Abstract Background To examine associations between public park characteristics within different walking distances from residential locations and depression, to distinguish between features within parks (e.g. amenities, attractions, facilities, tree cover) and park metrics in the home area (e.g. number of parks, size, and total area), and to employ rigorous geospatial analysis linking the best available objectively measured park and urban green space (UGS) exposures to validated depression outcomes across multiple scales. Methods This population-based cross-sectional study utilised baseline data from 329,363 UK Biobank participants resident in urban areas. Prevalent diagnosed depression was defined as an ICD-10 code of F32 (depressive episode) or F33 (recurrent depressive disorder). Park characteristics and urban green space data were derived from Ordnance Survey Great Britain datasets and spatially linked to participants’ residential addresses. Three definitions of Home Catchment Area size were tested for every individual respondent: 400 m (m), 800 m, and 1600 m, as proxies for a 10-,20- and 40-min return walk respectively. Logistic regression models assessed associations with robust statistical approaches including assessment of interaction, correction for multiple testing, confounder adjustment, and sensitivity analyses. Results Specific park characteristics within 20-min and 40-min catchments were associated with reduced depression likelihood among women only. Within 40-min catchments, protective associations were observed for recreational amenities (cafés: odds ratio (OR) 0.89, 95% confidence interval (CI) 0.85–0.93; toilets: OR 0.85, 95% CI 0.79–0.91), attractions ( OR 0.83, 95% CI 0.80–0.87), sports facilities ( OR 0.84, 95% CI 0.79–0.90), and tree canopy coverage (e.g. > 20%, OR 0.88, 95% CI 0.85–0.91). In a 20-min catchment, each 1% increase in urban greenspace classified as parks was associated with 11% reduced depression odds among women ( OR 0.89, 95% CI 0.82–0.95). No significant protective associations were observed among men, with some paradoxical adverse associations identified. Conclusions This study provides robust evidence for protective associations between park characteristics and depression among women, but not men. Findings support proximity-based planning concepts but challenge the current policy and practice focus on 20-min neighbourhood and identify park features which optimise preventive potential. Results have direct implications for evidence-based urban planning policy internationally, providing a framework for developing mental health-supporting green infrastructure that recognises sex-based differences.

ABSTRACT Large Language Models (LLMs) have demonstrated substantial progress in task automation and natural language understanding. However, without domain expertise in geographic information science (GIS), they continue to encounter limitations including reduced accuracy and unstable performance when processing complex spatial tasks. To address these challenges, we propose GeoJSON agents—a novel multi-agent LLM architecture specifically designed for geospatial analysis. This framework transforms natural language instructions into structured GeoJSON operations through two widely adopted LLM enhancement techniques: function calling and code generation. The architecture integrates three core components: task parsing, agent collaboration, and result integration. The planner agent systematically decomposes user-defined tasks into executable subtasks, while specialized worker agents perform spatial data processing and analysis either by invoking predefined function APIs or by dynamically generating and executing Python-based analytical code. The system produces reusable, standards-compliant GeoJSON outputs through iterative refinement. To systematically evaluate both approaches, we constructed a hierarchical benchmark comprising 70 tasks spanning basic, intermediate, and advanced complexity levels, conducting experiments with OpenAI’s GPT-4o as the core model. Results indicate that the code generation–based agent achieved 97.14% accuracy, while the function calling–based agent attained 85.71%—both significantly outperforming the best-performing general-purpose model (48.57%). Comparative analysis reveals that code generation offers superior flexibility for complex, open-ended tasks, whereas function calling provides enhanced execution stability for structured operations. This study represents the first systematic integration of GeoJSON data with a multi-agent LLM framework and provides empirical evidence comparing two mainstream enhancement methodologies in geospatial contexts, offering new perspectives for improving GeoAI system performance and reducing barriers to GIS application.

ABSTRACT Accurate water body identification and extraction presents a critical challenge in geographic information systems, particularly for boundary-sensitive applications. While deep learning offers promising solutions for automated geospatial analysis, most semi-supervised methods inadequately model edge information. This study introduces a Boundary-Enhanced Consistency Semi-supervised Learning (BECSL) framework to address this gap. Our approach integrates Segment Anything Model (SAM) with OpenStreetMap (OSM) data to create multi-modal training datasets. The framework employs a dual-decoder architecture combining reverse attention and boundary enhancement mechanisms. This design generates refined pseudo-labels for unlabeled data supervision. We further develop a self-contrast strategy targeting regions with boundary prediction inconsistencies. Comprehensive evaluation on 2024EarthVQA and custom datasets demonstrates our method's effectiveness. The framework achieves superior performance using merely 10% labeled data while maintaining precise boundary delineation. This work provides both theoretical and practical advances in resource-efficient water extraction from remote sensing images.

This study investigates flooding and erosion impacts and human responses in Aoraki Mount Cook and Westland Tai Poutini national parks in Aotearoa New Zealand. These fast-eroding landscapes provide important test cases and insights for considering the public access dimensions of climate change. Our objectives were to explore and characterise the often-overlooked role of public access as a ubiquitous concern for protected areas and other area-based conservation approaches that facilitate connections between people and nature alongside their protective functions. We employed a mixed-methods approach including volunteered geographic information (VGI) from a park user survey (n = 273) and detailed case studies of change on two iconic mountaineering routes based on geospatial analyses of digital elevation models spanning 1986–2022. VGI data identified 36 adversely affected locations while 21% of respondents also identified beneficial aspects of recent landscape changes. Geophysical changes could be perceived differently by different stakeholders, illustrating the potential for competing demands on management responses. Impacts of rainfall-triggered erosion events were explored in case studies of damaged access infrastructure (e.g., roads, tracks, bridges). Adaptive responses resulted from formal or informal (park user-led) actions including re-routing, rebuilding, or abandonment of pre-existing infrastructure. Three widely transferable dimensions of public access management are identified: providing access that supports the core functions of protected areas; evaluating the impacts of both physical changes and human responses to them; and managing tensions between stakeholder preferences. Improved attention to the role of access is essential for effective climate change adaptation in parks and reserves.

Purpose This study examines how spatial factors shape the popularity of coffee shops in Chiang Mai, Thailand and a mid-sized Southeast Asian city with a rapidly expanding café culture. It investigates whether proximity to urban amenities and neighboring cafés influences customer engagement reflected in online reviews and ratings. Design/methodology/approach Data were collected in June 2025 using the Google Places API, identifying 2,468 coffee shops. Each entry was enriched with OpenStreetMap (OSM) data on nearby tourist attractions, universities, coworking spaces and transport hubs. A composite popularity score was constructed from ratings and review counts. Spatial clustering methods (DBSCAN, Moran's I and LISA) and correlation analysis were used to assess how popularity relates to locational characteristics. Findings Popular cafés cluster in central districts, especially tourism areas, while proximity to general amenities shows weak associations with popularity. Newer or lower-performing cafés benefit from locating near successful peers, suggesting visibility spillovers. In contrast, top performers often succeed outside clusters, indicating differentiation or destination appeal. Practical implications New entrants may enhance visibility by co-locating near established cafés, while mature businesses can sustain engagement through distinctive positioning in lower-density areas. Policymakers may support café districts such as Nimmanhaemin and the Old City while encouraging balanced dispersal. Originality/value This study integrates digital reputation metrics with geospatial data to advance understanding of spatial embeddedness in hospitality entrepreneurship. Combining Google Places and OSM demonstrates how spatial positioning and online visibility jointly shape outcomes.

Health burden assessment of legacy organochlorine compounds in Northeast China's marine fisheries: Carcinogenic potential and multi-disease risk characterization in coastal populations.

Background/Objectives: Penile squamous cell carcinoma (PSCC) is a rare malignancy with poor prognosis in advanced and recurrent disease, and therapeutic options remain limited. Increasing evidence suggests that the tumor immune microenvironment (TIME), including immune cell composition and spatial organization, plays a critical role in tumor progression and survival outcomes. This study aimed to characterize immune cell density and geospatial clustering patterns within the TIME of PSCC and to evaluate their associations with clinical outcomes. Methods: Multiplex immunofluorescence (mIF) was performed on tumor samples from 57 patients with PSCC using a panel of immune markers to identify lymphoid and myeloid cell populations. Immune cell densities were quantified within tumoral and stromal compartments. Spatial relationships among immune cells and between immune cells and tumor cells were analyzed using point pattern analysis. Survival outcomes, including overall survival (OS), recurrence-free survival (RFS), and cancer-specific survival (CSS), were assessed using Kaplan–Meier methods and Cox proportional hazards models, with analyses stratified by nodal and human papillomavirus (HPV) status. Results: Higher intratumoral and stromal densities of pro-immunogenic M1 macrophages were associated with improved OS. Increased densities of CD3+CD4+ helper T cells in both compartments were also associated with favorable survival outcomes. In contrast, close clustering of pro-tumorigenic M2 macrophages with tumor cells and with one another was associated with worse OS, RFS, and CSS. Bivariate clustering of helper T cells with tumor cells was associated with improved OS, including among patients with node-positive disease. Survival outcomes did not differ significantly by HPV status in patients with high helper T cell clustering. Conclusions: Immune cell density and spatial organization within the TIME are associated with survival outcomes in PSCC. Favorable patterns involving helper T cells and M1 macrophages correlate with improved survival, whereas clustering of M2 macrophages is associated with poorer outcomes, supporting the relevance of spatial immune profiling in this disease.

Maternal mortality is a pressing public health issue in Pakistan, and reducing its rate would enhance human well-being and public health. This study aims to identify spatial dependence or heterogeneity in maternal mortality rate (MMR) in Pakistan, assess the spatial spillover effects of sociocultural factors on MMR, and measure the spatial variation in the impact of sociocultural variables on MMR across districts in Pakistan. This study utilized data from Multiple Indicators Cluster Survey (MICS), conducted by UNICEF in 2017-18 and 2018-19. Global and local Moran's tests were employed to identify the spatial clusters and outliers in MMR. Spatial Durbin Model (SDM) was applied to measure the effects of sociocultural variables in own district (direct effects) and neighboring districts (indirect or spillover effects) on MMR, while Multiscale Geographically Weighted Regression (MGWR) assessed the extant of spatial sensitivity of sociocultural factors on MMR. The study found that districts were spatially dependent on their neighbouring districts up to 26%. LISA results showed a significant spatial cluster of high-high MMR in Baluchistan and KP, whereas low-low cluster of MMR was found in Punjab. Empirical findings confirmed that in terms of direct effects, MMR in own districts is influenced positively by multidimensional poverty index vulnerability (MPIV), traditional method of delivery (TDM), delivery by friends/relatives (DF/R), justification of wives in beating (JWB) while negatively affected by maternal education (ME) and first antenatal check-up in pregnancy (FACP). Concerning spatial spillover effect, the MMR in own districts is influenced positively by MPIV, TDM, DF/R, JWB of the neighboring districts and inversely influenced by ME and FACP of neighbouring districts. Furthermore, MPIV, TDM and DF/R were found to be highly sensitive, particularly in Baluchistan, while JWB was found to be more sensitive in KP and Baluchistan. The study concluded that districts in Pakistan exhibit significant spatial dependence regarding MMR, which are strongly influenced by socioeconomic and cultural variables. Prioritizing highly sensitive districts can significantly reduce maternal mortality by addressing advanced delivery methods, promoting maternal empowerment and autonomy, improving education, and enhancing the financial conditions of multidimensionally poor families, particularly in the districts of Baluchistan.

Abstract Background Stillbirths due to congenital syphilis are among the most devastating yet preventable outcomes of maternal infection. Vertical transmission remains widespread globally, especially in low-middle-income countries. In Mexico, rising maternal syphilis rates have been accompanied by an increase in CS-related stillbirths, yet the factors driving these outcomes remain unclear. Geographic context—distance to care and local environmental or social conditions—may influence clinical trajectories. Geospatial analysis identifies patterns of vulnerability and informs targeted interventions.Image 1.Gepgraphic and distance analysis of stillborn infants cases with syphilis infection from 2015 to 2022.A. Geographic distribution map of stillborn infants with syphilis from 2015 to 2022.B. Heat map of stillborn infants with syphilis from 2015 to 2022.C. Distance to the nearest OPC from the address of stillborn infants with syphilis from 2015 to 2022.D. Distance to HRMI from the address of stillborn infants with syphilis from 2015 to 2022Image 2.Gepgraphic and distance analysis of confirmed congenital syphilis cases from 2015 to 2022.A. Geographic distribution map of congenital syphilis cases from 2015 to 2022.B. Heat map of congenital syphilis cases from 2015 to 2022.C. Distance to the nearest OPC from the address of congenital syphilis cases from 2015 to 2022.D. Distance to HRMI from the address of congenital syphilis cases from 2015 to 2022 Methods We conducted a retrospective analysis of stillbirths associated with congenital syphilis (CS) at the Maternal and Pediatric Reference Hospital (HRMI) in Nuevo Leon, Mexico, from 2015-2022. We included data from stillborn infants (SbI) born to women with positive VDRL test at delivery, classified according to CDC criteria. Home addresses were geocoded using QGIS® (X/Y coordinates). Analyses included kernel density estimation, hierarchical clustering, and calculation of distances to the nearest outpatient clinic (OPC) and HRMI. Geographic areas were characterized using national marginalization index (NMIndx) to explore the social context surrounding each case.Image 3.Geographic distribution and relation with National Marginalization Index (NMIndx) categorization of syphilis realted stillborn infants cases from 2015 to 2022.National Marginalization Index: Mexican government construction, official data from 2022.Image 3.Geographic distribution and relation with National Marginalization Index (NMIndx) categorization of confirmed congenital syphilis cases from 2015 to 2022.National Marginalization Index: Mexican government construction, official data from 2022. Results Data from 162 cases of CS and 57 SbI were included. Cases were concentrated in 3 urban municipalities. For SbI, mean distance to the nearest OPC was 1.6 km and 18 km to HRMI. For CS, 1.6 km and 14 km, respectively. Most mothers (75%) of SbI and of newborns with CS (52%) lived >10 km from HRMI. Interestingly, 71% of mothers of SbI and 77% of CS cases lived within 2 km of an OPC, where diagnosis was often missed. NNI revealed a clustering pattern with statistical significance (p < 0.05) for both SbI and CS cases throughout 8 years. Characterization of NMIndx, showed that 88% SbI and 76% CS lived in high or very high marginalization areas. Conclusion Confirmed CS cases and SbI showed clear spatial clustering, primarily in areas with high levels of social marginalization. In contrast, low-marginalization zones reported few or no cases. These findings highlight possible geographic and social inequities driving adverse syphilis outcomes and underscore the need for targeted, community-level interventions in the most affected regions. Disclosures All Authors: No reported disclosures

BackgroundIn the United States, most malaria cases are related to travel to endemic areas and disproportionately affect specific communities. The burden of malaria and relationship with social determinants of health in developed countries is understudied. Our objective was to report the geographic distribution and any demographic associations of children with malaria in the DC metropolitan area.Figure 1Heat map of all malaria cases (n = 114)Figure 2Geographic distribution of Severe and Non-Severe Malaria cases in the Washington, D.C. metropolitan area. Severe malaria cases are primarily clustered in the eastern suburban regions outside of D.C. (notably corresponding to areas such as Prince George's County), whereas Non-Severe malaria cases exhibit higher concentrations in the northern suburb areas.MethodsWe conducted a retrospective study of children and adolescents up to 21 years of age with malaria admitted to a single center from 2018 to 2023. We extracted demographics, clinical, insurance, and travel history data. Using Empirical Bayes Kriging, we created a smoothed map predicting case densities; Choropleth maps and nonparametric test statistics were applied as appropriate.Figure 3Geographic distribution of insurance status in the Washington D.C. areaFigure 4Geographic distribution of travel status (recent immigration, U.S. based returning traveler or visitor to the U.S. from another country)ResultsWe identified 114 patients with malaria (65 severe, 49 non-severe). Patients with severe malaria were younger (median age 8.4 years [IQR 4.2–12.8] vs 12.4 [8.7–15.0], p=0.002) than those with non-severe malaria. Sex, race, insurance status did not differ between severe and non-severe malaria. Overall case density was highest in DC and adjacent Maryland (Figure 1). Severe cases were clustered in the Eastern suburbs of DC , whereas non-severe cases were concentrated in the North/Northwestern suburbs (Figure 2). Insurance status is mapped widely, with public insurance densest in Southeast DC and Maryland (Figure 3). Travel-status mapping demonstrated that recent immigrants, U.S.-based returning travelers, and visitors to the U.S. each exhibited unique spatial patterns, with recent immigrants dispersed all over the metropolitan area (Figure 4).ConclusionGeospatial mapping of pediatric malaria admissions due to international travel in the DC metropolitan area reveals distinct clusters and patterns. The future includes analysis of clinical severity within a social-determinants-of-health framework to pinpoint vulnerable communities and guide targeted public health interventions. These geospatial insights can direct culturally tailored outreach—such as pre-travel counseling in identified hotspots—and inform policies on equitable access to prophylaxis and care.DisclosuresAlexandra B. Yonts, MD, Pfizer: Grant/Research Support

Child malnutrition remains a major public health concern in low and middle-income countries, and Uttar Pradesh (UP), India, continues to experience some of the highest levels of stunting, wasting, and underweight among children under five. This study examines the determinants of child malnutrition and its association with morbidity using data from the National Family Health Survey (NFHS-5). Univariate and bivariate analyses, generalized Structural Equation Modeling (GSEM), and spatial analysis (Moran's I statistics and LISA cluster maps) were applied to assess both individual-level and district-level patterns. The NFHS-5 sample for Uttar Pradesh included 59,232 children under five, providing statistically robust estimates at both state and district levels. GSEM results indicated that maternal height, place of delivery, child age, caste, wealth index, and maternal education were significantly associated with stunting. Wasting was influenced by maternal height, birth order, child age, caste, wealth index, place of residence, and maternal education. Underweight was associated with maternal height, work status, child age, caste, wealth index, and maternal education. Malnutrition had a significant positive association with childhood morbidity (β = 0.032), indicating higher morbidity levels among malnourished children. Spatial analysis revealed clear geographic clustering of stunting, wasting, and underweight across districts, identifying several high-burden hotspots. These findings highlight the need for integrated, geographically targeted interventions addressing socio-economic inequalities, healthcare access, maternal factors, and environmental conditions to improve child nutrition and health outcomes in Uttar Pradesh.

Examining spatial variation and inequity in COVID-19 immunisation coverage in Aotearoa New Zealand: a nationwide geospatial study.

Current status and comparative risk assessment of microplastic pollution in surface water and sediment from the Black Sea coastline using geospatial analysis.

This paper provides an analysis of Ontario’s Permits to Take Water (PTTW) from 1960 to 2022, a period marked by profound shifts in environmental policy, technological advancement, economic development and growing ecological awareness. Through a detailed examination of data obtained from the Government of Ontario’s open data portal, this study investigates the distribution, issuance, and trends related to water permits across the province, exploring the evolution of water use and relevant policy insights. Our findings reveal significant sectoral and regional variations in permitted water use, influenced by diverse water users, various water use activities, and evolving permit policies. Over time, the province has seen an increase in the number, distribution, and sources of water permits, highlighting potential implications for water resource sustainability, policy and management. By employing geospatial and statistical analysis, this research identifies critical trends in water permit allocations offering insights into the complex interplay between policy, permitting, water use and sustainability. These findings offer insights and guidance for policymakers, including identifying high-demand areas for proactive water policy and management, improving permit data, categorization and reporting practices, and aligning water use monitoring with sustainability objectives. This paper aims to bridge the gap from permits to policy, presenting evidence-based policy insights and recommendations aimed at refining open data and the permitting process to enhance water resource management. The article also presents a framework that can be used to analyze open water permit data in other jurisdictions across Canada.

By mid-2024, >13 billion COVID-19 vaccine doses had been administered globally, with totals continuing to rise into 2025, yet persistent inequities remain in low- and middle-income countries (LMICs). We examined spatial determinants of COVID-19 vaccination uptake (proportion of eligible persons vaccinated) in Kenya by using the most recent nationally representative survey, the Kenya Demographic and Health Survey 2022. Our central contribution is the detection of seven spatially concentrated vulnerability clusters, complemented by using a Development Index (DI) and equity auditing to guide targeted action. We integrated socioeconomic, healthcare, environmental, and demographic measures at the Demographic and Health Survey cluster level; quantified spatial dependence (Moran's I; spatial lag models); identified socio-geographic clusters (K-means); estimated variable importance (random forest); and synthesized a DI. Equity was assessed by using the Erreygers Concentration Index (ECI) along two axes: wealth-based (poorest→richest) and immunization-linked (lowest→highest routine child immunization coverage). Our results reveal stark geographic disparities: vaccination rates range from 5.93% in Garissa to 46.02% in Nyeri, with urban clusters achieving significantly higher uptake. Key predictors include bank access (financial inclusion), household crowding, and environmental factors (nitrogen dioxide levels, precipitation). The DI correlated positively with uptake and the ECI indicated modest immunization-linked inequality and more pronounced wealth-related inequality. This study underscores the need for targeted interventions, including mobile vaccination units, financial inclusion programs (e.g. M-Pesa subsidies), and the integration of COVID-19 vaccines into routine immunization programs. As Kenya and many LMICs integrate COVID-19 vaccination into routine immunization, our spatial approach, combining DI, cluster detection, and equity metrics, provides an operational toolkit to prioritize underserved areas, inform the placement of service points/mobile teams, and monitor equity as programs transition from campaigns to routine delivery.

The accelerating climate crisis underscores the limitations of state-centric and technocratic approaches to environmental governance. Although Nature-Based Solutions (NbS) are increasingly promoted as strategies for climate adaptation and biodiversity conservation, prevailing frameworks often neglect Indigenous governance systems that have long sustained ecosystems through customary law, ecological knowledge, and cultural values. This article positions Indigenous governance as a pivotal dimension of NbS, emphasizing its capacity to integrate ecological stewardship with social justice and intergenerational equity. Drawing on the case of the Ammatoa Kajang community in South Sulawesi, Indonesia, the study illustrates how Indigenous forest classifications and customary norms safeguard ecological balance while reinforcing cultural resilience. Employing a mixed-methods approach, combining ethnography, geospatial analysis, and reflective narrative. The research demonstrates that Indigenous-led governance provides legitimacy and inclusivity frequently absent in state-driven conservation initiatives. The findings highlight the importance of legal pluralism and co-management models that recognize Indigenous rights, thereby advancing NbS that are ecologically robust, socially just, and culturally sustainable.

Geospatial analysis and predictive modeling of Hofmeister ions in relation to chronic kidney disease risk in South Sinai, Egypt

Mudflows are highly destructive geohazards that pose serious risks to human life, infrastructure, and ecosystems. The frequency of these events is growing due to climate change, deforestation, and natural triggers like heavy rainfall, earthquakes, and volcanic eruptions. This bibliometric study analyzes the global research trends regarding mudflow using data from the Scopus database between 1928 and 2024, assessing 393 publications through Biblioshiny, VOSviewer, and MapChart. Results show a steep increase in mudflow research, especially after 1965, which coincides with the time of technological development and increasing environmental awareness. The first two most prolific countries are the United States and China, followed by other European and Asian countries. Predictive modeling, Geographic Information Systems (GIS), and remote sensing have been highly instrumental in the study of mudflows because they provide better risk assessment, monitoring, and mitigation. The research highlights the growing role of global collaboration and advanced technologies like machine learning and geospatial analysis in improving mudflow prediction and disaster readiness. Researchers have developed complex simulation models for a better understanding of flow dynamics and improving early warning systems. These approaches enable more effective policy decisions and disaster management plans to be formed. The growing body of literature underscores the urgent need for truly interdisciplinary research that integrates geological, hydrological, climatic, and geospatial approaches to achieve more effective mitigation of mudflow hazards. This study highlights the need to combine modern technology with traditional methods to better prepare for future disasters. The findings, therefore, provide important insights for researchers, policymakers, and practitioners seeking to contribute to sustainable development and improved disaster preparedness in mudflow-prone areas.

Groundwater depletion in hard rock terrains necessitates scientifically validated approaches for identifying suitable aquifer replenishment sites. This study integrates geophysical resistivity surveys with geospatial multi criteria analysis to delineate recharge potential zones in the Kiliyar Sub-Basin of the Palar River Basin, Tamil Nadu. Seven thematic layers, geomorphology, land use/land cover (LULC), lineament density, drainage density, slope, resistivity layer properties, and depth were generated using Landsat ETM+, SRTM DEM, and 32 Vertical Electrical Sounding (VES) points. These parameters are converted to raster format and assigned rank and weights then computed from the Normalized Weight Method (NWM). All the layers have been integrated to generate final layer to assess the artificial recharge structures in the Kiliyar Sub-Basin. The final output zones are classified into five suitability categories: least suitable (5.66%), poorly suitable (22.91%), moderately suitable (29.25%), highly suitable (30.19%), and very highly suitable (12%), and the resulting recharge zonation map groups the basin into the same five classes from least to very highly suitable. The study area boundaries and drainage framework provide important spatial context for interpreting recharge behavior. This aquifer replenishment zone map will be useful for extraction and management of groundwater in the study area. Keywords: Aquifer Replenishment, GIS, Resistivity Survey, Multi-criteria Analysis, Kiliyar Sub-Basin, Remote Sensing

Geospatial and geostatistical analysis of land fragmentation and parcel shape indicators for sustainable farm structure in land consolidation