiang, and Connor Yuhao Wu. 7/24/2023. “Staying Home, Tweeting Hope: Mixed Methods Study of Twitter Sentiment Geographical Index During US Stay-At-Home Orders.” Journal of Medical Internet Research, 25. Publisher's Version Abstract

Background: Stay-at-home orders were one of the controversial interventions to curb the spread of COVID-19 in the United States. The stay-at-home orders, implemented in 51 states and territories between March 7 and June 30, 2020, impacted the lives of individuals and communities and accelerated the heavy usage of web-based social networking sites. Twitter sentiment analysis can provide valuable insight into public health emergency response measures and allow for better formulation and timing of future public health measures to be released in response to future public health emergencies.

Objective: This study evaluated how stay-at-home orders affect Twitter sentiment in the United States. Furthermore, this study aimed to understand the feedback on stay-at-home orders from groups with different circumstances and backgrounds. In addition, we particularly focused on vulnerable groups, including older people groups with underlying medical conditions, small and medium enterprises, and low-income groups.

Methods: We constructed a multiperiod difference-in-differences regression model based on the Twitter sentiment geographical index quantified from 7.4 billion geo-tagged tweets data to analyze the dynamics of sentiment feedback on stay-at-home orders across the United States. In addition, we used moderated effects analysis to assess differential feedback from vulnerable groups.

Results: We combed through the implementation of stay-at-home orders, Twitter sentiment geographical index, and the number of confirmed cases and deaths in 51 US states and territories. We identified trend changes in public sentiment before and after the stay-at-home orders. Regression results showed that stay-at-home orders generated a positive response, contributing to a recovery in Twitter sentiment. However, vulnerable groups faced greater shocks and hardships during the COVID-19 pandemic. In addition, economic and demographic characteristics had a significant moderating effect.

Conclusions: This study showed a clear positive shift in public opinion about COVID-19, with this positive impact occurring primarily after stay-at-home orders. However, this positive sentiment is time-limited, with 14 days later allowing people to be more influenced by the status quo and trends, so feedback on the stay-at-home orders is no longer positively significant. In particular, negative sentiment is more likely to be generated in states with a large proportion of vulnerable groups, and the policy plays a limited role. The pandemic hit older people, those with underlying diseases, and small and medium enterprises directly but hurt states with cross-cutting economic situations and more complex demographics over time. Based on large-scale Twitter data, this sociological perspective allows us to monitor the evolution of public opinion more directly, assess the impact of social events on public opinion, and understand the heterogeneity in the face of pandemic shocks.

Fahui Wang, Yutian Zeng, Lingbo Liu, and Tracy Onega. 4/5/2023. “Disparities in spatial accessibility of primary care in Louisiana: From physical to virtual accessibility.” Frontiers in Public Health, 11, Pp. 2296-2565. Publisher's Version Abstract

Telehealth has been widely employed and has transformed how healthcare is delivered in the United States as a result of COVID-19 pandemic. While telehealth is utilized and encouraged to reduce the cost and travel burden for access to healthcare, there are debates on whether telehealth can promote equity in healthcare services by narrowing the gap among diverse groups. Using the Two-Step Floating Catchment Area (2SFCA) and Two-Step Virtual Catchment Area (2SVCA) methods, this study compares the disparities of physical and virtual access to primary care physicians (PCPs) in Louisiana. Both physical and virtual access to PCPs exhibit similar spatial patterns with higher scores concentrated in urban areas, followed by low-density and rural areas. However, the two accessibility measures diverge where broadband availability and affordability come to play an important role. Residents in rural areas experience additive disadvantage of even more limited telehealth accessibility than physical accessibility due to lack of broadband service provision. Areas with greater Black population proportions tend to have better physical accessibility, but such an advantage is eradicated for telehealth accessibility because of lower broadband subscription rates in these neighborhoods. Both physical and virtual accessibility scores decline in neighborhoods with higher Area Deprivation Index (ADI) values, and the disparity is further widened for in virtual accessibility compared to than physical accessibility. The study also examines how factors such as urbanicity, Black population proportion, and ADI interact in their effects on disparities of the two accessibility measures.

Lingbo Liu, Jennifer Alford-Teaster, Tracy Onega, and Fahui Wang. 5/12/2023. “Refining 2SVCA method for measuring telehealth accessibility of primary care physicians in Baton Rouge, Louisiana.” Cities, 138, Pp. 104364. Publisher's Version Abstract

Equity in health care delivery is a longstanding concern of public health policy. Telehealth is considered an important way to level the playing field by broadening health services access and improving quality of care and health outcomes. This study refines the recently developed “2-Step Virtual Catchment Area (2SVCA) method” to assess the telehealth accessibility of primary care in the Baton Rouge Metropolitan Statistical Area, Louisiana. The result is compared to that of spatial accessibility via physical visits to care providers based on the popular 2-Step Floating Catchment Area (2SFCA) method. The study shows that both spatial and telehealth accessibilities decline from urban to low-density and then rural areas. Moreover, disproportionally higher percentages of African Americans are in areas with higher spatial accessibility scores; but such an advantage is not realized in telehealth accessibility. In the study area, absence of broadband availability is mainly a rural problem and leads to a lower average telehealth accessibility than physical accessibility in rural areas. On the other side, lack of broadband affordability is a challenge across the rural-urban continuum and is disproportionally associated with high concentrations of disadvantaged population groups such as households under the poverty level and Blacks.

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