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BACKGROUND AND HYPOTHESIS: Serious mental illness (SMI) may compromise diabetes self-management. This study assessed the association between SMI and glycemic control, and explored sociodemographic predictors and geographic clustering of this outcome among patients with and without SMI. STUDY DESIGN: We used electronic health record data for adult primary care patients with diabetes from 2 San Francisco health care delivery systems. The primary outcome was poor glycemic control (hemoglobin A1c >9.0%), which was modeled on SMI diagnosis status and sociodemographics. Geospatial analyses examined hotspots of poor glycemic control and neighborhood characteristics. STUDY RESULTS: The study included 11 694 participants with diabetes, 21% with comorbid SMI, of whom 22% had a schizophrenia spectrum or bipolar disorder. Median age was 62 years; 52% were female and 79% were Asian, Black, or Hispanic. In adjusted models, having schizophrenia spectrum disorder or bipolar disorder was associated with greater risk for poor glycemic control (vs participants without SMI, adjusted relative risk [aRR] = 1.24; 95% confidence interval, 1.02, 1.49), but having broadly defined SMI was not. People with and without SMI had similar sociodemographic correlates of poor glycemic control including younger versus older age, Hispanic versus non-Hispanic White race/ethnicity, and English versus Chinese language preference. Hotspots for poor glycemic control were found in neighborhoods with more lower-income, Hispanic, and Black residents. CONCLUSIONS: Poor diabetes control was significantly related to having a schizophrenia spectrum or bipolar disorder, and to sociodemographic factors and neighborhood. Community-based mental health clinics in hotspots could be targets for implementation of diabetes management services.

In an effort to mitigate anthropogenic climate impacts the U.S. has established ambitious Nationally Determined Contribution (NDC) targets, aiming to reduce greenhouse gas emissions by 50% before 2030 and achieving net-zero emissions by 2050. Enhanced rock weathering (ERW)—the artificial enhancement of chemical weathering of rocks to accelerate atmospheric CO2 capture—is now widely seen as a potentially promising carbon dioxide removal (CDR) strategy that could help to achieve U.S. climate goals. Grinding rocks to smaller particle size, which can help to facilitate more rapid and efficient CO2 removal, is the most energy-demanding and cost-intensive step in the ERW life cycle. As a result, accurate life cycle analysis of ERW requires regional constraints on the factors influencing the energetic and economic demands of feedstock grinding for ERW. Here, we perform a state-level geospatial analysis to quantify how carbon footprints, costs, and energy demands vary among regions of the coterminous U.S. in relation to particle size and regional electricity mix. We find that CO2 emissions from the grinding process are regionally variable but relatively small compared to the CDR potential of ERW, with national averages ranging between ~5–35 kgCO2 trock−1 for modal particle sizes between ~10–100  μm. The energy cost for feedstock grinding also varies regionally but is relatively small, with national average costs for grinding of roughly 0.95–5.81 $ trock−1 using grid mix power and 1.35–8.26 $ trock−1 (levelized) for solar PV for the same particle size range. Overall energy requirements for grinding are also modest, with the demand for grinding 1 Gt of feedstock representing less than 2% of annual national electricity supply. In addition, both cost and overall energy demand are projected to decline over time. These results suggest that incorporating feedstock grinding into ERW deployment at scale in the coterminous U.S. should generally have only modest impacts on lifecycle emissions, cost-effectiveness, and energy efficiency. [ABSTRACT FROM AUTHOR]

Background: Early marriage and motherhood have long been prevalent in India, with 44.5% of women aged 20–24 reporting marriage before 18 in NFHS 3 (2005–2006), dropping to 26.8% in NFHS 4 (2015–2016). Early motherhood has also seen a significant decline, with first births by age 18 decreasing from 34.3% in NFHS I to 8.2% in NFHS V. Despite these improvements, significant regional disparities persist due to social, normative, and legal factors. This study investigates the spatial heterogeneity of early marriage and motherhood across Indian districts, offering a multilevel analysis that reveals critical local variations often obscured at broader levels. Understanding these patterns is crucial for targeted policy interventions and addressing the root causes of early marriage and motherhood. Methods: Utilizing data from the fifth round of National Family Health Survey, this study employs multilevel logistic regression and geospatial analysis to assess the determinants and spatial distribution of early marriage and early motherhood among ever-married women in India. The analysis incorporates individual, household, and community-level variables, complemented by spatial analysis techniques, including Empirical Bayes Bivariate Moran's I values and LISA cluster maps, to identify regional patterns and hotspots. Results: This study revealed that educational attainment emerged as a critical determinant, with uneducated women significantly more likely to marry early. Socioeconomic factors, such as poverty and limited mass media exposure, also heightened the risk of early marriage and motherhood. Caste and religion were significantly associated with these events, with marginalized groups facing higher prevalence. Spatial analysis revealed significant geographic disparities, with central and eastern regions showing higher concentrations of early marriage and motherhood. District-level characteristics and the influence of neighboring districts were also significant, highlighting the importance of localized interventions. Conclusions: The findings underscore the critical role of education, economic empowerment, and media literacy in mitigating early marriage and motherhood risks. The study calls for multi-sectoral interventions in geographical hotspots to break the cycle of early family formation and promote reproductive health. Policies enhancing educational opportunities, addressing economic disadvantages, and considering district-specific factors are essential. Comprehensive strategies are necessary to empower women, foster reproductive health, and address the multifaceted nature of early marriage and motherhood in India. [ABSTRACT FROM AUTHOR]

ObjectiveMethodsResultsConclusionWork in seafood harvesting and processing has been associated with a range of occupational hazards and associated health problems. However, limited information exists regarding the occupational health risks among workers engaged in seaweed handling.A cross-sectional study was conducted to map seaweed co-operatives (n = 45), assess working practices, and identify common work-related symptoms, occupational hazards, and interventions in seaweed farming sites (n = 24), across three regions of Zanzibar, Tanzania.Seaweed farming in Zanzibar primarily involves the cultivation of Eucheuma Spinosum, with the majority of workers being women. Common work-related symptoms identified include musculoskeletal symptoms, mainly affecting the wrist (87%), back (84%) and lower limbs (73%). Furthermore, a significant proportion of workers were reported to have skin problems (80%) as well as heat and asthma-related symptoms (51%). Walk-through hazard evaluations of work sites identified a range of occupational hazards, with ergonomic (58–68%), biological (50–63%) and chemical (55–65%) hazards obtaining higher scores across the three regions. Geospatial analysis revealed regional variations in occupational hazards and work-related symptom prevalence, with Unguja South having a statistically significant (p < .05) higher mean prevalence of skin symptoms (95%) and Mjini Magharibi region reporting a higher prevalence of lower limb pain (100%), back pain (100%), and heat-related symptoms (90%). Despite the high hazard scores, interventions were generally lacking across all regions.Occupational health hazards and adverse health outcomes vary across seaweed farming and processing regions. Tailored interventions will be key to improving workplace health and safety of workers engaged in this sector. [ABSTRACT FROM AUTHOR]

Soil erosion is one of the most significant problems in global environmental development. Assigning, analyzing, and quantifying the main physical characteristics of drainage basins are powerful keys in identifying regions where there is a higher risk of soil erosion and where prompt mitigation actions are needed. Drainage basins and their drainage systems are ideally defined using the analysis morphometric parameters and their quantitative description. The present study aims to analyze morphometric parameters to prioritize drainage basins that are prone to erosion. Topographic sheets and remotely sensed digital elevation model (DEM) datasets have been prepared and analyzed using geospatial techniques to delineate drainage basins of different sizes and extract different ordered drainage systems. Based on the analysis of morphometric parameters, the Rabigh area was divided into 12 drainage basins, which significantly contribute to determining soil erosion priority levels. The present study selected and applied the most effective morphometric parameters to rank and prioritize the drainage basins of the study area after considering the crucial quantitative characteristics, such as linear, relief, and areal aspects. For each single basin, the compound factor was assigned from several morphometric parameters and applied to rank the Rabigh area. The results confirm that Basins 1, 4, 11, and 12 require a high level of soil erosion priority over an area of 2107 km2; however, Basins 3, 8, 9, and 10 have little degradation and a low level of soil erosion priority. Therefore, in the regions where high soil erosion is a factor, mitigation techniques such as terracing, filter strips, contouring, and other effective and useful structural and non-structural methods should be applied. [ABSTRACT FROM AUTHOR]

Large Language Models (LLMs) such as GPT, BART, and Gemini stand at the forefront of Generative Artificial Intelligence, showcasing remarkable prowess in natural language comprehension and task execution. This paper proposes a novel framework developed on the foundation of Llama 2, aiming to bridge the gap between natural language queries and executable code for geospatial analyses within the PyQGIS environment. It empowers non-expert users to leverage GIS technology without requiring deep knowledge of geospatial programming or tools. Through cutting-edge Natural Language Processing (NLP) techniques, including tailored entity recognition and ontology mapping, the framework accurately interprets user intents and translates them into specific GIS operations. Integration of geospatial ontologies enriches semantic comprehension, ensuring precise alignment between user descriptions, geospatial datasets, and geospatial analysis tasks. A code generation module empowered by Llama 2 converts these interpretations into PyQGIS scripts, enabling the execution of geospatial analysis and results visualization. Rigorous testing across a spectrum of geospatial analysis tasks, with incremental complexity, evaluates the framework and the performance of such a system, with LLM at its core. The proposed system demonstrates proficiency in handling various geometries, spatial relationships, and attribute queries, enabling accurate and efficient analysis of spatial datasets. Moreover, it offers robust error-handling mechanisms and supports tasks related to map styling, visualization, and data manipulation. However, it has some limitations, such as occasional struggles with ambiguous attribute names and aliases, which leads to potential inaccuracies in the filtering and retrieval of features. Despite these limitations, the system presents a promising solution for applications integrating LLMs into GIS and offers a flexible and user-friendly approach to geospatial analysis. [ABSTRACT FROM AUTHOR]

HIV-1 drug resistance (HIVDR) impedes treatment and control of HIV-1, especially in high-prevalence settings such as KwaZulu-Natal (KZN) province, South Africa. This study merged routine HIV-1 genotypic resistance test (GRT) data with Geographic Information Systems coordinates to assess patterns and geographic distribution of HIVDR in KZN, among ART-experienced adults with virological failure. We curated 3133 GRT records generated between 1 January 2018 and 30 June 2022, which includes the early phase of dolutegravir (DTG) rollout, of which 2735 (87.30%) had HIVDR. Of the 2735, major protease, nucleoside, and non-nucleoside reverse transcriptase inhibitor mutations were detected in 41.24%, 84.97% and 88.08% of GRTs, respectively. Additional genotyping of HIV-1 integrase for 41/3133 (1.31%) GRTs showed that 17/41 (41.46%) had integrase strand transfer inhibitor resistance. Notably, of 26 patients on DTG with integrase genotyping, 9 (34.62%) had DTG-associated resistance mutations. Dual- or triple-class resistance was observed in four of every five GRTs. The odds of HIVDR increased significantly with age, with ≥60 years having 5 times higher odds of HIVDR compared to 18–29 years (p = 0.001). We identified geospatial differences in the burden of HIVDR, providing proof of concept that this could be used for data-driven public health decision making. Ongoing real-time HIVDR surveillance is essential for evaluating the outcomes of the updated South African HIV treatment programme. [ABSTRACT FROM AUTHOR]

Urban trees support vital ecological functions and help with the mitigation of and adaption to climate change. Yet, their monitoring and management require significant public resources. remote sensing could facilitate these tasks. Recent hyperspectral satellite programs such as PRISMA have enabled more advanced remote sensing applications, such as species classification. However, PRISMA data's spatial resolution (30 m) could limit its utility in urban areas. Improving hyperspectral data resolution with pansharpening using the PRISMA coregistered panchromatic band (spatial resolution of 5 m) could solve this problem. This study addresses the need to improve hyperspectral data resolution and tests the pansharpening method by classifying exemplative urban tree species in Naples (Italy) using a convolutional neural network and a ground truths dataset, with the aim of comparing results from the original 30 m data to data refined to a 5 m resolution. An evaluation of accuracy metrics shows that pansharpening improves classification quality in dense urban areas with complex topography. In fact, pansharpened data led to significantly higher accuracy for all the examined species. Specifically, the Pinus pinea and Tilia x europaea classes showed an increase of 10% to 20% in their F1 scores. Pansharpening is seen as a practical solution to enhance PRISMA data usability in urban environments. [ABSTRACT FROM AUTHOR]

Coastal risk assessment is crucial for coastal management and decision making, especially in areas already experiencing the negative impacts of climate change. This study aims to investigate the coastal vulnerability due to climate change and human activities in an area west of the Limassol district's coastline, in Cyprus, on which there have been limited studies. Furthermore, an analysis is conducted utilising the Coastal Vulnerability Index (CVI) by exploiting eight key parameters: land cover, coastal slope, shoreline erosion rates, tidal range, significant wave height, coastal elevation, sea-level rise, and coastal geomorphology. These parameters were assessed utilising remote sensing (RS) data and Geographical Information Systems (GISs) along a 36.1 km stretch of coastline. The results exhibited varying risk levels of coastal vulnerability, mainly highlighting a coastal area where the Kouris River estuary is highly vulnerable. The study underscores the need for targeted coastal management strategies to address the risks associated with coastal erosion. Additionally, the CVI developed in this study can be exploited as a tool for decision makers, empowering them to prioritise areas for intervention and bolster the resilience of coastal areas in the face of environmental changes. [ABSTRACT FROM AUTHOR]

Copyright of Afyon Kocatepe University Journal of Science & Engineering / Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi is the property of Afyon Kocatepe University, Faculty of Science & Literature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The study utilizes a geospatial approach to analyze the risk of motorcycle accidents in Khon Kaen Municipality, Thailand. We focus on identifying high-crash areas involving motorcyclists and investigating factors contributing to the severity of accidents and potential survival rates. The analysis is framed within the "Chain of Survival" framework, specifically focusing on how the location of ambulance stations affects the Emergency Medical Service (EMS) response times. Additionally, we explore various traffic management strategies, with an emphasis on their effectiveness in reducing nighttime accidents. The primary goal of this research is to gain a comprehensive understanding of the spatial distribution of motorcycle accidents and to identify key factors influencing their outcomes. Our findings reveal that motorcycle accidents constitute the highest number of traffic accidents in the area, with a significant proportion occurring in the southwest of Khon Kaen Municipality during late night hours. Given the importance of early intervention within the EMS, we suggest locating EMS stations close to areas with a high incidence of accidents concerning the effective response time, as recommended at two specific points. However, due to limitations in the number of EMS teams available, these recommended points may experience delays in response times, particularly during late-night hours when traffic accidents peak. Ultimately, the insights gained from this study will inform data-driven recommendations aimed at optimizing both emergency response systems and traffic management strategies to enhance road safety in Khon Kaen Municipality. [ABSTRACT FROM AUTHOR]

Recent advancements in deep learning have significantly improved the performance of remote sensing scene classification, a critical task in remote sensing applications. This study presents a new aerial scene classification model, the Multi-Scale Swin–CNN Aerial Classifier (MSCAC), which employs the Swin Transformer, an advanced architecture that has demonstrated exceptional performance in a range of computer vision applications. The Swin Transformer leverages shifted window mechanisms to efficiently model long-range dependencies and local features in images, making it particularly suitable for the complex and varied textures in aerial imagery. The model is designed to capture intricate spatial hierarchies and diverse scene characteristics at multiple scales. A framework is developed that integrates the Swin Transformer with a multi-scale strategy, enabling the extraction of robust features from aerial images of different resolutions and contexts. This approach allows the model to effectively learn from both global structures and fine-grained details, which is crucial for accurate scene classification. The model's performance is evaluated on several benchmark datasets, including UC-Merced, WHU-RS19, RSSCN7, and AID, where it demonstrates a superior or comparable accuracy to state-of-the-art models. The MSCAC model's adaptability to varying amounts of training data and its ability to improve with increased data make it a promising tool for real-world remote sensing applications. This study highlights the potential of integrating advanced deep-learning architectures like the Swin Transformer into aerial scene classification, paving the way for more sophisticated and accurate remote sensing systems. The findings suggest that the proposed model has significant potential for various remote sensing applications, including land cover mapping, urban planning, and environmental monitoring. [ABSTRACT FROM AUTHOR]

Banana (Musa sp.) is one of the world's most planted and consumed crops. Analysis of plantations using a geospatial perspective is growing in Costa Rica, and it can be used to optimize environmental analysis. The aim of this study was to propose a methodology to identify areas prone to water accumulation to quantify nitrate concentrations using geospatial modeling techniques in a 40 ha section of a banana plantation located in Siquirres, Limón, Costa Rica. A total of five geomorphometric variables (Slope, Slope Length factor (LS factor), Terrain Ruggedness Index (TRI), Topographic Wetness Index (TWI), and Flow Accumulation) were selected in the geospatial model. A 9 cm resolution digital elevation model (DEM) derived from unmanned aerial vehicles (UAVs) was employed to calculate geomorphometric variables. ArcGIS 10.6 and SAGA GIS 7.8.2 software were used in the data integration and analysis. The results showed that Slope and Topographic Wetness Index (TWI) are the geomorphometric parameters that better explained the areas prone to water accumulation and indicated which drainage channels are proper areas to sample nitrate values. The average nitrate concentration in high-probability areas was 8.73 ± 1.53 mg/L, while in low-probability areas, it was 11.28 ± 2.49 mg/L. Despite these differences, statistical analysis revealed no significant difference in nitrate concentrations between high- and low-probability areas. The method proposed here allows us to obtain reliable results in banana fields worldwide. [ABSTRACT FROM AUTHOR]

Long-term monitoring studies on the transition of different land cover units to barren areas are crucial to gain a better understanding of the potential challenges and threats that land surface ecosystems face. This study utilized the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover products (MCD12C1) to conduct geospatial analysis based on the maximum extent (MaxE) concept, to assess the spatiotemporal changes in barren areas from 2001 to 2022, at global and continental scales. The MaxE area includes all the pixels across the entire period of observations where the barren land cover class was at least once present. The relative expansion or reduction of the barren areas can be directly assessed with MaxE, as any annual change observed in the barren distribution is comparable over the entire dataset. The global barren areas without any land change (UA) during this period were equivalent to 12.8% (18,875,284 km2) of the global land surface area. Interannual land cover changes to barren areas occurred in an additional area of 3,438,959 km2 (2.3% of the global area). Globally, barren areas show a gradual reduction from 2001 (91.1% of MaxE) to 2012 (86.8%), followed by annual fluctuations until 2022 (88.1%). These areas were mainly interchanging between open shrublands and grasslands. A relatively high transition between barren areas and permanent snow and ice is found in Europe and North America. The results show a 3.7% decrease in global barren areas from 2001 to 2022. Areas that are predominantly not barren account for 30.6% of the transitional areas (TAs), meaning that these areas experienced short-term or very recent transitions from other land cover classes to barren. Emerging barren areas hotspots were mainly found in the Mangystau region (Kazakhstan), Tibetan plateau, northern Greenland, and the Atlas Mountains (Morocco, Tunisia). [ABSTRACT FROM AUTHOR]

Will climate change affect how armed actors behave toward civilians? Scholars analyzed different links between environmental variability and war, but research on links between the former and violence against civilians is very limited. Focusing on the Sahara Desert transition zone, which stretches across the entire African continent, we argue that climate harshness raises the willingness of actors to engage in competitive violence over resources. An underlying condition is this violence happens in times of greater environmental security, which provides opportunity for actors to engage in violence. We test this argument using statistical analysis of a newly released climate-conflict geospatial dataset and find support for this theoretical expectation. The conclusion outlines both research and policy implications. [ABSTRACT FROM AUTHOR]

Over the past few decades, cities have experienced increased floods affecting property and threatening human life as a result of a warming planet. There is still an incomplete understanding of the flood risk patterns in urban communities with different socioeconomic characteristics. In this study, we produced separate flood exposure and vulnerability indices based on relevant factors, then combined them as a risk index for Houston, Texas and Charleston, West Virginia. We applied statistical methods to extract the most significant social vulnerability factors in each study area. Finally, we mapped significant hot spots or clusters of high flood risk and compared results to socioeconomically disadvantaged populations. Based on the results, high-risk or 1%-annual-chance floodplains cover 23% of the Houston and 7% of Charleston study areas. Within these floodplains, 13% of the total developed land in Houston and 9% in Charleston are situated. In the event of a 1%-annual-chance flood, an estimated 5% of the total population in Houston and 6% in Charleston may require evacuation. Statistically significant flood risk clusters could only be identified in Houston. The implications from this work help to provide an analysis framework for larger urban areas while offering suggestions for its improvement in smaller populated areas. [ABSTRACT FROM AUTHOR]

Floating offshore wind (FOW) is rapidly gaining interest due to its large potential. In this regard, it is of special interest to determine the best locations for its installation. One of the main aspects when evaluating the feasibility of a project is the levelised cost of energy (LCOE), but there are many variables to consider when calculating it for FOW, and plenty of them are hard to find when the scope is all the suitable areas worldwide. This paper presents the calculation and analysis of the global LCOE with particular focus on the best countries and territories from an economic point of view, considering four types of platforms: semi-submersible, barge, spar, and tension leg platform (TLP). The model takes into account, on the one hand, wind data, average significant wave height, and distance to shore for an accurate calculation of delivered energy to the onshore substation and, on the other hand, bathymetry, distances, and existing data from projects to find appropriate functions for each cost with regression models (e.g., manufacturing, installation, operation and maintenance (O&M), and decommissioning costs). Its results can be used to assess the potential areas around the world and identify the countries and territories with the greatest opportunities regarding FOW. The lowest LCOE values, i.e., the optimal results, correspond to areas where wind resources are more abundant and the main variables of the site affecting the costs (water depth, average significant wave height, distance to shore, and distance to port) are as low as possible. These areas include the border between Venezuela and Colombia, the Canary Islands, Peru, the border between Western Sahara and Mauritania, Egypt, and the southernmost part of Argentina, with LCOEs around 90 €/MWh. Moreover, there are many areas in the range of 100–130 €/MWh. [ABSTRACT FROM AUTHOR]

Copyright of Naturaliste Canadien is the property of La Societe Provancher and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study addresses energy poverty, a situation where households are unable to afford the minimum energy services required to meet their basic needs, a problem with profound implications for the well-being of vulnerable populations. To identify the most affected areas, a geospatial analysis of energy poverty distribution in the Valencian Community (Spain) was conducted using a correlation study between the global vulnerability index and the energy poverty index, calculated from residential energy certificates. The results highlight areas of significant social, economic, demographic, and residential vulnerability, as well as regions with very high or very low energy consumption. Specifically, regions with high energy poverty and vulnerability indices were identified, enabling targeted interventions. The study concludes that interventions in these identified areas are essential to mitigate the adverse effects of energy poverty and improve living conditions for affected populations. This research offers a novel methodology for mapping regional energy poverty, surpassing previous studies in precision for identifying and addressing vulnerable areas. [ABSTRACT FROM AUTHOR]

Globally, road crashes impose massive burdens, and understanding the complex factors influencing crash patterns requires advanced analytical methods. This study reviews 20 years of published literature on geospatial crash analysis to chart key innovations shaping this critical field. The analysis reveals a progression from fundamental mapping approaches towards integrative predictive modelling and dynamic risk monitoring leveraging diverse data sources. While crash records and road networks remain core datasets, aerial imagery, social media, weather, infrastructure attributes, and traffic data have become increasingly incorporated. Techniques have evolved from hotspot analysis to sophisticated machine learning algorithms automating crash prediction and factor analysis. Research objectives now emphasize not just pattern identification but predictive risk modelling, and there is an increased focus on prevention through improved emergency response, infrastructure enhancements, and targeted countermeasures. Interactive 3D visualizations and virtual reality applications are enhancing geospatial communication and decision-making. As geospatial innovations and data integration accelerate, this continuously advancing field holds tremendous potential to guide proactive evidence-based road safety planning. However, validating analysis approaches and assessing geographic transferability remain critical research needs. By synthesizing two decades of developments, this study provides key perspectives to harness geospatial technology innovations and unlock new frontiers in data-driven road crash prevention worldwide. [ABSTRACT FROM AUTHOR]

Land degradation from gully erosion poses a significant threat to the Erer watershed in Eastern Ethiopia, particularly due to agricultural activities and resource exploitation. Identifying erosion-prone areas and underlying factors using advanced machine learning algorithms (MLAs) and geospatial analysis is crucial for addressing this problem and prioritizing adaptive and mitigating strategies. However, previous studies have not leveraged machine learning (ML) and GIS-based approaches to generate susceptibility maps identifying these areas and conditioning factors, hindering sustainable watershed management solutions. This study aimed to predict gully erosion susceptibility (GES) and identify underlying areas and factors in the Erer watershed. Four ML models, namely, XGBoost, random forest (RF), support vector machine (SVM), and artificial neural network (ANN), were integrated with geospatial analysis using 22 geoenvironmental predictors and 1,200 inventory points (70% used for training and 30% for testing). Model performance and robustness were validated through the area under the curve (AUC), accuracy, precision, sensitivity, specificity, kappa coefficient, F1 score, and logarithmic loss. The relative slope position is most influential, with 100% importance in SVM and RF and 95% importance in XGBoost, while annual rainfall (AR) dominated ANN (100% importance). Notably, XGBoost demonstrated robustness and superior prediction/mapping, achieving an AUC of 0.97, 91% accuracy, 92% precision, and 81% kappa while maintaining a low logloss (0.0394). However, SVM excelled in classifying gully resistant/susceptible areas (97% sensitivity, 98% specificity, and 91% F1 score). The ANN model predicted the most areas with very high gully susceptibility (13.74%), followed by the SVM (11.69%), XGBoost (10.65%), and RF (7.85%) models, while XGBoost identified the most areas with very low susceptibility (70.19%). The ensemble technique was employed to further enhance GES modeling, and it outperformed the individual models, achieving an AUC of 0.99, 93.5% accuracy, 92.5% precision, 97.5% sensitivity, 95.4% specificity, 85.8% kappa, and 94.9% F1 score. This technique also classified the GES of the watershed as 36.48% very low, 26.51% low, 16.24% moderate, 11.55% high, and 9.22% very high. Furthermore, district-level analyses revealed the most susceptible areas, including the Babile, Fedis, Harar, and Meyumuluke districts, with high GES areas of 32.4%, 21.3%, 14.3%, and 13.6%, respectively. This study offers robust and flexible ML models with comprehensive validation metrics to enhance GES modeling and identify gully prone areas and factors, thereby supporting decision-making for sustainable watershed conservation and land degradation prevention. [ABSTRACT FROM AUTHOR]

Monitoring changes in coral reef ecosystems for their conservation and management is important as they are frequently subject to various natural and anthropogenic threats. However, in Sri Lanka, the attention in this regard is to be further concerned for their well‐being. Therefore, this study focused on the selected shallow water (<5 m) coral reefs as Pigeon Island (PIR), Adukkuparu (AR), Salli Beach (SBR), Kayankerni (KR), and Passikuda (PR) on the Eastern coast of Sri Lanka to estimate their changes over 2020, 2021, and 2022. The remote sensing demarcations with the composition of reef ecosystems were done through the analysis of the Allen Coral Atlas data followed by field surveys. Higher accuracy levels for corals were obtained for each site due to field verifying all individual polygons and omitting unmatched polygons of the initial maps from the Allen Coral Atlas. Then, the in‐situ reef surveys were conducted over randomly selected permanent transects at each site. From 2020 to 2022, the live coral percentages have increased in PIR, AR, and SBR while reduced in KR and PR. Dead corals have facilitated the overgrowth of macroalgae. The combined area of live coral on all five reefs has slightly gone up. This increase is primarily due to the rise in live coral cover in AR and SBR. Also, prominent increments of coral diversity have resulted from those sites. Therefore, it is suggested that AR and SBR be designated as Marine Protected Areas to safeguard the newly emerging live corals. [ABSTRACT FROM AUTHOR]

Pediatric scald burns account for 12% of all U.S. burn center admissions and are the most common type of burn in children. We hypothesized that geospatial analysis of burn registry data could identify specific geographic areas and risk factors to focus injury prevention efforts. The burn registry of a U.S. regional burn center was used to retrospectively identify pediatric scald burn patients ages 0-17, from January 2018 to June 2023. Geocoding of patient home addresses with census tract data was performed. Area Deprivation Index (ADI) was assigned to patients at the census block group level. Burn incident hot spot analysis to identify statistically significant burn incident clusters was done using the Getis Ord Gi∗ statistic. There were 950 pediatric scald burn patients meeting study criteria. The cohort was 52% male and 36% White, with median age of 3 y and median total body surface area of 1.5%; 23.8% required hospital admission. On multivariable logistic regression, increased child poverty levels (P = 0.004) and children living in single-parent households (P = 0.009) were associated with increased scald burn incidence. Geospatial analysis identified burn hot spots, which were associated with higher ADI (P < 0.001). Black patients were more likely to undergo admission compared to White patients. Geospatial analysis of burn registry data identified geographic areas at high risk of pediatric scald burn. ADI, poverty, and children in single-parent households were the greatest predictors of injury. Addressing these inequalities requires targeted injury prevention education, enhanced outpatient support systems and more robust community resources. [ABSTRACT FROM AUTHOR]

Landfill siting is a major problem in solid waste management. Many aspects should be considered when selecting an appropriate site for a landfill in any particular region. In the current research, seventeen criteria were considered, including rivers, water bodies (lakes), geology, slope, elevation, power lines, groundwater depth, major district roads, archaeological sites, urban centers, infrastructure, villages, pipelines, quarrying, forests, aspect (wind), and agricultural areas. Several random open dump waste disposal sites are dispersed throughout the Ranya District, Iraq. This research employed the integration of a Geographic Information System (GIS) and Analytical Hierarchy Process (AHP) to identify the optimal sites for the establishment of sanitary landfills. The seventeen criteria were mapped and assigned subcriteria within GIS software. Subsequently, the normalized weights from the AHP were identified for each measure to generate the final Land Suitability Index (LSI) map for the site. Consequently, approximately 0.34% of the entire study area, equivalent to roughly 2,396,000 square meters, is estimated to be suitable for locating a landfill. Additionally, the suitable sites were categorized into moderately suitable, suitable, and mostly suitable. The normalized weight analysis revealed that groundwater depth significantly influences the selection of the best landfill site [ABSTRACT FROM AUTHOR]

The aim of the study was to assess the distribution and potential of tourism resources in Kyrgyzstan within the context of the Belt and Road Initiative (BRI). Utilizing geospatial analysis, nearest neighbor analysis (NNA), and geographic concentration index, the study reveals the concentration of various types of tourism resources across Kyrgyzstan's regions. Natural resources such as geological landscapes and water bodies are found to be dispersed, with significant concentrations in Chuy and Issyk-Kul regions. Cultural and religious landscapes, on the other hand, are highly clustered in Bishkek and Osh. The study identifies key strengths, such as Kyrgyzstan's rich and diverse tourism resources, but also highlights weaknesses including infrastructure limitations and language barriers. Opportunities arise from the deepening China-Kyrgyzstan relations under the BRI, while threats include potential environmental pressures and over-commercialization. The findings provide a comprehensive understanding of Kyrgyzstan's tourism landscape, offering actionable insights for leveraging the BRI to enhance tourism development in the country. [ABSTRACT FROM AUTHOR]

This paper aims to investigate whether online private supplementary education, also known as shadow education, can alleviate educational inequality and what types of mechanisms can help alleviate it. We investigate this using an online learning platform dataset (3,603 anonymous students from China) with additional data from multiple sources and employ geospatial analyses to measure students' socioeconomic, regional and rural/urban inequalities. We find that taking part in online education narrows the performance gap in mathematics between privileged and unprivileged students in terms of school status and regional disparity in China. Theoretically, two micro-level mechanisms explain the alleviating differences: 1) equal access mechanism: students from lower city tiers and low-status schools show greater score improvement when having equal access to online education; 2) equal quality mechanism: students from rural regions improve their in-class rankings more substantially if they receive equal quality online education with the same tutoring and learning environment alongside urban students. This study comprehensively looks at the different effects of two mechanisms of online education—equal access and equal quality—for alleviating various types of inequality. Thus we speak to both educational inequality and digital inequality theory, finding that equal access to online education is not enough for rural students, as they also need access to classes of equal educational quality with their urban counterparts. [ABSTRACT FROM AUTHOR]

In recent decades, the cultivation of avocados (Persea americana Mill) has expanded throughout the Iberian Peninsula, with most of the production occurring on the Southern Atlantic and Mediterranean Coast, as well as in the Canary Islands. This expansion is due to high demand and high prices, which have made the crop very attractive. However, climatic suitability criteria have not always been followed, putting sustainability at risk. Avocados originate from tropical and subtropical areas and have very specific climatic requirements that must be met to ensure good production. This study analyzed the key climatic variables, including winter cold damage, pollen viability and flowering and fruit set temperatures. Using daily climate data from 1975 to 2022, advanced spatial analysis techniques were applied to produce suitability maps. The results indicate that the expansion of the crop is possible in certain areas of Southern Andalusia but not in the rest of the peninsula. Variables associated with low temperatures, such as cold damage (temperatures below 0 °C in winter) and conditions unfavorable for fruit set (minimum daily temperatures below 10 °C in spring), have been identified as the most restrictive factors. Specifically, the number of days with minimum temperatures above 10 °C in March and April appears to be the most limiting climatic factor for avocado cultivation in the Iberian Peninsula and the Balearic Islands. Andalusia and the Valencian Community have been identified as the most climatically suitable regions for avocado cultivation, with approximately 1,500,000 hectares recognized as potentially suitable for avocado production. This work provides valuable insights into the potential for sustainable agricultural intensification, the sustainability of agricultural decision-making and resilience to climate change. [ABSTRACT FROM AUTHOR]

Background: Cancer screening is effective in reducing the burden of breast, cervical, and colorectal cancers, but not all communities have appropriate access to these services. In this study, we aimed to identify under-resourced communities by assessing the association between the Social Vulnerability Index (SVI) with screening rates for breast, cervical, and colorectal cancers in ZIP-code tabulation areas (ZCTAs) in Rhode Island. Methods: This study leveraged deidentified health insurance claims data from HealthFacts RI, the state's all-payer claims database, to calculate screening rates for breast, cervical, and colorectal cancers using Healthcare Effectiveness Data and Information Set measures. We used spatial autoregressive Tobit models to assess the association between the SVI, its four domains, and its 15 component variables with screening rates in 2019, accounting for spatial dependencies. Results: In 2019, 73.2, 65.0, and 66.1% of eligible individuals were screened for breast, cervical, and colorectal cancer, respectively. For every 1-unit increase in the SVI, screening rates for breast and colorectal cancer were lower by 0.07% (95% CI 0.01–0.08%) and 0.08% (95% CI 0.02–0.15%), respectively. With higher scores on the SVI's socioeconomic domain, screening rates for all three types of cancers were lower. Conclusion: The SVI, especially its socioeconomic domain, is a useful tool for identifying areas that are under-served by current efforts to expand access to screening for breast, cervical, and colorectal cancer. These areas should be prioritized for new place-based partnerships that address barriers to screening at the individual and community level. [ABSTRACT FROM AUTHOR]

Given the recent invasion of Scirtothrips dorsalis Hood in North America, there is limited information regarding their distribution and population dynamics in cultivated small fruit crops. Therefore, we investigated the spatial and temporal distribution of S. dorsalis and their natural enemies in commercially produced strawberry fields in Florida. During 2 consecutive strawberry production seasons, 4 and 6 geographically separated strawberry fields were sampled and were divided into grids with 30–40 sampling points per field. At each sampling point, 4–5 leaf and flower samples were collected, and sticky traps were deployed. We quantified the occurrence of S. dorsalis as well as potential natural enemies, including Orius spp. Geocoris spp. and other predators such as long-legged flies. During both years, most of the S. dorsalis and natural enemies were found on field borders, and counts progressively diminished further into the interiors of plots and away from field edges. Cluster and outlier analysis revealed that S. dorsalis formed statistically significant clusters and that these "hot spots" remained in the same general locations throughout the season. There was a strong relationship between the occurrence of natural enemies and the presence of S. dorsalis , but the number of natural enemies was generally low compared to S. dorsalis. Our results indicate that targeting field borders for chemical control or planting strawberries away from natural areas containing potential alternative hosts for thrips may be an effective strategy for reducing agricultural inputs; however, future field assessments are needed to determine if these methods could replace the treatment of entire fields. [ABSTRACT FROM AUTHOR]

Urban crime prevention remains a critical challenge intertwined with socioeconomic disparities. This study investigates the spatial nexus between urban deprivation and crime decline in Chicago from 2015 to 2022, addressing the question: How do changes in neighborhood deprivation relate to crime reduction patterns? Using comprehensive crime incident and census tract Area Deprivation Index (ADI) data, this study conducted exploratory spatial analysis, regression modeling, and local bivariate relationship analysis. The findings reveal persistent hotspots of concentrated deprivation on Chicago's south and west sides, alongside a general citywide crime decline. However, the current research uncovered significant spatial heterogeneity in both deprivation patterns and crime reduction, challenging the achievement of equitable public safety outcomes. Surprisingly, while crime incidents in 2015 and 2022 significantly predicted crime change, ADI scores were not statistically significant predictors in our regression model. Local bivariate analysis exposed diverse relationships between ADI changes and crime rate changes across neighborhoods, with 72.2% of census tracts showing no statistically significant relationship. This underscores the complexity of urban crime dynamics and the importance of local context in understanding these patterns. Our research contributes a nuanced understanding of the intricate relationship between urban deprivation and crime patterns, providing valuable insights for policymakers, law enforcement agencies, and urban planners. These findings highlight the need for integrated, long-term strategies that address both crime prevention and socioeconomic disparities, ultimately fostering safer, more equitable urban environments and informing evidence-based interventions tailored to specific neighborhood contexts. [ABSTRACT FROM AUTHOR]

Mangroves are woody plant communities that appear in tropical and subtropical regions, mainly in intertidal zones along the coastlines. Despite their considerable benefits to humans and the surrounding environment, their existence is threatened by anthropogenic activities and natural drivers. Accordingly, it is vital to conduct efficient efforts to increase mangrove plantations by identifying suitable locations. These efforts are required to support conservation and plantation practices and lower the mortality rate of seedlings. Therefore, identifying ecologically potential areas for plantation practices is mandatory to ensure a higher success rate. This study aimed to identify suitable locations for mangrove plantations along the southern coastal frontiers of Hormozgan, Iran. To this end, we applied a hybrid Fuzzy-DEMATEL-ANP (FDANP) model as a Multi-Criteria Decision Making (MCDM) approach to determine the relative importance of different criteria, combined with geospatial and remote sensing data. In this regard, ten relevant sources of environmental criteria, including meteorological, topographical, and geomorphological, were used in the modeling. The statistical evaluation demonstrated the high potential of the developed approach for suitable location identification. Based on the final results, 6.10% and 20.80% of the study area were classified as very-high suitable and very-low suitable areas. The obtained values can elucidate the path for decision-makers and managers for better conservation and plantation planning. Moreover, the utility of charge-free remote sensing data allows cost-effective implementation of such an approach for other regions by interested researchers and governing organizations. [ABSTRACT FROM AUTHOR]

This study employs comprehensive clustering analysis to examine COVID-19 vaccine hesitancy and related socio-demographic factors across U.S. counties, using the collected and curated data from Johns Hopkins University. Utilizing K-Means and hierarchical clustering, we identify five distinct clusters characterized by varying levels of vaccine hesitancy, MMR vaccination coverage, population demographics, and political affiliations. Principal Component Analysis (PCA) was conducted to reduce dimensionality, and key variables were selected based on their contribution to cumulative explained variance. Our analysis reveals significant geographic and demographic patterns in vaccine hesitancy, providing valuable insights for public health strategies and future pandemic responses. Geospatial analysis highlights the distribution of clusters across the United States, indicating areas with high and low vaccine hesitancy. In addition, multiple regression analyses within each cluster identify key predictors of vaccine hesitancy in corresponding U.S. county clusters, emphasizing the importance of socio-economic and demographic factors. The findings underscore the need for targeted public health interventions and tailored communication strategies to address vaccine hesitancy across the United States and, potentially, across the globe. [ABSTRACT FROM AUTHOR]

Healthcare access greatly impacts skin cancer diagnosis and mortality rates. Recognition of current disparities in Mohs micrographic surgery (MMS) access can assist future policy and clinical decisions to correct them. For the years 2014-2018, the CPT codes for MMS (17,311 and 17,313) were counted on a per county level across the United States per the Medicare Centers for Medicare & Medicaid Services (CMS) Medicare Prescriber Database. Any county with 0 MMS CPT codes recorded were classified as without MMS cases. MMS hotspots were identified as counties that possessed a high average number of MMS cases compared to the national average, while also being surrounded by counties that possessed a low average number of MMS cases compared to the national average. Three thousand eighty-four counties in the United States were analyzed; 785 (25%) counties were designated as with MMS cases and 2301 (75%) without MMS cases. There were no significant differences in age, ethnicity distribution, or cost per enrollee between the two designations. 74% of counties with MMS cases were considered urban, while only 25% of those without cases were urban (p

Abstract Background Early marriage and motherhood have long been prevalent in India, with 44.5% of women aged 20–24 reporting marriage before 18 in NFHS 3 (2005–2006), dropping to 26.8% in NFHS 4 (2015–2016). Early motherhood has also seen a significant decline, with first births by age 18 decreasing from 34.3% in NFHS I to 8.2% in NFHS V. Despite these improvements, significant regional disparities persist due to social, normative, and legal factors. This study investigates the spatial heterogeneity of early marriage and motherhood across Indian districts, offering a multilevel analysis that reveals critical local variations often obscured at broader levels. Understanding these patterns is crucial for targeted policy interventions and addressing the root causes of early marriage and motherhood. Methods Utilizing data from the fifth round of National Family Health Survey, this study employs multilevel logistic regression and geospatial analysis to assess the determinants and spatial distribution of early marriage and early motherhood among ever-married women in India. The analysis incorporates individual, household, and community-level variables, complemented by spatial analysis techniques, including Empirical Bayes Bivariate Moran’s I values and LISA cluster maps, to identify regional patterns and hotspots. Results This study revealed that educational attainment emerged as a critical determinant, with uneducated women significantly more likely to marry early. Socioeconomic factors, such as poverty and limited mass media exposure, also heightened the risk of early marriage and motherhood. Caste and religion were significantly associated with these events, with marginalized groups facing higher prevalence. Spatial analysis revealed significant geographic disparities, with central and eastern regions showing higher concentrations of early marriage and motherhood. District-level characteristics and the influence of neighboring districts were also significant, highlighting the importance of localized interventions. Conclusions The findings underscore the critical role of education, economic empowerment, and media literacy in mitigating early marriage and motherhood risks. The study calls for multi-sectoral interventions in geographical hotspots to break the cycle of early family formation and promote reproductive health. Policies enhancing educational opportunities, addressing economic disadvantages, and considering district-specific factors are essential. Comprehensive strategies are necessary to empower women, foster reproductive health, and address the multifaceted nature of early marriage and motherhood in India.

Floating offshore wind (FOW) is rapidly gaining interest due to its large potential. In this regard, it is of special interest to determine the best locations for its installation. One of the main aspects when evaluating the feasibility of a project is the levelised cost of energy (LCOE), but there are many variables to consider when calculating it for FOW, and plenty of them are hard to find when the scope is all the suitable areas worldwide. This paper presents the calculation and analysis of the global LCOE with particular focus on the best countries and territories from an economic point of view, considering four types of platforms: semi-submersible, barge, spar, and tension leg platform (TLP). The model takes into account, on the one hand, wind data, average significant wave height, and distance to shore for an accurate calculation of delivered energy to the onshore substation and, on the other hand, bathymetry, distances, and existing data from projects to find appropriate functions for each cost with regression models (e.g., manufacturing, installation, operation and maintenance (O&M), and decommissioning costs). Its results can be used to assess the potential areas around the world and identify the countries and territories with the greatest opportunities regarding FOW. The lowest LCOE values, i.e., the optimal results, correspond to areas where wind resources are more abundant and the main variables of the site affecting the costs (water depth, average significant wave height, distance to shore, and distance to port) are as low as possible. These areas include the border between Venezuela and Colombia, the Canary Islands, Peru, the border between Western Sahara and Mauritania, Egypt, and the southernmost part of Argentina, with LCOEs around 90 €/MWh. Moreover, there are many areas in the range of 100–130 €/MWh.

Landfill siting is a major problem in solid waste management. Many aspects should be considered when selecting an appropriate site for a landfill in any particular region. In the current research, seventeen criteria were considered, including rivers, water bodies (lakes), geology, slope, elevation, power lines, groundwater depth, major district roads, archaeological sites, urban centers, infrastructure, villages, pipelines, quarrying, forests, aspect (wind), and agricultural areas. Several random open dump waste disposal sites are dispersed throughout the Ranya District, Iraq. This research employed the integration of a Geographic Information System (GIS) and Analytical Hierarchy Process (AHP) to identify the optimal sites for the establishment of sanitary landfills. The seventeen criteria were mapped and assigned sub-criteria within GIS software. Subsequently, the normalized weights from the AHP were identified for each measure to generate the final Land Suitability Index (LSI) map for the site. Consequently, approximately 0.34% of the entire study area, equivalent to roughly 2,396,000 square meters, is estimated to be suitable for locating a landfill. Additionally, the suitable sites were categorized into moderately suitable, suitable, and mostly suitable. The normalized weight analysis revealed that groundwater depth significantly influences the selection of the best landfill site.

Banana (Musa sp.) is one of the world’s most planted and consumed crops. Analysis of plantations using a geospatial perspective is growing in Costa Rica, and it can be used to optimize environmental analysis. The aim of this study was to propose a methodology to identify areas prone to water accumulation to quantify nitrate concentrations using geospatial modeling techniques in a 40 ha section of a banana plantation located in Siquirres, Limón, Costa Rica. A total of five geomorphometric variables (Slope, Slope Length factor (LS factor), Terrain Ruggedness Index (TRI), Topographic Wetness Index (TWI), and Flow Accumulation) were selected in the geospatial model. A 9 cm resolution digital elevation model (DEM) derived from unmanned aerial vehicles (UAVs) was employed to calculate geomorphometric variables. ArcGIS 10.6 and SAGA GIS 7.8.2 software were used in the data integration and analysis. The results showed that Slope and Topographic Wetness Index (TWI) are the geomorphometric parameters that better explained the areas prone to water accumulation and indicated which drainage channels are proper areas to sample nitrate values. The average nitrate concentration in high-probability areas was 8.73 ± 1.53 mg/L, while in low-probability areas, it was 11.28 ± 2.49 mg/L. Despite these differences, statistical analysis revealed no significant difference in nitrate concentrations between high- and low-probability areas. The method proposed here allows us to obtain reliable results in banana fields worldwide.

Over the past few decades, cities have experienced increased floods affecting property and threatening human life as a result of a warming planet. There is still an incomplete understanding of the flood risk patterns in urban communities with different socioeconomic characteristics. In this study, we produced separate flood exposure and vulnerability indices based on relevant factors, then combined them as a risk index for Houston, Texas and Charleston, West Virginia. We applied statistical methods to extract the most significant social vulnerability factors in each study area. Finally, we mapped significant hot spots or clusters of high flood risk and compared results to socioeconomically disadvantaged populations. Based on the results, high-risk or 1%-annual-chance floodplains cover 23% of the Houston and 7% of Charleston study areas. Within these floodplains, 13% of the total developed land in Houston and 9% in Charleston are situated. In the event of a 1%-annual-chance flood, an estimated 5% of the total population in Houston and 6% in Charleston may require evacuation. Statistically significant flood risk clusters could only be identified in Houston. The implications from this work help to provide an analysis framework for larger urban areas while offering suggestions for its improvement in smaller populated areas.

Promoting food consumption patterns based on a sustainable future in low-income households is important in reducing food waste, particularly in light of global population growth. This study presents an innovative approach to assess nutrient intake efficiency by prioritizing the selection of desirable and undesirable foods, thereby minimizing food waste across diverse locations. The investigation explores the influence of socioeconomic factors on nutrient efficiency in urban and rural areas, considering the categorization of desirable and undesirable food items. To achieve the study's objectives, a combination of Data Envelopment Analysis and a Tobit model is employed, utilizing We used the data of 30,000 rural and urban Iranian households across all provinces in 2022. Then, a Nutrient Efficiency Map was used to visualize the outcomes using the Geographic Information System in Iran. The results reveal that numerous townships exhibit nutrient efficiency scores surpassing 80%, while the Northeastern townships record the lowest scores, falling below 80%. Generally, the Eastern (desert cities) and Northeastern (isolated cities) regions exhibit comparably lower efficiency levels compared to other regions. To enhance nutrient efficiency, it is recommended to curtail the consumption of undesirable food items such as oil, fat, and sweets. Additionally, certain socio-economic and socio-demographic factors display significant associations with nutrient efficiency in both rural and urban areas. Consequently, the government must consider adapting support policies and implementing protection packages that align with the social, geographical, and cultural context. By addressing these factors, policymakers can make substantial contributions toward improving nutrient efficiency and fostering sustainable food consumption practices. [ABSTRACT FROM AUTHOR]

Recent advancements in deep learning have significantly improved the performance of remote sensing scene classification, a critical task in remote sensing applications. This study presents a new aerial scene classification model, the Multi-Scale Swin–CNN Aerial Classifier (MSCAC), which employs the Swin Transformer, an advanced architecture that has demonstrated exceptional performance in a range of computer vision applications. The Swin Transformer leverages shifted window mechanisms to efficiently model long-range dependencies and local features in images, making it particularly suitable for the complex and varied textures in aerial imagery. The model is designed to capture intricate spatial hierarchies and diverse scene characteristics at multiple scales. A framework is developed that integrates the Swin Transformer with a multi-scale strategy, enabling the extraction of robust features from aerial images of different resolutions and contexts. This approach allows the model to effectively learn from both global structures and fine-grained details, which is crucial for accurate scene classification. The model’s performance is evaluated on several benchmark datasets, including UC-Merced, WHU-RS19, RSSCN7, and AID, where it demonstrates a superior or comparable accuracy to state-of-the-art models. The MSCAC model’s adaptability to varying amounts of training data and its ability to improve with increased data make it a promising tool for real-world remote sensing applications. This study highlights the potential of integrating advanced deep-learning architectures like the Swin Transformer into aerial scene classification, paving the way for more sophisticated and accurate remote sensing systems. The findings suggest that the proposed model has significant potential for various remote sensing applications, including land cover mapping, urban planning, and environmental monitoring.

Abstract Rajasthan faces significant environmental challenges, including the pressing need for effective climate change mitigation strategies. Recognizing afforestation as a vital tool in this endeavor, this study leverages the latest in remote sensing and geospatial analysis to map out the state's afforestation potential. The goal is to assess land suitability across Rajasthan for afforestation projects and evaluate the potential carbon sequestration capabilities of different tree species. This aims to inform sustainable land management strategies that can contribute to climate change mitigation. By integrating satellite imagery, land cover data, terrain analysis, and vegetation indices, the study evaluates environmental factors such as slope, soil moisture, and vegetation health to identify areas optimal for tree planting. A carbon sequestration model was also developed to estimate sequestration rates based on species-specific growth patterns. Findings indicate that approximately 40% of Rajasthan is suitable for afforestation, with potential carbon sequestration rates ranging from 2 to 8 tons per hectare per year. Species such as Azadirachta indica (Neem) and Prosopis cineraria (Khejri) are identified as particularly effective for short-term sequestration, while Phyllanthus emblica (Amla) and Ziziphus mauritiana (Ber) are better suited for long-term carbon capture. This research highlights the importance of targeted afforestation using suitable tree species as a nature-based solution (NBS) for climate change mitigation in Rajasthan. The study offers a data-driven approach to enhancing ecosystem resilience and supports decision-making for climate adaptation in arid regions, highlighting the significant potential of Rajasthan's land for carbon sequestration through afforestation.

Urban and regional planning faces unprecedented challenges in the 21st century, ranging from rapid urbanization and population growth to climate change and resource depletion. In addressing these challenges, artificial intelligence (AI) has emerged as a transformative toolset for planners, offering advanced analytics, predictive modeling, and optimization capabilities. In this paper, the author discusses how artificial intelligence can be integrated into urban and regional planning in India’s socio-economic landscape. It highlights the use of machine learning to predict future trends and interpret complex data sets, geospatial analysis using various AI-powered tools for spatial planning, as well as Natural Language Processing for data mining. As a way of understanding and improving urban infrastructure, deep learning techniques can be used in urban image analysis and agent-based modeling along with urban simulation for better prediction and decision-making. Nevertheless, a great number of actors make it difficult to implement such techniques locally such as the absence of valuable local data, limited infrastructure facilities, professional knowledge gaps among employees and their poor integration into existing planning processes. The article strongly stresses institutional capacity building, interagency cooperation through governance structures and open data initiatives. Importantly, there has been an indication that the Indian government is committed to artificial intelligence based on various initiatives and policies showing its willingness to embrace these technologies despite their minimal direct application in Indian urban and regional development so far .

Abstract Geospatial mapping and monitoring are crucial for designing slopes in opencast mining, as failure can result in significant economic and life loss. To overcome these issues, the present investigation evaluated the Digital Elevation Model (DEM) generated using high resolution Cartosat-1 satellite imagery. Simultaneously, mapping of shaded relief map, aspect map and slope maps was done for the monitoring of Shatabdi opencast coal mine of Jharia region, an eastern coalfield region of India. Shaded relief map was categorized in to low, moderate and high categories and maximum area was covered by moderate to high shades. The constructed aspect map encompasses the range between 0 and 360 degrees of direction, with categorization and representation through distinct colors. The slope map was classified into various categories based on degrees of slope, including very gentle, gentle, moderate, moderately steep, steep, and very steep slopes. The maps were ground validated using Differential Global Positioning System (DGPS) collected data points by field visit in the region. For opencast mines to operate safely and profitably, slope stability in different stages of mining must be mapped and monitored. The study synthesizes data from diverse sources, highlighting the role of geospatial technology in addressing multiple research gaps within the mining industries prior to leveraging Cartosat-1 satellite data. Slope mapping and monitoring of coalfield regions are crucial to reducing construction costs, mitigation of natural hazard risks like flooding and landslides, and effective conservation of natural resources such as soils, vegetation, and water systems. The present study benefits policymakers, environmental planners and government in formulating policies to ensure safety, reliability, and enhance the economic growth of workers and the country.

Mangroves are woody plant communities that appear in tropical and subtropical regions, mainly in intertidal zones along the coastlines. Despite their considerable benefits to humans and the surrounding environment, their existence is threatened by anthropogenic activities and natural drivers. Accordingly, it is vital to conduct efficient efforts to increase mangrove plantations by identifying suitable locations. These efforts are required to support conservation and plantation practices and lower the mortality rate of seedlings. Therefore, identifying ecologically potential areas for plantation practices is mandatory to ensure a higher success rate. This study aimed to identify suitable locations for mangrove plantations along the southern coastal frontiers of Hormozgan, Iran. To this end, we applied a hybrid Fuzzy-DEMATEL-ANP (FDANP) model as a Multi-Criteria Decision Making (MCDM) approach to determine the relative importance of different criteria, combined with geospatial and remote sensing data. In this regard, ten relevant sources of environmental criteria, including meteorological, topographical, and geomorphological, were used in the modeling. The statistical evaluation demonstrated the high potential of the developed approach for suitable location identification. Based on the final results, 6.10% and 20.80% of the study area were classified as very-high suitable and very-low suitable areas. The obtained values can elucidate the path for decision-makers and managers for better conservation and plantation planning. Moreover, the utility of charge-free remote sensing data allows cost-effective implementation of such an approach for other regions by interested researchers and governing organizations.

In contemporary urban contexts, addressing challenges like urban sprawl, pollution, and heat islands is imperative. Understanding urban mechanisms and prioritizing proximity and sustainable mobility are crucial for meeting citizens' needs. This study presents a quantitative model with 21 composite indicators, aligning with the 15-minute city theory's dimensions (EDU, HSW, L&C, SUP), to measure resource accessibility. Focused on Valencia, the analysis of its 70 neighborhoods reveals significant disparities in indicators, mainly due to geographical distribution. Peripheries consistently score lower, while city centers and high-status neighborhoods demonstrate higher values. This underscores the importance of targeted interventions to promote equitable access to resources across urban areas.

Abstract Background Along the southern shoreline of Lake Malawi, the incidence of schistosomiasis is increasing with snails of the genera Bulinus and Biomphalaria transmitting urogenital and intestinal schistosomiasis, respectively. Since the underlying distribution of snails is partially known, often being focal, developing pragmatic spatial models that interpolate snail information across under-sampled regions is required to understand and assess current and future risk of schistosomiasis. Methods A secondary geospatial analysis of recently collected malacological and environmental survey data was undertaken. Using a Bayesian Poisson latent Gaussian process model, abundance data were fitted for Bulinus and Biomphalaria. Interpolating the abundance of snails along the shoreline (given their relative distance along the shoreline) was achieved by smoothing, using extracted environmental rainfall, land surface temperature (LST), evapotranspiration, normalised difference vegetation index (NDVI) and soil type covariate data for all predicted locations. Our adopted model used a combination of two-dimensional (2D) and one dimensional (1D) mapping. Results A significant association between normalised difference vegetation index (NDVI) and abundance of Bulinus spp. was detected (log risk ratio − 0.83, 95% CrI − 1.57, − 0.09). A qualitatively similar association was found between NDVI and Biomphalaria sp. but was not statistically significant (log risk ratio − 1.42, 95% CrI − 3.09, 0.10). Analyses of all other environmental data were considered non-significant. Conclusions The spatial range in which interpolation of snail distributions is possible appears < 10km owing to fine-scale biotic and abiotic heterogeneities. The forthcoming challenge is to refine geospatial sampling frameworks with future opportunities to map schistosomiasis within actual or predicted snail distributions. In so doing, this would better reveal local environmental transmission possibilities. Graphical Abstract

Cities need to interact more harmoniously with their environment, especially with regard to the urban water cycle. Currently, public space design standards in Colombia do not include rainwater management for reuse in buildings and irrigation of green spaces, wasting treated water on activities that do not require it. The environmental design of public spaces can also help reduce urban temperatures, especially in dense urban areas. Adapting cities and public spaces to climate change is crucial, especially in geographic contexts such as Colombia, where the El Niño and La Niña climate oscillations will further increase precipitations or droughts in regions of Colombia, making urban and rural populations more vulnerable to flooding, landslides and sea level rise. This study aims to assess the public space potential for the adoption of nature-based solutions (NBSs) in three Colombian cities with different hydrological conditions, climates and urban morphologies: Bogotá, Bucaramanga and Cartagena. The potential for NBSs in urban context was assessed with land suitability analysis and MCDA tools. The results showed that adoption of NBS in public spaces and urban design guidelines can be a valuable strategy to prepare our cities for climate change and provide a better support for urban ecosystem services in Colombia. HIGHLIGHTS Assessment of public space potential for climate change adaptation.; Methodology for the Water Balance Model.; GIS MCDA for NBS location in cities.; Recommendations for public space design retrofitting with NBS.;

Abstract Flooding is the most frequent type of natural disaster, inducing devastating damage at large and small spatial scales. Flood exposure analysis is a critical part of flood risk assessment. While most studies analyze the exposure elements separately, it is crucial to perform a multi-parameter exposure analysis and consider different types of flood zones to gain a comprehensive understanding of the impact and make informed mitigation decisions. This research analyzes the population, properties, and road networks potentially exposed to the 100, 200, and 500-year flood events at the county level in the State of Iowa using geospatial analytics. We also propose a flood exposure index at the county level using fuzzy overlay analysis to help find the most impacted county. During flooding, results indicate that the county-level percentage of displaced population, impacted properties, and road length can reach up to 46%, 41%, and 40%, respectively. We found that the most exposed buildings and roads are laid in residential areas. Also, 25% of the counties are designated as very high-exposure areas. This study can help many stakeholders identify vulnerable areas and ensure equitable distribution of investments and resources toward flood mitigation projects.

This study aims to find a more realistic management approach to Municipal solid waste (MSW), focusing on Ndokwa West LGA, Delta State, Nigeria. It employs a combination of geospatial analyses and laboratory examination of water samples from three borehole locations congruent to landfills in the study area. GPS-measured positions of the five landfill sites were plotted on the topographic plan of the study area and analysed. This landfills' location, which is the southeastern part of the area, is a lowland. By hydraulic gradient and flow pattern of the area deducted from DEM, these landfills are within the pathway of major groundwater resources. Proximity analyses with 5 to 10 km buffer zones show that most of the topographic features are at risk of considerable pollution due to landfill's leachate into the surrounding area. Groundwater vulnerability map shows 21.5% of the studied area at a high risk of contamination, 7.5% at medium, and ~71% at low risk. The Dissolved Oxygen (DO) levels from the three boreholes were significant, suggesting severe pollution, and pointing to the landfill as the main cause. Colour showed an unobjectionable state and signified pollution of the wells. Temperature (0°C) ranged from 20 to 26°C, much higher than the 5°C limit, set by the WHO and NSDWQ. The water pH ranges from 6.05 to 7.02, suggesting traces of heavy metals in water samples. Nitrate and nitrite have values ranging from 5.0 to 6.162 and 0.251 to 0.455 mg/l, suggesting the water samples contain some contaminants. Lead (Pb) (from 0.25 to 0.65 mg/l) is practically beyond the WHO and NSDWQ permissible limits for heavy metals. The accumulation of lead in landfills has substantial effects on groundwater pollution. The Heavy Metal Index Calculation scale rated the result of heavy metals analyses as 'VI,' representing a 'Seriously Affected' water supply system. [ABSTRACT FROM AUTHOR]

In this study, the coastal changes in Hersek Lagoon, have occurred in the 5 years (2015 to 2020) were examined. With the Modified Normalised Difference Water Index (MNDWI), the water areas in the lagoon were determined by object-based classification. When the changes are examined on a regional basis, it is seen that there are erosion and accretion areas were observed. Pearson's r value was examined between End Point Rate (EPR) and Linear Regression Rate (LRR) and a high correlation such as r = 0.921 was obtained. The maximum coastal change was observed as 18.74 m/year with EPR and 18.62 m/year with LRR. [ABSTRACT FROM AUTHOR]