The outbreak of SARS-CoV-2 in Wuhan, China in late December 2019 became the harbinger of the COVID-19 pandemic. During the pandemic, geospatial techniques, such as modeling and mapping, have helped in disease pattern detection. Here we provide a synthesis of the techniques and associated findings in relation to COVID-19 and its geographic, environmental, and socio-demographic characteristics, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) methodology for scoping reviews. We searched PubMed for relevant articles and discussed the results separately for three categories: disease mapping, exposure mapping, and spatial epidemiological modeling. The majority of studies were ecological in nature and primarily carried out in China, Brazil, and the USA. The most common spatial methods used were clustering, hotspot analysis, space-time scan statistic, and regression modeling. Researchers used a wide range of spatial and statistical software to apply spatial analysis for the purpose of disease mapping, exposure mapping, and epidemiological modeling. Factors limiting the use of these spatial techniques were the unavailability and bias of COVID-19 data—along with scarcity of fine-scaled demographic, environmental, and socio-economic data—which restrained most of the researchers from exploring causal relationships of potential influencing factors of COVID-19. Our review identified geospatial analysis in COVID-19 research and highlighted current trends and research gaps. Since most of the studies found centered on Asia and the Americas, there is a need for more comparable spatial studies using geographically fine-scaled data in other areas of the world.

University–community partnerships offer synergistic spaces for communities to address difficulties and universities to meet their missions. Geographers are well positioned to participate in these partnerships, owing to the discipline's integrative nature, spatial perspectives and analytic approaches, and its attention to social and environmental issues at many scales. This paper endeavours to assist geographers interested in these partnerships by illustrating three case studies of geography–community partnerships. We reflect critically on the mutual benefits from, and barriers to, these partnerships. On the basis of case studies and literature review, the paper recommends ways to enhance university–community partnerships to help dismantle the ivory tower.

This study focuses on the spatiotemporal analysis of early summer heatwaves, droughts, and their relationship with vegetation and soil moisture from 2013 to 2022 in South Punjab, Pakistan. The study uses a unique approach of utilizing a combination of meteorological-based maximum air temperature Tmax derived from Modern-Era Retrospective analysis for Research and Applications V.2 (MERRA-2) and remotely sensed land surface temperature (LST) to calculate heatwave metrics. Station-based precipitation (P) and remote sensing-based temperature condition index (TCI) are utilized to calculate droughts. The normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI) are used to analyze the relationship between extreme events with vegetation and soil moisture. The results reveal a significant rise in the region's heatwave frequency, duration, and intensity. Spatiotemporal analysis of the SPI and TCI shows the occurrence of droughts in more heatwave years. Negative Pearson correlation R = 0.5–0.9 and coefficient of determination R2 of 0.76–0.86 of the NDVI and NDWI with drought and heatwave metrics represents a significant (<0.01) relationship and direct response of vegetation and soil moisture towards extreme events. The study affirms the hydrological vulnerability of arid regions due to extreme events and marks the attention to appropriate mitigation and adaptation strategies in the future.

Background: Cutaneous leishmaniasis (CL) is a wide-reaching infection of major public health concern. Iran is one of the six most endemic countries in the world. This study aims to provide a spatiotemporal visualization of CL cases in Iran at the county level from 2011 to 2020, detecting high-risk zones, while also noting the movement of high-risk clusters.Methods: On the basis of clinical observations and parasitological tests, data of 154,378 diagnosed patients were obtained from the Iran Ministry of Health and Medical Education. Utilizing spatial scan statistics, we investigated the disease’s purely temporal, purely spatial, spatial variation in temporal trends and spatiotemporal patterns. AtP = 0.05 level, the null hypothesis was rejected in every instance.Results: In general, the number of new CL cases decreased over the course of the 9-year research period. From 2011 to 2020, a regular seasonal pattern, with peaks in the fall and troughs in the spring, was found. The period of September–February of 2014–2015 was found to hold the highest risk in terms of CL incidence rate in the whole country [relative risk (RR) = 2.24,P < 0.001)]. In terms of location, six significant high-risk CL clusters covering 40.6% of the total area of the country were observed, with theRRranging from 1.87 to 9.69. In addition, spatial variation in the temporal trend analysis found 11 clusters as potential high-risk areas that highlighted certain regions with an increasing tendency. Finally, five space-time clusters were found. The geographical displacement and spread of the disease followed a moving pattern over the 9-year study period affecting many regions of the country.Conclusions: Our study has revealed significant regional, temporal, and spatiotemporal patterns of CL distribution in Iran. Over the years, there have been multiple shifts in spatiotemporal clusters, encompassing many different parts of the country from 2011 to 2020. The results reveal the formation of clusters across counties that cover certain parts of provinces, indicating the importance of conducting spatiotemporal analyses at the county level for studies that encompass entire countries. Such analyses, at a finer geographical scale, such as county level, might provide more precise results than analyses at the scale of the province.Graphical

There are different area-based factors affecting the COVID-19 mortality rate in urban areas. This research aims to examine COVID-19 mortality rates and their geographical association with various socioeconomic and ecological determinants in 350 of Tehran’s neighborhoods as a big city. All deaths related to COVID-19 are included from December 2019 to July 2021. Spatial techniques, such as Kulldorff’s SatScan, geographically weighted regression (GWR), and multi-scale GWR (MGWR), were used to investigate the spatially varying correlations between COVID-19 mortality rates and predictors, including air pollutant factors, socioeconomic status, built environment factors, and public transportation infrastructure. The city’s downtown and northern areas were found to be significantly clustered in terms of spatial and temporal high-risk areas for COVID-19 mortality. The MGWR regression model outperformed the OLS and GWR regression models with an adjusted R² of 0.67. Furthermore, the mortality rate was found to be associated with air quality (e.g., NO₂, PM₁₀, and O₃); as air pollution increased, so did mortality. Additionally, the aging and illiteracy rates of urban neighborhoods were positively associated with COVID-19 mortality rates. Our approach in this study could be implemented to study potential associations of area-based factors with other emerging infectious diseases worldwide.