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.

Changes in land use and land cover (LULC) are critical for evaluating global spatio-temporal trends, especially regarding climate change and urbanization. This study investigates the dynamics of Landsat Surface Temperature (LST) in response to LULC changes and their effects on the seasonal microclimate in Kasur District, Pakistan. Using the Google Earth Engine (GEE) platform, we employed Random Forest (RF) algorithm to detect LULC changes (cropland, forest, built up, fallow, barren, and water) and analyze seasonal spectral indices from Landsat imagery for 1988, 2002, and 2022. Significant LULC changes were observed, including a 9.8% increase in built-up areas, a 4.2% decrease in cropland, and a 1.4% decrease in forested areas, linked to urban heat island effects and population growth. Additionally, there was a 2.7% increase in fallow and open land, contributing to the district's impervious surface area. Significant correlations (p < 0.001) were found between LST and spectral indices—Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Normalized Difference Built Index (NDBI) ranging from 0.7 to 0.8 in both winter and summer. In summer, the maximum LST increased from 43°C in 1988 to 44°C in 2002, with a linear correlation (R²) increase from 0.57 to 0.75 and a polynomial correlation (R²) increase from 0.63 to 0.76 with NDBI from 1988 to 2022. Understanding these dynamics is crucial as LULC changes and resulting temperature variations have significant implications for local climate, agriculture, and human health. This study underscores the need for effective LULC policies to mitigate impacts, protect vegetation cover, and ensure sustainable land management. These findings provide valuable insights for policymakers and urban planners aiming to balance development with environmental sustainability.

Land cover analysis and land use transformations, which are mainly associated with population growth, are considered as essential elements for studying the relationship between population growth, land use conversions and environmental consequences. Most cities of the world are becoming urbanized with every passing day and human induced land cover changes are modifying the pattern of land use in urban areas. Currently, Pakistan is standing at an influential place with regard to the size of its population, occupying sixth position among the world countries. Almost all its administrative entities have experienced unprecedented population growth throughout its entire life. Its urban areas are expanding more rapidly than their rural counterparts due to both by natural increase and by rural-urban migration. Study area for the current research, Bahawalpur is one of the fastest growing cities of Pakistan ranking twelfth among the cities of Punjab Province. This study aims at to examine the historical population growth of the city since last fifty years correlated with changing land use pattern and urban area expansion. Study is based on secondary data analysis using urban expansion indices for measuring the rate of urban change with increasing population growth. Application of regression model also verifies the increasing rate of urban area with population size and indicates an alarming situation for urban and city planners. Trend of urban growth in Bahawalpur shows that city is moving promptly towards highly urbanized cities of Pakistan. Its unprecedented and unplanned expansion may cause serious urban problems. Thus, it needs a serious look and attention of city planners. This study can also be helpful in providing some base line information.