ReviewTransactions in GIS : TG2021
A review of GIS methodologies to analyze the dynamics of COVID-19 in the second half of 2020.
Review in Transactions in GIS : TG, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it, 71 citations in OpenAlex.
- Spatial and spatiotemporal machine learning models for COVID-19 dynamics: a review of methodology and reporting practices.Epidemiologic reviews · 2025Pooled it
- A Review of Influencing Factors on Spatial Spread of COVID-19 Based on Geographical Perspective.International journal of environmental research and public health · 2021Pooled it
- An exploratory study of patient hospitalization patterns and behavioral risk factors using mobile phone location data.PLOS digital health · 2026Article
- An experienced racial-ethnic diversity dataset in the United States using human mobility data.Scientific data · 2024Article
- Spatiotemporal hierarchical Bayesian analysis to identify factors associated with COVID-19 in suburban areas in Colombia.Heliyon · 2024Article
- Review
- Global Challenges After a Global Challenge: Lessons Learned from the COVID-19 Pandemic.Advances in experimental medicine and biology · 2024Article
- Understanding spatiotemporal patterns of COVID-19 incidence in Portugal: A functional data analysis from August 2020 to March 2022.PloS one · 2024Article
- Housing situations and local COVID-19 infection dynamics using small-area data.Scientific reports · 2023Article
- Exploration of spatiotemporal heterogeneity and socio-demographic determinants on COVID-19 incidence rates in Sarawak, Malaysia.BMC public health · 2023Article
- The Role of Functional Urban Areas in the Spread of COVID-19 Omicron (Northern Spain).Journal of urban health : bulletin of the New York Academy of Medicine · 2023Article
- Key risk factors associated with fractal dimension based geographical clustering of COVID-19 data in the Flemish and Brussels region, Belgium.Frontiers in public health · 2023Article
- Article
- Tempo-Spatial Modelling of the Spread of COVID-19 in Urban Spaces.International journal of environmental research and public health · 2022Article
- Generating High-Granularity COVID-19 Territorial Early Alerts Using Emergency Medical Services and Machine Learning.International journal of environmental research and public health · 2022Article
- Spatial Syndromic Surveillance and COVID-19 in the U.S.: Local Cluster Mapping for Pandemic Preparedness.International journal of environmental research and public health · 2022Article
- Geospatial Perspectives on the Intersection of Chronic Disease and COVID-19.Preventing chronic disease · 2022Article
- COVID-19 Variant Surveillance and Social Determinants in Central Massachusetts: Development Study.JMIR formative research · 2022Article
- Are spatial patterns of Covid-19 changing? Spatiotemporal analysis over four waves in the region of Cantabria, Spain.Transactions in GIS : TG · 2022Article
- The Spatial Association of Demographic and Population Health Characteristics with COVID-19 Prevalence Across Districts in India.Geographical analysis · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 has infected over 163 million people and has resulted in over 3.9 million deaths. Regarding the tools and strategies to research the ongoing pandemic, spatial analysis has been increasingly utilized to study the impacts of COVID-19. This article provides a review of 221 scientific articles that used spatial science to study the pandemic published from June 2020 to December 2020. The main objectives are: to identify the tools and techniques used by the authors; to review the subjects addressed and their disciplines; and to classify the studies based on their applications. This contribution will facilitate comparisons with the body of work published during the first half of 2020, revealing the evolution of the COVID-19 phenomenon through the lens of spatial analysis. Our results show that there was an increase in the use of both spatial statistical tools (e.g., geographically weighted regression, Bayesian models, spatial regression) applied to socioeconomic variables and analysis at finer spatial and temporal scales. We found an increase in remote sensing approaches, which are now widely applied in studies around the world. Lockdowns and associated changes in human mobility have been extensively examined using spatiotemporal techniques. Another dominant topic studied has been the relationship between pollution and COVID-19 dynamics, which enhance the impact of human activities on the pandemic's evolution. This represents a shift from the first half of 2020, when the research focused on climatic and weather factors. Overall, we have seen a vast increase in spatial tools and techniques to study COVID-19 transmission and the associated risk factors.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.