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Ecotourism is now the fastest-growing sector in the Himalayan region as well as in the Garhwal region (Uttarakhand, India) as it has negligible adverse impacts on the environment and natural resources than tourism. Ecotourism plays an important role in the protection and sustainability of natural resources. Thus, the present study attempts to identify potential ecotourism sites using the analytic hierarchy process (AHP) and Geographical Information System-Remote sensing (GIS-RS) techniques in the Garhwal Himalayan region. The study is based on the use of GIS-RS used parameter concerning landscape naturalness, climatic characteristics, topographic attributes, accessibility parameters, reserved and protected areas, and natural attractiveness using a weighted overlay method in the GIS platform. We also used expert knowledge to assign weights and then normalized them by AHP eigenvector. We used the receiver operating characteristic curve for validation, which indicates the methods are very useful in ecotourism potentiality. The results show very highly, and the highly suitable area is about 21.12%, wherein 17.40% located in the greater Himalayan region. Areas adjacent to the densely forested areas, where snow-out occurs every year, develop various grasslands, cool climate, U and V-shaped valleys, very attractive landscapes sites suitable for ecotourism, but not for all seasons. The moderately suitable areas confined in the lower dissected valleys and upper snow-covered areas and make up about 26.04% (8456.68 km 2 ) of the region. This study can help tourism planners and the government select locations precisely and further develop ecotourism activities and release pressures on the tourism burden in the region. The results have implications for sustainable tourism and ecotourism efforts of the United Nations Sustainable Development Goal-15 (SDG-15) of improving life on land by preserving natural heritage, wilderness areas, and culture. It can help the employment generation of the local people and direct profits to the local communities. Graphic abstract

This study presents a geospatial approach in conjunction with a multi-criteria decision-making (MCDM) tool for mapping forest fire risk zones in the district of Ri-Bhoi, Meghalaya, India which is very rich in biodiversity. Analytical hierarchy process (AHP)-based pair-wise comparison matrix was constructed to compare the selected parameters against each other based on their impact/influence (equal, moderate, strong, very strong, and extremely strong) on a forest fire. The final output delineated fire risk zones in the study area in four categories that include very high-risk, high-risk, moderate-risk, and low-risk zones. The delineated fire risk zones were found to be in close agreement with actual fire points obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) fire data for the study area. Results indicated that Ri-Bhoi’s 804.31 sq. km. (32.86%) the area was under ‘very high’ fire susceptibility. This was followed by 583.10 sq. km. (23.82%), 670.47 sq. km. (27.39%), and 390.12 sq. km. (15.93%) the area under high, moderate, and low fire risk categories, respectively. These results can be used effectively to plan fire control measures in advance and the methodology suggested in this study can be adopted in other areas too for delineating potential fire risk zones.

Background The Guangxi government initiated two rounds of the Guangxi AIDS Conquering Project (GACP) in 2010 (Phase I) and 2015 (Phase II) to control human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) epidemics. However, the effectiveness of GACP in HIV prevention and treatment has rarely been reported. This study aimed to assess the effectiveness of the GACP implemented in Guangxi, China and provide data for strategy and praxis improvements to achieve Joint United Nations Programme on HIV/AIDS (UNAIDS) 95-95 targets. Methods We used spatial approaches to trace the spatiotemporal distribution properties, epidemic trends, and correlation between macroscopic factors and HIV incidence using data from the Chinese HIV/AIDS case reporting system to explore the effects of the GACP. Results During the GACP era, the HIV epidemic stabilized in urban centers, showing a downward trend in the Hengzhou and Binyang Counties in the eastern region, whereas it continued to increase in rural areas of the northwest region, such as the Long’an, Mashan, Shanglin, and Wuming Districts. The linear directional mean (LDM) of HIV infection reported cases displayed a southeast–northwest direction, with an LDM value of 12.52°. Compared with that in Phase I, Hengzhou withdrew from the high-high clustering area, and the west–north suburban counties pulled out the low-low clustering area during Phase II. Significant HIV clusters were identified in the eastern region during Phase I, whereas these clusters emerged in the northwestern areas during Phase II. Regarding HIV, socioeconomic status, population mobility, and medical care levels were the key social drivers of heterogeneous spatial distribution. Conclusions The GACP assisted in effectively managing the HIV epidemic in urban and eastern areas of Nanning City. However, prevention and control efforts in rural regions, particularly those located in the northwest, may not have yielded comparable outcomes. To address this disparity, allocating additional resources and implementing tailored intervention measures for these rural areas are imperative.

Climate change has led to increased sea levels, which are caused by a complex interplay of the physical environment components from coastal areas, causing the rise in storm surge, erosion and flooding. In this scenario, the low-lying topography of the Mumbai region is highly susceptible to sea level-induced flooding and coastal erosion due to the increasing number of economic activities. The unsustainable urbanization, unplanned development, and huge land conversion lead to the destruction of this region lead to the destruction of mangroves and filled waterways with construction debris which makes the region more vulnerable to flooding due to inadequate drainage, overflow and absence of natural protectors. These human-induced factors and their impacts remain unknown. Therefore, the study uses four socioeconomic variables (CVI4) with five geological (CVI5) and three geological variables (CVI8; with integrating CVI5) to assess the role of developmental and socio-economic activities in overall coastal vulnerability (CVI12) analysis. To quantify the importance of the combined variables and understand the response, random forest (RF) model was also used. This study selected four different iterations with integrating the pixel-based differentially weighted rank values of all variables to determine the significant causes behind that have an impact on coastal vulnerability index (CVI). The results show that CVI5 and CVI8 contributed 7.8% and 36.9%, respectively, whereas CVI4 contributed 55.3% to the CVI12. The response curve shows that the influence of these variables is an increasing trend to CVI12 and the results of CVI12 are highly correlated with socioeconomic index variables ( r  = 0.84, p  = 0.001) which indicates the socio-economic variables played a major role towards the coastal vulnerability of the region. It suggests that unsustainable urbanization, unplanned development and coastal erosion increasing pressure make Mumbai and Kurla region more vulnerable to flood. Accordingly, CVI12 results show 55.83 km of the shoreline surveyed, being very low vulnerable, a moderate vulnerability of 60.91 km, while a high vulnerability of 50.75 km is considered to be very high. The results may be used as a guide in formulating policies to mitigate and adjust the Mumbai coast as the rise in sea level is expected to cause more frequent coastal floods, etc.

A detailed understanding of forest cover changes and fragmentation is essential for guiding effective conservation and reforestation efforts. This study analyzes land use changes, forest loss, and fragmentation in Jharsuguda, India, over three decades (1993–2023) using Landsat satellite imagery. The results revealed significant land use transitions, with forest cover declining dramatically from 854.79 sq. km in 1993 to 386.4 sq. km in 2023, while built-up areas expanded substantially from 82.26 sq. km to 343.74 sq. km. Core forest fragments shrank significantly, dropping from 271.47 sq. km to 80.21 sq. km, indicating severe habitat degradation. The highest deforestation rate, − 0.047% per year, was observed between 2013 and 2023. Canopy density analysis highlighted notable differences among forest types, reflecting the varying impacts of human activities. These findings underscore the urgent need for sustainable land use planning and offer critical insights for policymakers to address forest degradation and promote global forest conservation initiatives.

The US is exposed to myriad natural hazards causing USD billions in damages and thousands of fatalities each year. Significant population and economic growth during the last several decades have resulted in more people residing in hazardous places. However, consistent national-scale hazard threat assessment techniques reflecting the state of hazard knowledge are not readily available for application in risk and vulnerability assessments. Mapping natural hazard threats is the crucial first step in identifying and implementing threat reduction or mitigation strategies. In this study, we demonstrate applied GIS approaches for creating and synthesizing US hazard threat extents using publicly available data for 15 natural hazards. Individually mapping each threat enables empirically supported intervention development and the building of a Composite Hazard Index (CHI). Summarizing the hazard frequencies provides a novel representation of US hazardousness. Implementing cluster analysis to regionalize US counties based on their underlying hazard characteristics offers insight into hazard threats’ spatial (non-political) natures. The results indicate that the southeast, central plains, and coastal regions of the northeast had high hazard occurrence scores, whereas more moderate hazard scores were observed west of the continental divide. Furthermore, while no place is safe from hazard occurrence, identifying each region’s distinct “hazardousness” can support individualized risk assessments and mitigation intervention development.

The connectivity and interdependence of our world and its inhabitants’ health have come under increasing focus. Elucidation of the common and interdependent mechanisms of health and disease requires approaches that facilitate understanding of complex systems behavior and probing of both individual and collective system parameters. To this end, multiscale physical and computational modeling offers a particularly powerful tool to predict behavior over vast time and length scales. Other novel technologies, e.g., rapid isolation nanotechnology developed to analyze nanoscale small extracellular vesicles in ocular tears, enable tracking of “fingerprints” from diseases as diverse as ocular to neurodegenerative (e.g., dementia) and cancer. In the future, it will be possible to track the health and disease of ecosystems and their inhabitants, using geospatial and epidemiological approaches, as well as novel biotechnologies, to prevent and mitigate disease processes and enhance well‐being. These concepts are applied by way of an exemplary approach to understand and address the impact of toxic, recalcitrant manmade chemicals (i.e., PFAS) on the health of ecosystems and their diverse inhabitants. Such convergent efforts will be necessary and a priority for solving the complex problems threatening the health of our planet and its inhabitants. Probing and sustaining the health of Earth's ecosystems and their inhabitants requires convergent approaches at multiple length and time scales. Predictive computational and physical modeling, together with novel biotechnologies applied in unexpected ways, are paving the way for discoveries at the intersection of epidemiology and geospatial science, with implications for the health of our planet and all its inhabitants.

Introduction Over the period of time, the increasing trend of voluntary cesarean section (CS) delivery has become a serious maternal health concern. Previous studies have shown wide geographic variations in the prevalence of voluntary CS delivery constraints by several sociodemographic determinants. However, none of them have attempted to disentangle this phenomenon by using spatially varying coefficient models to consider local factors. This study aims to identify the spatially heterogeneous relationships among voluntary CS deliveries against the backdrop of sociodemographic determinants in India. Data and Methods This study utilized data from the National Family Health Survey (NFHS-5), 2019-21. The ordinary least square (OLS) model was used as a base model, and multiscale geographically weighted regression (MGWR) was employed at the district level to consider the local factors associated with voluntary CS delivery. An appropriate model diagnostic check was also performed to make our model robust and reliable. Several maps were produced using ArcGIS Pro to visualize the distribution of coefficients of several factors. Results More than half (58%) of the total CS deliveries were voluntary; however, choropleth maps have shown that southern peninsular and northeastern districts have a higher prevalence than the rest. The distribution of voluntary CS deliveries with a Moran’s Index of 0.22 was also spatially autocorrelated. Moreover, the values of Jarque–Bera statistics and Koenker (BP) Statistics show that the OLS model residuals were not normally distributed and reflected heteroskedasticity. The estimates from the MGWR show that four and above ANC visits and mass media exposure show a positive relationship with voluntary CS deliveries. The local R-squared map explains nearly half of the variation for voluntary CS deliveries for all northeastern districts. The model fit metric shows higher R square and lower corrected Akaike’s Information Criterion (AICc) values for the MGWR model, which validates that the MGWR model is more robust and parsimonious. Conclusion The present study revealed that although voluntary CS deliveries are at an increasingly alarming pace, the geographic distribution of voluntary CS deliveries is, albeit unequally. Therefore, this investigation has made an effort to examine how spatial analysis can help identify the geographic patterns and factors that explain this variation in voluntary CS deliveries across space. These findings add value to our understanding of how geographic space and scale matter when health is examined. Public health practitioners can recognize such variations when devising targeted interventions for CS deliveries in India and other developing nations.

Climate change and increase in global surface temperature are growing concerns worldwide, especially big urban agglomerations like National Capital Region of India, New Delhi and surrounding region have experienced exponential urbanization paving way to horizontal spilling of urban built-up areas, which consequently amplifid the climate variability and surface temperature change over the past few decades. Threfore, the city is highly susceptible to several climate extremes, including heat waves, cold waves, droughts, and flods, impacting socioeconomic lives of over 20 million population. In this study, we applied remote sensing and GIS approaches to study climate variability and its impacts on urban areas. Indicators such as the Land Surface Temperature (LST), Urban Heat Islands (UHI), Normalized Diffrence Vegetation Index (NDVI), and Land Use Land Cover (LULC), were calculated using satellite data for the years 1993, 2000, 2010, and 2020. Th result shows that LST values sharply rose as the maximum value reached 6.9°C in the last three decades (1993-2020), and UHIs maximum values reached 1.76, indicating a clear warming trend in the study area. During this period, the NDVI levels have decreased considerably, going from 0.59 to 0.21, which can be attributed to the expanding urbanization and the decreased green area. Th LULC loss and gain analysis revealed that the urban area has rapidly expanded. In contrast, it resulted in loss of agricultural land, barren and scrubs, water bodies and forest area. Th results show vast climate variability in the region posing threat to environment and socio-economic livelihood of the population.

The rapid increase in urbanization forced the change in land-use patterns from agriculture to impermeable residential. This phenomenon will negatively affect both direct and indirect agricultural employment opportunities and productivity. A systematic technique helps in recovering the decremental agricultural land proportion. Spatial analysis of a region is a leading scientific method for land characteristics and suitability assessment. Land suitability can assist in the creation of agricultural productivity growth strategies. Geographic Information System (GIS) is successful in multiple criterion-based decision approaches. This study considered bare land extracted by supervised classification on the collected georeferenced Landsat-8 satellite image as the study area. Organic compounds, conductivity, pH, and soil texture were the criteria for estimating the suitable regions for agricultural activities in our study area, Sivanthipatti village, Tirunelveli district. The analytical hierarchy process (AHP) helped to assign weightage for the selected criteria. The combined AHP and GIS technique includes creating an AHP hierarchy, defining evaluation criteria, conducting pairwise comparisons, and creating criterion maps and land suitability analysis maps. The criterion weightage determined from AHP for pH, organic carbon content, salinity, and soil texture is 54.86%, 28.05%, 11.51%, and 5.58%, respectively. This analysis estimated that around 1750 acres of land are readily suitable for agricultural land-use conversion from its existing bare land surface.