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Application of GIS and Spatial Data Analytics in Studies of COVID-19

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainability in Geographic Science".

Deadline for manuscript submissions: closed (15 November 2023) | Viewed by 5585

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Special Issue Editors

Dr. Bing She

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Guest Editor

Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
Interests: spatial data integration; geospatial analysis; GIScience; big data analytics

Dr. Tao Hu

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Guest Editor

Department of Geography, Oklahoma State University, Stillwater, OK 74074, USA
Interests: GIS; geospatial big data; health geography; health disparities
Special Issues, Collections and Topics in MDPI journals

Dr. Siqin Wang

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Guest Editor

School of Earth and Environmental Sciences, University of Queensland, Brisbane 4067, Australia
Interests: applying GIScience and big data analytics to study human–environment interaction in the domain of human mobility and migration; digital health geography; built environment; social vulnerability; natural hazard and climate change

Special Issue Information

Dear Colleagues,

The public health crisis caused by the COVID-19 pandemic has posed unprecedented challenges to our societies. Putting the pandemic in the spatiotemporal context and understanding its dynamics is crucial to effectively measuring and mitigating its impacts. Since the start of the pandemic, research communities across disciplines have increasingly relied on emerging geospatial data sources and technologies in a wide range of applications (disease mapping, modeling, case prediction, mobility analysis, decision support, measurements of social behavior and economic activity, etc.). These new applications have shed light on leveraging GIS tools and spatial analysis methods to better understand and combat this global crisis.

Along these lines, this Special Issue aims to capture emerging applications of GIS and spatial data analysis for multidisciplinary COVID-19 studies. The applications should demonstrate how recent advances in geospatial big data and spatial analysis provide new perspectives and methods towards understanding the impacts of COVID-19. We invite contributions that leverage data sources with a spatial dimension (public health records, socio-economic statistics, surveys, mobile phones, social media, transportation, remote sensing, etc.) and applied GIS and spatial analysis methods, including geovisual analytics, spatial statistics, GeoAI, space-time simulation, big data analysis, and spatial optimization. We also welcome studies that create new computational methods and tools in GIScience that further advance its applications in multidisciplinary pandemic research.

Dr. Bing She
Dr. Tao Hu
Dr. Siqin Wang
Guest Editors

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Keywords

COVID-19
inequality
susceptibility
Ibrahim index
drivers
Kolkata

Published Papers (3 papers)

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Research

16 pages, 5023 KiB

Open AccessArticle

Methods for Infectious Disease Risk Assessments in Megacities Using the Urban Resilience Theory

by Hao Wang, Changhao Cao, Xiaokang Ma and Yao Ma

Sustainability 2023, 15(23), 16271; https://doi.org/10.3390/su152316271 - 24 Nov 2023

Viewed by 769

Abstract

Since the 20th century began, the world has witnessed the emergence of contagious diseases such as Severe Acute Respiratory Syndrome (SARS), H1N1 influenza, and the recent COVID-19 pandemic. Conducting timely infectious disease risk assessments is of significant importance for preventing the spread of viruses, safeguarding public health, and achieving sustainable development. Most current studies on epidemic risk assessments focus on administrative divisions, making it challenging to reflect the risk disparities within these areas. Taking Shanghai as an example, this research introduces the concept of urban resilience frameworks and identifies the risk factors. By analyzing the interactions among different risk factors using geographic detectors, this study establishes the distribution relationship between the risk factors and newly reported cases using Geographically Weighted Regression. A risk assessment model is constructed to evaluate the infection risk within different regions of the administrative area. The results demonstrate that the central area of Shanghai exhibits the highest infection risk, gradually decreasing toward the periphery. The Spearman’s correlation coefficient (p) between the predicted and actual distribution of new cases reaches 0.869 (p < 0.001), and the coefficient of determination (R2) is 0.938 (p < 0.001), indicating a relatively accurate assessment of infection risk in different spatial areas. This research methodology can be effectively applied to infectious disease risk assessments during public health emergencies, thereby assisting in the formulation of epidemic prevention policies. Full article

(This article belongs to the Special Issue Application of GIS and Spatial Data Analytics in Studies of COVID-19)

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15 pages, 2596 KiB

Open AccessArticle

Spatial Distribution Analysis of Community Radio Stations as Means for Promoting Climate Change Adaptation Measures in Agriculture under COVID-19 Scenario, Southern Province, Zambia

by Albert Novas Somanje, Lauraine Mwila Mwansa and Kafula Chisanga

Sustainability 2022, 14(22), 15380; https://doi.org/10.3390/su142215380 - 18 Nov 2022

Viewed by 1358

Abstract

Community Radio Stations (CRS) play an important role in information dissemination at local and context-specific levels. This study aimed to analyze the point data distribution of the CRS and their role in promoting climate change adaptation measures in agriculture in times of the Coronavirus Disease (COVID-19). The study’s methodological approach included the geospatial mapping of point data of CRS in Arc GIS 10.3, surveys and interviews with thirty-nine (39) experts. In addition, the interview data were analyzed using SPSS 28.0 for frequency and descriptive analysis and excel for graphical outputs. The study found 19 operational CRS in 13 districts, and their radii completely cover the Southern Province of Zambia. Out of the time allocated to agricultural programs, an average of 47% is on climate change adaptation measures in local languages. However, the CRS have limited access to experts to provide information and program sponsorship. This study has established that CRS have the potential to disseminate climate change adaptation measures. Sixty-nine percent (69%) of the CRS noticed an increase in the demand for agricultural programs during the COVID-19 era, with the rapid growth of CRS. The study recommends stakeholders’ collaboration to provide appropriate information to enhance the agricultural climate programmes on CRS and address the challenges of limited access to experts and associated costs. Full article

(This article belongs to the Special Issue Application of GIS and Spatial Data Analytics in Studies of COVID-19)

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14 pages, 15364 KiB

Open AccessArticle

Spatiotemporal Accessibility of COVID-19 Healthcare Facilities in Jakarta, Indonesia

by Jumadi Jumadi, Vidya N. Fikriyah, Hamim Z. Hadibasyir, Muhammad I. T. Sunariya, Kuswaji D. Priyono, Noor A. Setiyadi, Steve J. Carver, Paul D. Norman, Nick S. Malleson, Arif Rohman and Aynaz Lotfata

Sustainability 2022, 14(21), 14478; https://doi.org/10.3390/su142114478 - 4 Nov 2022

Cited by 10 | Viewed by 2181

Abstract

During the first year of the COVID-19 pandemic in Jakarta, Indonesia, the government designated some hospitals as specific COVID-19 healthcare centers to meet demand and ensure accessibility. However, the policy demand evaluation was based on a purely spatial approach. Studies on accessibility to healthcare are widely available, but those that consider temporal as well as spatial dynamics are lacking. This study aims to analyze the spatiotemporal dynamics of healthcare accessibility against COVID-19 cases within the first year of the COVID-19 pandemic, and the overall pattern of spatiotemporal accessibility. A two-step floating catchment area (2SFCA) was used to analyze the accessibility of COVID-19 healthcare against the monthly data of the COVID-19 infected population, as the demand. Such a spatiotemporal approach to 2SFCA has never been used in previous studies. Furthermore, rather than the traditional buffer commonly used to define catchments, the 2SFCA in this study was improved with automated delineation based on the road network using ArcGIS Service Areas Analysis tools. The accessibility tends to follow the distance decay principle, which is relatively high in the city’s center and low in the outskirts. This contrasts with the city’s population distribution, which is higher on the outskirts and lower in the center. This research is a step toward optimizing the spatial distribution of hospital locations to correspond with the severity of the pandemic condition. One method to stop the transmission of disease during a pandemic that requires localizing the infected patient is to designate specific healthcare facilities to manage the sick individuals. ‘What-if’ scenarios may be used to experiment with the locations of these healthcare facilities, which are then assessed using the methodology described in this work to obtain the distribution that is most optimal. Full article

(This article belongs to the Special Issue Application of GIS and Spatial Data Analytics in Studies of COVID-19)

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