Unfolding Events in Space and Time: Geospatial Insights into COVID-19 Diffusion in Washington State during the Initial Stage of the Outbreak

Green spaces, especially nearby forest, may reduce the SARS-CoV-2 infection rate: A nationwide study in the United States

The coronavirus pandemic is an ongoing global crisis that has profoundly harmed public health. Although studies found exposure to green spaces can provide multiple health benefits, the relationship between exposure to green spaces and the SARS-CoV-2 infection rate is unclear. This is a critical knowledge gap for research and practice. In this study, we examined the relationship between total green space, seven types of green space, and a year of SARS-CoV-2 infection data across 3,108 counties in the contiguous United States, after controlling for spatial autocorrelation and multiple types of covariates. First, we examined the association between total green space and SARS-CoV-2 infection rate. Next, we examined the association between different types of green space and SARS-CoV-2 infection rate. Then, we examined forest-infection rate association across five time periods and five urbanicity levels. Lastly, we examined the association between infection rate and population-weighted exposure to forest at varying buffer distances (100 m to 4 km). We found that total green space was negative associated with the SARS-CoV-2 infection rate. Furthermore, two forest variables (forest outside park and forest inside park) had the strongest negative association with the infection rate, while open space variables had mixed associations with the infection rate. Forest outside park was more effective than forest inside park. The optimal buffer distances associated with lowest infection rate are within 1,200 m for forest outside park and within 600 m for forest inside park. Altogether, the findings suggest that green spaces, especially nearby forest, may significantly mitigate risk of SARS-CoV-2 infection.

Tracing out the effect of transportation infrastructure on NO2 concentration levels with Kernel Density Estimation by investigating successive COVID-19-induced lockdowns

This study aims to investigate the effect of transportation infrastructure on the decrease of NO₂ air pollution during three COVID-19-induced lockdowns in a vast region of France. For this purpose, using Sentinel-5P satellite data, the relative change in tropospheric NO₂ air pollution during the three lockdowns was calculated. The estimation of regional infrastructure intensity was performed using Kernel Density Estimation, being the predictor variable. By performing hotspot-coldspot analysis on the relative change in NO₂ air pollution, significant spatial clusters of decreased air pollution during the three lockdowns were identified. Based on the clusters, a novel spatial index, the Clustering Index (CI) was developed using its Coldspot Clustering Index (CCI) variant as a predicted variable in the regression model between infrastructure intensity and NO₂ air pollution decline. The analysis revealed that during the three lockdowns there was a strong and statistically significant relationship between the transportation infrastructure and the decline index, CCI (r = 0.899, R² = 0.808). The results showed that the largest decrease in NO₂ air pollution was recorded during the first lockdown, and in this case, there was the strongest inverse correlation with transportation infrastructure (r = -0.904, R² = 0.818). Economic and population predictors also explained with good fit the decrease in NO₂ air pollution during the first lockdown: GDP (R² = 0.511), employees (R² = 0.513), population density (R² = 0.837). It is concluded that not only economic-population variables determined the reduction of near-surface air pollution but also the transportation infrastructure. Further studies are recommended to investigate other pollutant gases as predicted variables.

Spatial and Temporal Analysis of COVID-19 in the Elderly Living in Residential Care Homes in Portugal

Background: The goal of this study is to identify geographic areas for priority actions in order to control COVID-19 among the elderly living in Residential Care Homes (RCH). We also describe the evolution of COVID-19 in RHC throughout the 278 municipalities of continental Portugal between March and December 2020. Methods: A spatial population analysis of positive COVID-19 cases reported by the Portuguese National Health Service (NHS) among the elderly living in RCH. The data are for COVID-19 testing, symptomatic status, comorbidities, and income level by municipalities. COVID-19 measures at the municipality level are the proportion of positive cases of elderly living in RCH, positive cases per elderly living in RCH, symptomatic to asymptomatic ratio, and the share of comorbidities cases. Spatial analysis used the Kernel density estimation (KDE), space-time statistic Scan, and geographic weighted regression (GWR) to detect and analyze clusters of infected elderly. Results: Between 3 March and 31 December 2020, the high-risk primary cluster was located in the regions of Braganca, Guarda, Vila Real, and Viseu, in the Northwest of Portugal (relative risk = 3.67), between 30 September and 13 December 2020. The priority geographic areas for attention and intervention for elderly living in care homes are the regions in the Northeast of Portugal, and around the large cities, Lisbon and Porto, which had high risk clusters. The relative risk of infection was spatially not stationary and generally positively affected by both comorbidities and low-income. Conclusion: The regions with a population with high comorbidities and low income are a priority for action in order to control COVID-19 in the elderly living in RCH. The results suggest improving both income and health levels in the southwest of Portugal, in the environs of large cities, such as Lisbon and Porto, and in the northwest of Portugal to mitigate the spread of COVID-19.

Resilience of the Romanian Independent Cultural Sector under COVID-19 Pandemic Using the Grounded Theory

In the event of any crisis, such as, in this case, the COVID-19 pandemic, new challenges arise, ranging from social and environmental phenomena to economic issues. One of the most affected economic sectors was the cultural one, especially independent artists, whose financial stability is usually inconsistent. The aim of this article was to test the immediate reactions of the cultural sector, both public and private, to the pandemic shock and, implicitly, to the restrictions imposed during the state of emergency in Romania (27 February–14 May 2020). By using grounded theory, 36 public documents of cultural stakeholders were coded and analyzed. All documents were identified in the Romanian online environment during the state of emergency. Based on the identified interrelationships, it was found that the independent contractors, self-employed workers in the creative-cultural sector, whether or not associated with NGOs or employees of public institutions, need financial and community support. However, the resilience of the cultural sector is conditioned by the creation of new multi-level policies for crisis management.

The Geographical Distribution and Influencing Factors of COVID-19 in China

The study of the spatial differentiation of COVID-19 in cities and its driving mechanism is helpful to reveal the spatial distribution pattern, transmission mechanism and diffusion model, and evolution mechanism of the epidemic and can lay the foundation for constructing the spatial dynamics model of the epidemic and provide theoretical basis for the policy design, spatial planning and implementation of epidemic prevention and control and social governance. Geodetector (Origin version, Beijing, China) is a great tool for analysis of spatial differentiation and its influencing factors, and it provides decision support for differentiated policy design and its implementation in executing the city-specific policies. Using factor detection and interaction analysis of Geodetector, 15 indicators of economic, social, ecological, and environmental dimensions were integrated, and 143 cities were selected for the empirical research in China. The research shows that, first of all, risks of both infection and death show positive spatial autocorrelation, but the geographical distribution of local spatial autocorrelation differs significantly between the two. Secondly, the inequalities in urban economic, social, and residential environments interact with COVID-19 spatial heterogeneity, with stronger explanatory power especially when multidimensional inequalities are superimposed. Thirdly, the spatial distribution and spread of COVID-19 are highly spatially heterogeneous and correlated due to the complex influence of multiple factors, with factors such as Area of Urban Construction Land, GDP, Industrial Smoke and Dust Emission, and Expenditure having the strongest influence, the factors such as Area of Green, Number of Hospital Beds and Parks, and Industrial NOx Emissions having unignorable influence, while the factors such as Number of Free Parks and Industrial Enterprises, Per-GDP, and Population Density play an indirect role mainly by means of interaction. Fourthly, the factor interaction effect from the infected person’s perspective mainly shows a nonlinear enhancement effect, that is, the joint influence of the two factors is greater than the sum of their direct influences; but from the perspective of the dead, it mainly shows a two-factor enhancement effect, that is, the joint influence of the two factors is greater than the maximum of their direct influences but less than their sum. Fifthly, some suggestions are put forward from the perspectives of building a healthy, resilient, safe, and smart city, providing valuable reference and decision basis for city governments to carry out differentiated policy design.

A Vector Field Approach to Estimating Environmental Exposure Using Human Activity Data

Environmental exposure of people plays an important role in assessing the quality of human life. The most existing methods that estimate the environmental exposure either focus on the individual level or do not consider human mobility. This paper adopts a vector field generated from the observed locations of human activities to model the environmental exposure at the population level. An improved vector-field-generation method was developed by considering people’s decision-making factors, and we proposed two indicators, i.e., the total exposure indicator (TEI) and the average exposure indicator (AEI), to assess various social groups’ environmental exposure. A case study about the risky environmental exposure of coronavirus disease 2019 (COVID-19) was conducted in Guangzhou, China. Over 900 participants with various socioeconomic backgrounds were involved in the questionnaire, and the survey-based activity locations were extracted to generate the vector field using the improved method. COVID-19 pandemic exposure (or risk) was estimated for different social groups. The findings show that people in the low-income group have an 8% to 10% higher risk than those in the high-income group. This new method of vector field may benefit geographers and urban researchers, as it provides opportunities to integrate human activities into the metrics of pandemic risk, spatial justice, and other environmental exposures.

Applications of GIS and geospatial analyses in COVID-19 research: A systematic review

Background: Geographic information science (GIS) has established itself as a distinct domain and incredibly useful whenever the research is related to geography, space, and other spatio-temporal dimensions. However, the scientific landscape on the integration of GIS in COVID-related studies is largely unknown. In this systematic review, we assessed the current evidence on the implementation of GIS and other geospatial tools in the COVID-19 pandemic.

Methods: We systematically retrieved and reviewed 79 research articles that either directly used GIS or other geospatial tools as part of their analysis. We grouped the identified papers under six broader thematic groups based on the objectives and research questions of the study- environmental, socio-economic, and cultural, public health, spatial transmission, computer-aided modeling, and data mining.

Results: The interdisciplinary nature of how geographic and spatial analysis was used in COVID-19 research was notable among the reviewed papers. Geospatial techniques, especially WebGIS, have even been widely used to visualize the data on a map and were critical to informing the public regarding the spread of the virus, especially during the early days of the pandemic. This review not only provided an overarching view on how GIS has been used in COVID-19 research so far but also concluded that geospatial analysis and technologies could be used in future public health emergencies along with statistical and other socio-economic modeling techniques. Our review also highlighted how scientific communities and policymakers could leverage GIS to extract useful information to make an informed decision in the future.

Conclusions:  Despite the limited applications of GIS in identifying the nature and spatio-temporal pattern of this raging pandemic, there are opportunities to utilize these techniques in handling the pandemic. The use of spatial analysis and GIS could significantly improve how we understand the pandemic as well as address the underserviced demographic groups and communities.

Spatiotemporal Characteristics and Risk Factors of the COVID-19 Pandemic in New York State: Implication of Future Policies

The Coronavirus disease 2019 (COVID-19) has been spreading in New York State since March 2020, posing health and socioeconomic threats to many areas. Statistics of daily confirmed cases and deaths in New York State have been growing and declining amid changing policies and environmental factors. Based on the county-level COVID-19 cases and environmental factors in the state from March to December 2020, this study investigates spatiotemporal clustering patterns using spatial autocorrelation and space-time scan analysis. Environmental factors influencing the COVID-19 spread were analyzed based on the Geodetector model. Infection clusters first appeared in southern New York State and then moved to the central western parts as the epidemic developed. The statistical results of space-time scan analysis are consistent with those of spatial autocorrelation analysis. The analysis results of Geodetector showed that both temperature and population density were strong indications of the monthly incidence of COVID-19, especially in March and April 2020. There is a trend of increasing interactions between various risk factors. This study explores the spatiotemporal pattern of COVID-19 in New York State over ten months and explains the relationship between the disease transmission and influencing factors.

The US COVID Atlas: A dynamic cyberinfrastructure surveillance system for interactive exploration of the pandemic

Distributed spatial infrastructures leveraging cloud computing technologies can tackle issues of disparate data sources and address the need for data-driven knowledge discovery and more sophisticated spatial analysis central to the COVID-19 pandemic. We implement a new, open source spatial middleware component (libgeoda) and system design to scale development quickly to effectively meet the need for surveilling county-level metrics in a rapidly changing pandemic landscape. We incorporate, wrangle, and analyze multiple data streams from volunteered and crowdsourced environments to leverage multiple data perspectives. We integrate explorative spatial data analysis (ESDA) and statistical hotspot standards to detect infectious disease clusters in real time, building on decades of research in GIScience and spatial statistics. We scale the computational infrastructure to provide equitable access to data and insights across the entire USA, demanding a basic but high-quality standard of ESDA techniques. Finally, we engage a research coalition and incorporate principles of user-centered design to ground the direction and design of Atlas application development.

Exploring spatiotemporal effects of the driving factors on COVID-19 incidences in the contiguous United States

Since December 2019, the world has witnessed the stringent effect of an unprecedented global pandemic, coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of January 29,2021, there have been 100,819,363 confirmed cases and 2,176,159 deaths reported. Among the countries affected severely by COVID-19, the United States tops the list. Research has been conducted to discuss the causal associations between explanatory factors and COVID-19 transmission in the contiguous United States. However, most of these studies focus more on spatial associations of the estimated parameters, yet exploring the time-varying dimension in spatial econometric modeling appears to be utmost essential. This research adopts various relevant approaches to explore the potential effects of driving factors on COVID-19 counts in the contiguous United States. A total of three global spatial regression models and two local spatial regression models, the latter including geographically weighted regression (GWR) and multiscale GWR (MGWR), are performed at the county scale to take into account the scale effects. For COVID-19 cases, ethnicity, crime, and income factors are found to be the strongest covariates and explain most of the variance of the modeling estimation. For COVID-19 deaths, migration (domestic and international) and income factors play a critical role in explaining spatial differences of COVID-19 deaths across counties. Such associations also exhibit temporal variations from March to July, as supported by better performance of MGWR than GWR. Both global and local associations among the parameters vary highly over space and change across time. Therefore, time dimension should be paid more attention to in the spatial epidemiological analysis. Among the two local spatial regression models, MGWR performs more accurately, as it has slightly higher Adj. R² values (for cases, R² = 0.961; for deaths, R² = 0.962), compared to GWR's Adj. R² values (for cases, R² = 0.954; for deaths, R² = 0.954). To inform policy-makers at the nation and state levels, understanding the place-based characteristics of the explanatory forces and related spatial patterns of the driving factors is of paramount importance. Since it is not the first time humans are facing public health emergency, the findings of the present research on COVID-19 therefore can be used as a reference for policy designing and effective decision making.

Differencing the Risk of Reiterative Spatial Incidence of COVID-19 Using Space–Time 3D Bins of Geocoded Daily Cases

The space–time behaviour of COVID-19 needs to be analysed from microdata to understand the spread of the virus. Hence, 3D space–time bins and analysis of associated emerging hotspots are useful methods for revealing the areas most at risk from the pandemic. To implement these methods, we have developed the SITAR Fast Action Territorial Information System using ESRI technologies. We first modelled emerging hotspots of COVID-19 geocoded cases for the region of Cantabria (Spain), then tested the predictive potential of the method with the accumulated cases for two months ahead. The results reveal the difference in risk associated with areas with COVID-19 cases. The study not only distinguishes whether a bin is statistically significant, but also identifies temporal trends: a reiterative pattern is detected in 58.31% of statistically significant bins (most with oscillating behaviour over the period). In the testing method phase, with positive cases for two months ahead, we found that only 7.37% of cases were located outside the initial 3D bins. Furthermore, 83.02% of new cases were in statistically significant previous emerging hotspots. To our knowledge, this is the first study to show the usefulness of the 3D bins and GIS emerging hotspots model of COVID-19 microdata in revealing strategic patterns of the pandemic for geoprevention plans.

Intracity Pandemic Risk Evaluation Using Mobile Phone Data: The Case of Shanghai during COVID-19

The coronavirus disease 2019 (COVID-19) pandemic has provided an opportunity to rethink the development of a sustainable and resilient city. A framework for comprehensive intracity pandemic risk evaluation using mobile phone data is proposed in this study. Four steps were included in the framework: identification of high-risk groups, calculation of dynamic population flow and construction of a human mobility network, exposure and transmission risk assessment, and pandemic prevention guidelines. First, high-risk groups were extracted from mobile phone data based on multi-day activity chains. Second, daily human mobility networks were created by aggregating population and origin-destination (OD) flows. Third, clustering analysis, time series analysis, and network analysis were employed to evaluate pandemic risk. Finally, several solutions are proposed to control the pandemic. The outbreak period of COVID-19 in Shanghai was used to verify the proposed framework and methodology. The results show that the evaluation method is able to reflect the different spatiotemporal patterns of pandemic risk. The proposed framework and methodology may help prevent future public health emergencies and localized epidemics from evolving into global pandemics.

Applications of GIS and geospatial analyses in COVID-19 research: A systematic review

Background: Geographic information science (GIS) has established itself as a distinct domain and incredibly useful whenever the research is related to geography, space, and other spatio-temporal dimensions. However, the scientific landscape on the integration of GIS in COVID-related studies is largely unknown. In this systematic review, we assessed the current evidence on the implementation of GIS and other geospatial tools in the COVID-19 pandemic. Methods: We systematically retrieved and reviewed 79 research articles that either directly used GIS or other geospatial tools as part of their analysis. We grouped the identified papers under six broader thematic groups based on the objectives and research questions of the study- environmental, socio-economic, and cultural, public health, spatial transmission, computer-aided modeling, and data mining. Results: The interdisciplinary nature of how geographic and spatial analysis was used in COVID-19 research was notable among the reviewed papers. Geospatial techniques, especially WebGIS, have even been widely used to visualize the data on a map and were critical to informing the public regarding the spread of the virus, especially during the early days of the pandemic. This review not only provided an overarching view on how GIS has been used in COVID-19 research so far but also concluded that geospatial analysis and technologies could be used in future public health emergencies along with statistical and other socio-economic modeling techniques. Our review also highlighted how scientific communities and policymakers could leverage GIS to extract useful information to make an informed decision in the future. Conclusions:  Despite the limited applications of GIS in identifying the nature and spatio-temporal pattern of this raging pandemic, there are opportunities to utilize these techniques in handling the pandemic. The use of spatial analysis and GIS could significantly improve how we understand the pandemic as well as address the underserviced demographic groups and communities.

Is Crowdsourcing a Reliable Method for Mass Data Acquisition? The Case of COVID-19 Spread in Greece During Spring 2020

We present a GIS-based crowdsourcing application that was launched soon after the first COVID-19 cases had been recorded in Greece, motivated by the need for fast, location-wise data acquisition regarding COVID-19 disease spread during spring 2020, due to limited testing. A single question was posted through a web App, to which the anonymous participants subjectively answered whether or not they had experienced any COVID-19 disease symptoms. Our main goal was to locate geographical areas with increased number of people feeling the symptoms and to determine any temporal changes in the statistics of the survey entries. It was found that the application was rapidly disseminated to the entire Greek territory via social media, having, thus, a great public reception. The higher percentages of participants experiencing symptoms coincided geographically with the highly populated urban areas, having also increased numbers of confirmed cases, while temporal variations were detected that accorded with the restrictions of activities. This application demonstrates that health systems can use crowdsourcing applications that assure anonymity, as an alternative to tracing apps, to identify possible hot spots and to reach and warn the public within a short time interval, increasing at the same time their situational awareness. However, a continuous reminder for participation should be scheduled.