This study spatially analyzes localized geography of health insurance coverage among Scheduled Tribe (ST) households in India, revealing persistent regional disparities using nationally representative data from NFHS-5 and advanced spatial analytical techniques. Employing Exploratory Spatial Data Analysis (ESDA), Geographically Weighted (GW) correlation and Empirical Bayes Smoothing (EBS), the research identifies significant north-south and east-west rifts in economic status and insurance access. The findings reveal marked regional disparities, with significantly higher insurance coverage in southern and western districts, particularly in Kerala, Tamil Nadu, Andhra Pradesh, and parts of Gujarat compared to persistently low coverage in northern and eastern states such as Uttar Pradesh, Bihar, Jharkhand, and West Bengal. A Lorenz curve (Gini coefficient: 0.36) indicates moderate nationwide inequality in insurance coverage among ST households. Southern and western districts, notably in Kerala, Tamil Nadu, and Gujarat, exhibit stronger insurance penetration than northern and northeastern regions. While some ST populations in poorer northern areas benefit from targeted schemes, vast stretches of central and northeastern India remain underserved due to low economic development and weak healthcare infrastructure. Local Moran’s I analyses highlight High-High clusters in southern India and Low-Low clusters in the north-central belt, underscoring entrenched spatial disadvantage. The clustering effect suggests that health policy interventions in these regions have likely benefited from regional policy diffusion, where best practices and institutional capacities spill over into neighboring districts, creating reinforcing zones of success. Bivariate and GW correlation visualizations display a strong positive association between wealth status and insurance coverage, especially in southern and central regions. Conversely, regions with high proportions of BPL and poor ST households demonstrate strong negative correlations, indicating a double burden geography of economic vulnerability and exclusion from insurance schemes. Importantly, the findings emphasize the localized need for inclusive policy interventions to ensure universal access of insurance coverage for ST households, especially in identified vulnerable regions.

Farmers in most parts of Ethiopia are limited in their ability to produce crops by a lack of rain and its inconsistency, and climate change may make matters worse. One of the most common natural disasters is drought because it depletes natural resources, causes environmental destruction, and destroys the ecology that supports all life. Water harvesting is a desirable strategy for mitigating this problem. However, the identification of optimal sites for rainwater harvesting (RWH) is an important step to maximize the amount of water harvested and minimize the ecological impact. In this study, the remotely sensed data and geospatial modeling with the analytical hierarchical process (AHP) and Multi-Criteria Decision Analysis (MCDA) approach were used to identify a potential site for rainwater harvesting in the drought-prone area of Genale Dawa River Basin. The analysis has taken into account several thematic layers, such as soil texture, soil depth, slope, rainfall, land use land cover, separation from a road and a settlement, and drainage density. To locate the potential site, the aforementioned thematic layers were given the appropriate weights based on their relative value by AHP integrated with a GIS environment. This study identifies five potential classifications on the suitability map range from extremely suitable to not suitable. According to our result, 11.4% of the study area is extremely suitable, 49% is highly suitable, 34.1% is moderately suitable, 5.4% is barely acceptable, and 0.02% is not suitable. The outcome of the suitability map is particularly practical for decision-makers and planners to choose the best locations for rainwater harvesting.

Altitude plays a crucial role in determining habitat distribution, species diversity, and ecological resources, which support livelihoods and employment in the Himalayan region. Covering 18% of India's territory, the Himalayas house over 50% of the country's forest cover and 40% of the Indian subcontinent's endemic species, providing ecosystem services to 115 million people. However, the relationships between elevation, ecological services, and population resource dependency are poorly understood. This study examines the spatial linkages between elevation, climate, and ecosystem distribution, as well as the relationship between ecosystem services and population density using livelihood indicators. Spatial analysis reveals an inverse relationship between population density and relative relief (-0.69; P<0.05). Population density is concentrated (14.44%) along the fertile Alaknanda and Bhagirathi River valleys (900–2200m), which cover 14.95% of the area. Conversely, 5.33% of the population is scattered across 41% of the region, with higher elevations (>2000m) relying on non-timber forest products (NTFPs) for over 30% of income. These findings highlight critical patterns in population distribution, resource dependency, and ecosystem services, offering valuable insights for policymakers to promote biodiversity conservation, equitable resource sharing, and sustainable development in the Himalayan region.

Climate is a natural process that influences weather patterns across different regions.  The term average climate refers to the typical weather conditions observed in a specific area over a long period. In India, agriculture is largely dependent on rainfall and variations in climatic factors significantly impacting the crop production. This study analyzes climate trends in the agro-climatic zones of Satara district using data procured from NASA’s Prediction of Worldwide Energy Resources (POWER) (1984-2022). The study examines key climatic parameters, including annual rainfall, maximum and minimum temperatures, relative humidity, soil wetness and soil moisture. To assess climate trends using various statistical methods such as the non-parametric Mann-Kendall test, Sen’s slope estimator and linear regression were applied. The results indicate notable fluctuations in climatic factors over the past four decades. The ghat zone covering areas like Mahabaleshwar, Wai, and Patan has recorded the highest rainfall and lowest temperatures. In contrast, the scarcity zone, including Man, Khatav, and Khandala tehsils, has experienced lower rainfall and a rise in annual maximum temperatures. These findings suggest that the agro-climatic zones of Satara district are continuously influenced by changing climatic conditions. Understanding these trends is crucial for farmers, local administrators, researchers, and policymakers to develop effective agricultural and environmental management strategies.