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Introduction: People who use drugs in community settings are at risk of a fatal overdose, which can be mitigated by naloxone administered via bystanders. In this study we sought to investigate methods of estimating and tracking opioid overdose reversals by community members with take-home naloxone (THN) to coalesce possible ways of characterizing THN reach with a metric that is useful for guiding both distribution of naloxone and advocacy of its benefits.Methods: We conducted a scoping review of published literature on PubMed on August 15, 2022, using PRISMA-ScR protocol, for articles discussing methods to estimate THN reversals in the community. The following search terms were used: naloxone AND (“take home” OR kit OR “community distribution” OR “naloxone distribution”). We used backwards citation searching to potentially find additional studies. Overdose education and naloxone distribution program-based studies that analyzed only single programs were excluded.Results: The database search captured 614 studies, of which 14 studies were relevant. Backwards citation searching of 765 references did not reveal additional relevant studies. Of the 14 relevant studies, 11 were mathematical models. Ten used Markov models, and one used a system dynamics model. Of the remaining three articles, one was a meta-analysis, and two used spatial analysis. Studies ranged in year of publication from 2013–2022 with mathematical modeling increasing in use over time. Only spatial analysis was used with a focus on characterizing local naloxone use at the level of a specific city.Conclusion: Of existing methods to estimate bystander administration of THN, mathematical models are most common, particularly Markov models. System dynamics modeling, meta-analysis, and spatial analysis have also been used. All methods are heavily dependent upon overdose education and naloxone distribution program data published in the literature or available as ongoing surveillance data. Overall, there is a paucity of literature describing methods of estimation and even fewer with methods applied to a local focus that would allow for more targeted distribution of naloxone.

BACKGROUND: Community-acquired Staphylococcus aureus (CA-Sa) skin and soft tissue infections (SSTIs) are historically associated with densely populated urban areas experiencing high poverty rates, intravenous drug use, and homelessness. However, the epidemiology of CA-Sa SSTIs in the United States has been poorly understood since the plateau of the Community-acquired Methicillin-resistant Staphylococcus aureus epidemic in 2010. This study examines the spatial variation of CA-Sa SSTIs in a large, geographically heterogeneous population and identifies neighborhood characteristics associated with increased infection risk. METHODS: Using a unique neighborhood boundary, California Medical Service Study Areas, a hotspot analysis, and estimates of neighborhood infection risk ratios were conducted for all CA-Sa SSTIs presented in non-Federal California emergency departments between 2016 and 2019. A Bayesian Poisson regression model evaluated the association between neighborhood-level infection risk and population structure, neighborhood poverty rates, and being a healthcare shortage area. RESULTS: Emergency departments in more rural and mountainous parts of California experienced a higher burden of CA-Sa SSTIs between 2016 and 2019. Neighborhoods with high infection rates were more likely to have a high percentage of adults living below the federal poverty level and be a designated healthcare shortage area. Measures of population structure were not associated with infection risk in California neighborhoods. CONCLUSIONS: Our results highlight a potential change in the epidemiology of CA-Sa SSTIs in California emergency departments. Future studies should investigate the CA-Sa burden in other geographies to identify whether this shift in epidemiology holds across other states and populations. Further, a more thorough evaluation of potential mechanisms for the clustering of infections seen across California neighborhoods is needed.

Spatially resolved transcriptomics technologies have opened new avenues for understanding gene expression heterogeneity in spatial contexts. However, existing methods for identifying spatially variable genes often focus solely on statistical significance, limiting their ability to capture continuous expression patterns and integrate spot-level covariates. To address these challenges, we introduce spVC, a statistical method based on a generalized Poisson model. spVC seamlessly integrates constant and spatially varying effects of covariates, facilitating comprehensive exploration of gene expression variability and enhancing interpretability. Simulation and real data applications confirm spVC's accuracy in these tasks, highlighting its versatility in spatial transcriptomics analysis.

Background: Suicide mortality remains a global health concern, and community characteristics affect regional variations in suicide. This study investigated spatially clustered patterns of suicide mortality rates in South Korea and evaluated the impact of community factors on suicide. Methods: Suicide mortality rates were estimated by sex, age group, and district, using the 2021 Cause of Death Statistics in South Korea from the MicroData Integrated Service. Community-determinant data for 2021 or the nearest year were collected from the Korean Statistical Information Service. The spatial autocorrelation of suicide by sex and age was examined based on Global Moran's I index. Geographically weighted regression (GWR) was used to discern the influence of community determinants on suicide. Results: Suicide mortality rates were significantly higher among men (40.64 per 100,000) and adults over the age of 65 years (43.18 per 100,000). The male suicide mortality rates exhibited strong spatial dependence, as indicated by a high global Moran's I with p < 0.001, highlighting the importance of conducting spatial analysis. In the GWR model calibration, a subset of the community's age structure, single-person household composition, access to mental healthcare centers, and unmet medical needs were selected to explain male suicide mortality. These determinants disproportionately increased the risk of male suicide, varying by region. The GWR coefficients of each variable vary widely across 249 districts: aging index (Q1:0.06–Q3:0.46), single-person households (Q1:0.22–Q3:0.35), psychiatric clinics (Q1:-0.20–Q3:-0.01), and unmet medical needs (Q1:0.09–Q3:0.14). Conclusions: Community cultural and structural factors exacerbate regional disparities in suicide among men. The influencing factors exhibit differential effects and significance depending on the community, highlighting the need for efficient resource allocation for suicide. A regionally tailored approach is crucial for the effective control of the community's mental health management system. [ABSTRACT FROM AUTHOR]

Fuentes-Agueda, S.A.; Gallegos, M.E., and Mandujano, M.C., 2024. Spatial and temporal changes in the demography of Caulerpa paspaloides along a distance gradient. Journal of Coastal Research, 40(5), 952–961. Charlotte (North Carolina), ISSN 0749-0208. It has been proposed that Caulerpa species have greater abundances and growth rates near mangroves than offshore. The main purpose of this study was to observe the demographic differences in Caulerpa paspaloides in three different sites of Los Petenes, Campeche, Mexico (adjacent to the mangroves, at an intermediate distance to the mangroves and offshore). For the demographical census, the number of complete and incomplete fronds, stolons, their diameter, and dry biomass was registered in May, August, and December 2013 and February 2014 for each site. Also, 100 complete fronds in May and December were marked; they were collected in June and February based on growth data obtained through the marked fronds and calculable dry biomass population growth rate (k). Significant differences were found between the abundance of structures between sites and months. The offshore population had the biggest biomass and highest average stolon diameter, whereas the intermediate site has the highest number of complete and incomplete fronds. However, no significant differences in the number of stolons were found. The values of fitness (k) obtained from the analysis of the biomass were 2.06 in summer and 0.68 in winter adjacent to the mangroves, intermediate distance was 1.57 and 1.26, and distance to offshore was 1.56 and 0, respectively. For marked fronds, the fitness (k) was 1.78 for the first site, 1.89 at the intermediate site, and 1.67 offshore. Reduction in C. paspaloides fitness suggests that the species has seasonal growth and reproduction. Also, the most important parameters related to C. paspaloides biomass were salinity and nutrients (total phosphorus and dissolved inorganic nitrogen in column water). The greatest growth rates in biomass were observed close to the mangroves, suggesting that living close to the mangrove has positive effects on the growth of C. paspaloides. [ABSTRACT FROM AUTHOR]

Disposal of municipal solid waste is a significant environmental issue that has detrimental effects on both the environment and human life. The selection of landfill sites is critical and essential to implementing a comprehensive waste management strategy. This study optimizes the selection of landfill sites in Kuwait through the use of overlay analysis and Geographic Information System in conjunction with multi-criteria decision analysis. The study analysis takes into account human, physical economical, environmental, and governmental factors. Seven optimal locations are depicted on the resulting suitability map, each occupying 10 square kilometres or more and have the capacity to handle solid waste for at least one decade per site, considering the current reported waste generation rate. Furthermore, results of a preliminary geotechnical investigation encompassing field and laboratory tests conducted on soil samples collected at one of the proposed sites are presented. The characterization of the retrieved samples included major geotechnical parameters that are of concern in landfill design and siting studies. The results indicate that the soil at the proposed site exhibited low values of hydraulic conductivity, ranging between 3.50 X 10−07 and 8.96 X 10−08 cm/sec, confirming its suitability for siting landfills. There is an urgent need to develop and implement an integrated waste management system in Kuwait. By leveraging the findings of this study, policymakers and governmental authorities can optimize their decision-making procedures to ensure more effective site selection. This approach will significantly reduce contamination risks and enhance sustainability, leading to a more environmentally responsible waste management system in the country. [ABSTRACT FROM AUTHOR]

• Compare a time series of input-output coefficients for 66 different countries. • Assess the effects of geographical proximity on technological convergence over time. • Closer economies tend to be more similar due to geographical tech spillovers. • Institutional proximity also matters for technological convergence. • Over time, closer economies are becoming structurally more similar. From a time-space perspective, we assess the effects of geographical proximity on technological convergence over time identifying proximity dimensions associated with countries' technological similarities. We compare a time series of input-output coefficients for 66 different countries extracted from the 2021 edition of OECD Inter-Country Input-Output to verify whether nearby countries are more likely to share similar technologies. Our results reveal that geographical technological spillovers are important since closer economies tend to be more similar than distant ones. This is particularly evident for the European economies in the sample, suggesting that institutional proximity also matters for technological convergence. Over time, closer economies are becoming structurally more similar; however, this trend seems to have slowed down after the 2008–9 financial crisis. Conjectures on how informational gaps are filled in the consolidation of the databases – encountered in an environment of limited information – based on known practices of using regional and global average structures may add a layer of uncertainty to our results. [ABSTRACT FROM AUTHOR]

• The impact of labor market institutions (LMIs) on regional patents is studied. • I argue that the supply-side approach is fallacious for Europe. • Stronger wage-setting institutions can foster innovation in Europe. • Wage-setting institutions offset the negative impact of employment protection legislation (EPL) on innovation. • Positive spillover effects are identified between European regions. The main goal of this paper is to investigate the relationship between labor market institutions (LMIs) and patents in 177 NUTS-1 and NUTS-2 European regions. Fixed effects models, ordinary least squares (OLS), the generalized method of moments estimation of the fixed effects (FE-GMM), multilevel modeling (MLM), and spatial models are employed. Patents are negatively correlated with EPL and union density and positively associated with wage bargaining coverage and centralization. As a result, a uniform wage that is higher than the competitive wage can enable the Schumpeterian creative destruction process, forcing firms to invest in innovation to remain in the market. Spatial analysis emphasizes that regional proximity promotes the flow of knowledge and increases the chance of innovation. Interactions also matter. Increased bargaining power and coordination, in particular, may outweigh the negative consequences of isolated EPL reforms. Thus, policies that strengthen wage-setting institutions are required in Europe to boost innovation. [ABSTRACT FROM AUTHOR]

Because it is often assumed that fundamental behavioral differences distinguish Neanderthals and Homo sapiens, the ability to structure space within the sites they occupied into distinct activity areas is often invoked as a key distinctive trait of our species. However, this behavior has never been assessed for both groups at a single site, hindering direct comparisons to date. To help resolve this question, this study uses a single methodology to evaluate the spatial organization in the Protoaurignacian levels (A1-A2, associated with Homo sapiens) and the latest Mousterian levels (MS1-MS2, associated with Neanderthals) at Riparo Bombrini (Liguria, Italy) to assess the changes over these three stratigraphic units vis a vis other information about site use. Combining GIS and quantitative methods allows the study of the spatial distribution of plotted finds and features in these levels, showing that Neandertals and Homo sapiens organized their living spaces in accordance with the duration of occupation, the occupation intensity, the tool assemblage and the faunal exploitation. Our results indicate that there is a logic behind the distribution of plotted finds and the use of the space, suggesting comparable cognitive capacities for both anatomically modern humans and Neanderthals. This contributes further data that undermines the notion of 'behavioral modernity' as a useful heuristic in human origins research. [ABSTRACT FROM AUTHOR]

Climate change has added new dimensions of uncertainty associated with India's rice production. Previous research has explored the impact of climate variation on production and noted the interdependence among neighbouring regions. Using district-level data, we investigate two categories of spatial dynamics associated with the climate sensitivity of rice yield. First, we find evidence that supports previous research that yield is significantly influenced by the direct effects of monsoon maximum temperature and rainfall. The second category, capturing indirect effects, is novel because it provides additional insights into formal and informal networks that underpin the information flow on climate adaptation. Empirical evidence of spatial interdependence arising from information spillovers provides insights into the development of spatially targeted policies that offer the potential for knowledge transfer that contributes to sustaining and increasing rice yields. Policy objectives should therefore focus on advancing technology and promoting social networks that enhance information flow across regions. [ABSTRACT FROM AUTHOR]

In recent decades, intensive agricultural activities have adversely impacted the groundwater resources. The post‐green revolution era in India witnessed excessive irrigation practices that negatively impacted sustainable groundwater utilization. This study aims to analyze trends and patterns of groundwater depletion and identify potential factors to determine groundwater depletion across Indian states for the period 2004–2020. In doing so, percentage of safe blocks in terms of groundwater status is used as proxy for groundwater depletion. Employing the Mann–Kendal Method and Sen's slope, the study examines groundwater trends, while spatial analysis is conducted using Moran's I technique. Furthermore, a Fixed Effect‐Generalized Least Square (FE‐GLS) regression approach is employed to identify factors that influence groundwater status. Findings reveal the spatial autocorrelation of groundwater extraction among Indian states, which reflected by a Moran's I value of 0.62. Additionally, this study found that region of northern and southern India exhibits increasing trend groundwater depletion as compared to other regions. Based on the results of FE‐GLS regression and regression with Driscroll–Kraay standard error, our study indicates that crop intensity, gross irrigated land, gross sown area, and the area sown with water‐intensive crops have a negative and significant impacts on the percentage of safe blocks in terms of groundwater availability. Conversely, we found that annual rainfall has a positive and significant relationship with the percentage of safe blocks. From a policy perspective, the study suggests regulating the cultivation of water‐intensive crops and advocates for integrated policies to ensure sustainable water resource and food security – a balance often termed the food‐water‐energy nexus. [ABSTRACT FROM AUTHOR]

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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]

Amidst the spectrum of medical facilities, community health service centers (CHSCs) play an important role in alleviating the burden on hospitals. However, the intricate interaction between hospitals and CHSCs in accessibility analysis is often overlooked. To fill this gap, this study employs an enhanced gravity model to evaluate spatial accessibility of hospitals, focusing on both subdistrict and CHSC levels. The findings show that tertiary hospitals, providing the highest quality medical services, are concentrated in the central districts of Nanjing. Moreover, prevailing CHSC deployment plans risk an inequitable allocation of medical resources at the subdistrict level. In response, we proposed a collaborative mechanism that synergizing hospitals and CHSCs, built on a reciprocal medical alliance referral system. Specifically, our recommendations include: (1) promoting CHSCs to engage in alleviating the patient treatment load borne by tertiary hospitals in the central districts; and (2) balancing between the quality and quantity of CHSCs in the peripheral districts to increase accessibility for small-sized CHSCs in remote locations. This study contributes to the existing literature by incorporating CHSCs into hospital accessibility analysis and refining the framework for the spatial distribution of medical facility in alignment with established policies. [ABSTRACT FROM AUTHOR]

Understanding the inequity in spatial accessibility to COVID-19 vaccination sites is beneficial for the government to optimize vaccine resources. Nevertheless, the result of evaluating spatial accessibility to COVID-19 vaccination sites may be directly affected by the lack of fine-scale and accurate population distribution information in both urban and rural areas. In addition, the choice of travel modes related to different age groups has not been incorporated into existing methods well. Consequently, this study proposes a framework to evaluate spatial accessibility to COVID-19 vaccination sites based on fine-scale population distributions and heterogeneous travel modes. A divide-and-conquer strategy is first presented to estimate the fine-scale populations in both urban and rural areas. A hybrid time distance considering heterogeneous travel modes and a population-weighted proximity indicator are then defined to evaluate spatial accessibility for each age group. The experimental results from Xiangtan, China, show that (1) the divide-and-conquer strategy can effectively predict the urban and rural populations. (2) Similar clustering patterns of spatial accessibility are observed for each age group. (3) Inequities have been confirmed at the county level. The main findings in this study can provide valuable information supporting the spatial optimization of existing COVID-19 vaccination sites. [ABSTRACT FROM AUTHOR]

Land degradation is a global issue with severe implications for local sustainability, regional development, and resilience of socio-environmental systems. Curbing land degradation has become a priority in international strategies and sustainable development goals. The present study examines land vulnerability to degradation and its influencing factors in Italian provinces over four decades (1970–2010). After a comprehensive scrutiny of the best regression strategy based on econometric tests, spatial effects were modelled with the Spatially Autoregressive model run cross-section (1970, 1990, 2010), comparing the results of different weighting matrices representative of short-haul, medium-haul, and long-haul spatial interactions. Multiple (socioeconomic) drivers of land degradation were identified and classified according to their spatial effects. The results highlight the role of spatial interactions in shaping territorial models of land vulnerability to degradation, distinguishing on-site and off-site impacts. These outcomes emphasize the importance of comprehensive approaches that address a place-specific ensemble of socioeconomic drivers in tackling land degradation. A combination of local, regional, and national actions revealed more appropriate to curb unsustainable land practices in Mediterranean economies. [ABSTRACT FROM AUTHOR]

Background Chikungunya (CHIK) emerged in Brazil in 2014 and since then several epidemics have been observed. This study aims to describe the spatial, social and demographic characteristics of individuals affected by CHIK in Espírito Santo state. Methods A cross-sectional study was performed using data from individuals with a confirmed diagnosis of CHIK in Espírito Santo state, Brazil, from 2018 to 2020. Monthly incidence was calculated and annual spatial distribution maps were constructed. Statistical analysis using the χ2 test identified associations between disease occurrence and sociodemographic variables. Results In the period and area analysed, a CHIK epidemic occurred in 2020, with an incidence of 219.8 cases per 100 000 inhabitants. The southern and central regions of Espirito Santo state harboured a risk five times greater than the others in the epidemic region. Females (odds ratio [OR] 1.65 [95% confidence interval {CI} 1.58 to 1.72]), black people (OR 1.22 [95% CI 1.13 to 1.33]), individuals with ≤11 y of education (OR 1.48 [95% CI 1.37 to 1.61]) and the elderly (OR 7.49 [95% CI 6.53 to 8.59]) had a greater risk for the disease. Conclusions CHIK stands as an emerging public health problem in Brazil since its introduction in 2014. Espírito Santo suffered a substantial epidemic in 2020, possibly due to outbreaks in neighbouring states. The population at risk should be prioritized in healthcare, considering the morbidity potential of the disease. [ABSTRACT FROM AUTHOR]

Background: The built environment, as a critical factor influencing residents' cardiovascular health, has a significant potential impact on the incidence of cardiovascular diseases (CVDs). Methods: Taking Xixiangtang District in Nanning City, Guangxi Zhuang Autonomous Region of China as a case study, we utilized the geographic location information of CVD patients, detailed road network data, and urban points of interest (POI) data. Kernel density estimation (KDE) and spatial autocorrelation analysis were specifically employed to identify the spatial distribution patterns, spatial clustering, and spatial correlations of built environment elements and diseases. The GeoDetector method (GDM) was used to assess the impact of environmental factors on diseases, and geographically weighted regression (GWR) analysis was adopted to reveal the spatial heterogeneity effect of environmental factors on CVD risk. Results: The results indicate that the built environment elements and CVDs samples exhibit significant clustering characteristics in their spatial distribution, with a positive correlation between the distribution density of environmental elements and the incidence of CVDs (Moran's I > 0, p < 0.01). Further factor detection revealed that the distribution of healthcare facilities had the most significant impact on CVDs (q = 0.532, p < 0.01), followed by shopping and consumption (q = 0.493, p < 0.01), dining (q = 0.433, p < 0.01), and transportation facilities (q = 0.423, p < 0.01), while the impact of parks and squares (q = 0.174, p < 0.01) and road networks (q = 0.159, p < 0.01) was relatively smaller. Additionally, the interaction between different built environment elements exhibited a bi-factor enhancement effect on CVDs. In the local analysis, the spatial heterogeneity of different built environment elements on CVDs further revealed the regional differences and complexities. Conclusions: The spatial distribution of built environment elements is significantly correlated with CVDs to varying degrees and impacts differently across regions, underscoring the importance of the built environment on cardiovascular health. When planning and improving urban environments, elements and areas that have a more significant impact on CVDs should be given priority consideration. [ABSTRACT FROM AUTHOR]

In this study, the Geo-accumulation index (Igeo), Human Health Risk Assessment (HRA), and Ecological Risk Index (ERI) were utilized to examine the risks associated with the soils at the DaeyangYeongseong mine. Brassica juncea and Raphanus sativus were employed in the ecological toxicity test. In all soil samples, the mean Igeo value of arsenic measured 3.15, and cadmium measured 6.63, indicating a very high level of heavy metal contamination. The carcinogenic risk of cadmium and arsenic for adults was 4.30×10−3 and 1.43×10−5, respectively. For children, these values were 3.92 × 10−2 and 1.33 ×10−4, exceeding the acceptable level (1×10−6). In all soils, cadmium showed extremely high ecological risk levels, and arsenic had extremely high risk levels in 34.8% of the total area. This was also confirmed in toxicity assessments using plants. Therefore, arsenic and cadmium were found to be the main causes of soil contamination and ecological risk. [ABSTRACT FROM AUTHOR]

Transport accessibility is crucial for defining urban clusters and evaluating a region's centrality and connectivity. To foster equitable growth in urban conglomerates, it is crucial to understand the relationship between transport accessibility and regional development. This study introduces the concept of 'regional efficiency'—the effectiveness of converting regional inputs into outputs—determined via the super-efficiency Data Envelopment Analysis methodology, to assess the progression of regional development. Employing a combination of bivariate spatial autocorrelation and spatial regression analysis, this research elucidates the spatial dynamics between transport accessibility and regional efficiency, both at macro and micro levels. A binary Logit model identifies the factors affecting the Moran's I correlation between regional efficiency and transport accessibility. Based on the diverse urban characteristics within a single conglomerate, tailored development strategies are suggested. Focusing on the Yangtze River Delta Urban Agglomeration in China as a case study, four distinct spatial clusters—High–High (HH), Low–Low (LL), High–Low (HL), and Low–High (LH)—are identified, based on the local correlation between regional efficiency and transport accessibility. The findings reveal that regions with a High–High Moran's I correlation between road accessibility and regional efficiency predominantly located in the eastern sectors of the Yangtze River Delta, exhibiting effective regional efficiency. These regions are characterized by superior road accessibility, aligning well with their regional efficiency metrics. For air accessibility, cities classified under the HH category, including Shanghai and its neighboring cities, demonstrate a significant spatial correlation with regional efficiency. Population density and urban tier are key predictors of the significance of the Moran's I correlation between road accessibility and regional efficiency. In contrast, for air accessibility, these factors assume an inverse role. The identified cluster types (HH, LL, LH, and HL) are proposed as predictive indicators for the significance of the Moran's I relationship between air accessibility and regional efficiency. [ABSTRACT FROM AUTHOR]

Background: In malaria-endemic countries, asymptomatic carriers of plasmodium represent an important reservoir for malaria transmission. Estimating the burden at a fine scale and identifying areas at high risk of asymptomatic carriage are important to guide malaria control strategies. This study aimed to estimate the prevalence of asymptomatic carriage at the communal level in Burkina Faso, the smallest geographical entity from which a local development policy can be driven. Methods: The data used in this study came from several open sources: the 2018 Multiple Indicator Cluster Survey on Malaria and the 2019 general census of the population data and environmental. The analysis involved a total of 5489 children under 5 from the malaria survey and 293,715 children under 5 from the census. The Elbers Langjouw and Langjouw (ELL) approach is used to estimate the prevalence. This approach consists of including data from several sources (mainly census and survey data) in a statistical model to obtain predictive indicators at a sub-geographical level, which are not measured in the population census. The method achieves this by finding correlations between common census variables and survey data. Findings: The findings suggest that the spatial distribution of the prevalence of asymptomatic carriage is very heterogeneous across the communes. It varies from a minimum of 5.1% (95% CI 3.6–6.5) in the commune of Bobo-Dioulasso to a maximum of 41.4% (95% CI 33.5–49.4) in the commune of Djigoué. Of the 341 communes, 208 (61%) had prevalences above the national average of 20.3% (95% CI 18.8–21.2). Contributions: This analysis provided commune-level estimates of the prevalence of asymptomatic carriage of plasmodium in Burkina Faso. The results of this analysis should help to improve planning of malaria control at the communal level in Burkina Faso. [ABSTRACT FROM AUTHOR]

Degradation and desertification represent serious threats, as they present severe environmental and socio-economic consequences, demanding immediate action. Although a recognized methodology for assessing degradation and desertification is missing, remote sensing has been recognized as a powerful support for its accessibility and efficacy. The aim of this study is to examine the application of remote sensing for assessing land and soil degradation and desertification. A total of 278 research papers retrieved from Scopus/Web of Science database and published over the past decade have been analyzed. From the analysis of scientific publications, a rising interest for these topics and a dominance of research from China has been registered. Established satellite data, Landsat, and MODIS, despite limitations in accuracy and resolution, remain popular due to easy access. This restricts research to broader scales and limits practical applications like land management. The prevalent use of vegetation indexes, while convenient, can be misleading due to their indirect connection to soil health. Consequently, vegetation-based models may not fully capture the complexities involved. To improve understanding, the study suggests a shift towards multi-indexes models and a move away from relying solely on readily available data products. Moreover, the application of data fusion methods could provide a more holistic view. [ABSTRACT FROM AUTHOR]

China has established a nationwide formal e‐waste disassembly system based on the extended producer responsibility principle. However, the system mainly focuses on material recycling while excluding the reuse of used electrical and electronic equipment (used EEE) due to the complexity of flows and transactions during the reuse stage. Recently, emerging online platforms have played an increasingly crucial role in the value chain of reuse and recycling, significantly improving the visibility of flows in these activities. This paper aims to depict the spatial structure of the used EEE and e‐waste flow networks using a multi‐scale analysis framework. Using spatial analysis tools in complex network analysis, we characterize cross‐city reuse flows in China. A clear regional pattern of a hierarchical reuse network of notable regional hubs in certain regions in China is revealed in this analysis. The role of regulation is demonstrated in the comparison of the spatial flows in reuse and recycling. In conclusion, it is proposed that reuse should be included and emphasized in the management of waste electrical and electronic equipment in China to maximize the value of circularity. [ABSTRACT FROM AUTHOR]

This study addresses the urgent need for sustainable urban mobility through a comprehensive analysis of decarbonization potential in the metropolitan area of Valencia, Spain. The research is motivated by the imperative to mitigate climate change amidst high traffic and congestion levels. Utilizing the Origin–Destination matrix from the Valencian community's mobility plan, the study prioritizes displacements for substitution by sustainable alternatives. Through a detailed case study analysis, critical areas for decarbonization are identified, and practical strategies to reduce carbon emissions are proposed. The methodology encompasses a thorough literature review on decarbonization measures, examination of existing plans, and analysis of sustainable trips with high spatial resolution using geographic information systems (GISs). The study identifies trips with significant decarbonization potential and assesses the current public transportation network. Spatial analyses illustrate demand patterns for sustainable transport options, emphasizing the need for tailored strategies. Key findings highlight the role of private and commercial transport in CO2 emissions, advocating for targeted interventions to enhance active transport infrastructure, promote carpooling, adopt low-emission vehicles, and improve public transport accessibility and efficiency. [ABSTRACT FROM AUTHOR]

This paper is intended to elucidate the efficacy of employing a Geographic Information Systems (GIS) methodology to investigate the spatial interrelations among migration flows, housing prices, age demographics, and geographical distances. Utilizing data on migration flows and housing prices from Greater Manchester, UK, in 2011, in this study, we performed statistical analyses and generated a suite of visual mappings to illustrate the spatial dynamics between migration patterns, distances, age brackets, and the segmentation of migration destinations by housing price tiers. The findings reveal pronounced spatial associations among migration destinations, underscored by the interplay of housing price levels with age categories, distance intervals, or the origination points tied to specific housing price ranges. This GIS-based analytical framework offers policy-makers and urban planners a powerful tool for discerning distinct migration trends and locales, thereby facilitating more informed decision-making processes. [ABSTRACT FROM AUTHOR]

African urban agriculture (UA) has garnered attention for its contributions to food security and socio-economic improvement. However, its impact on the psychological well-being of farmers has received minimal focus. This study explores the psychosocial effects of UA by surveying 733 farmers in Dar es Salaam and Greater Lomé. Utilizing the Mental Health Continuum–Short Form (MHC-SF) and bespoke questionnaires, our research evaluates the emotional benefits of urban farming. Findings from regression analyses and spatial assessments conducted using Python and QGIS 3.32.2-Lima indicate significant variations in UA-related happiness across different city zones, with peripheral farmers experiencing greater satisfaction than their urban counterparts. Notably, female farmers reported higher levels of happiness, underscoring UA's potential to empower women. This study advocates for the integration of UA into urban planning frameworks to foster psychologically beneficial urban environments. [ABSTRACT FROM AUTHOR]

This article explores the relationship between spatial factors, socioeconomic conditions, and Twitter (now called X) sentiment in New York City (NYC) during the COVID-19 pandemic. Using Twitter data, the study investigates how sentiment varied across different geographies. It examines whether sentiment scores, unemployment rates, and COVID-19 hospitalization rates in NYC zip codes revealed spatial associations. The research employs sentiment analysis, a natural language processing technique used to algorithmically determine the emotional tone of a text, on a database of geo-located tweets spanning January to December 2020. The findings reveal a shift towards more negative sentiment during the initial year of the pandemic. Moreover, the study uncovers variations in sentiment trends across boroughs and zip codes. Additionally, a zip code-level fixed-effects model demonstrates a statistically significant relationship between sentiment scores and unemployment rates. In summary, this article makes a two-fold contribution: firstly, it adds a spatial lens to the scholarly debate regarding the use of Twitter data as an indicator of publicly expressed sentiment; secondly, it provides empirical evidence on the spatial interconnectedness of sentiment, health (hospitalization), and socioeconomic factors (unemployment). Overall, this research sheds light on the nuanced relationship between sentiment and space during the COVID-19 pandemic in NYC. [ABSTRACT FROM AUTHOR]

This study offers a new perspective on settler colonial theory through the case of Serbia/Kosovo. It develops two hypotheses: (1) settler colonial projects in neighboring territories differ markedly from those overseas, necessitating a revised conceptualization called here ‘contiguous settler colonialism,’ and (2) these projects may prompt settlers to remain amid decolonization despite adverse conditions. The empirical analysis identifies three key factors leading to this outcome: the 'Serbianization' of the physical environment, the (im)mobility arrangements of Kosovo Serbs, and Serbia's coercive cooptation system, resulting in a 'settler colonial make-believe space' between the former colonial state and the emerging decolonizing polity. [ABSTRACT FROM AUTHOR]

Background: More than 95% of malaria transmission in Brazil occurs in the Legal Amazon Region, which in 2010 recorded around 333,429 cases reported in the Epidemiological Surveillance Information System-Malaria (Sivep_malaria), presenting an annual parasitic incidence (IPA) of 13.1 cases/1000 inhabitants. Methods: This was a descriptive study that measured the community prevalence of Plasmodium infection and its relationship with land use in Três Fronteiras District, Colniza Municipality, Mato Grosso State. Data were collected during household visits in July 2011, with blood collection from finger pricks for the preparation of thick smear slides, and completion of a standardized case notification form. A georeferenced database was analysed, with land use evaluated as categorical variables. A kernel density map was built to show the density of cases and their location. Results: Of the 621 respondents, 68(11%) had Plasmodium infection: 39 (57.4%) with Plasmodium vivax, 27(39.7%) with Plasmodium falciparum and two (2.9%) with mixed infections. Among infected individuals, 49 (72.1%) were men. Cases of malaria were distributed over the district, with greater occurrence of cases per household in open areas close to the mining company and artisanal mining sites. The was a greater density of cases located in the gold mining region. Conclusion: Transmission of malaria in Três Fronteiras District has a heterogeneous distribution. Individuals residing in mining and timber extraction sites have increased occurrence of Plasmodium infection. [ABSTRACT FROM AUTHOR]

Rural electrification is a crucial step for the socio-economic development of isolated communities. Decentralized power generation, typically more favorable for renewable energies, requires an accurate analysis of the different electrification options, whose convenience depends on multiple factors. The application of Geographical Information Systems (GISs) to energy planning allows the assessment at a local level, considering the variability and demand distribution of spatial resources. This work introduces IntiGIS-local, a GIS-based model implemented in the ArcGIS environment, designed to calculate the levelized energy cost (LEC) for different electrification options. The model allows the comparison between three power generation alternatives: solar system, diesel generator set and solar–diesel hybrid system. Configurations are adjustable through input variables, with a special focus on the confrontation between individual systems and microgrids. The objective is to provide an adequate groundwork for developing a decision-making tool to assess diverse rural electrification options in future studies. The model IntiGIS-local is tested in the case study of the Guasasa community (Cuba). [ABSTRACT FROM AUTHOR]

Morphological variation along the spatial distribution of species has been extensively investigated in ecological studies, and several ecogeographical rules explore the relationships between morphological traits and the environment. Many morphological traits are correlated, providing an opportunity to evaluate the validity of multiple ecogeographical rules simultaneously. Bergmann's rule predicts that endothermic animals in colder locations are larger than those in warmer locations. Jordan's rule predicts that fish from colder locations have more vertebrae than those from warmer locations. We tested the validity of Bergmann's and Jordan's rules for the neotropical lancehead snake Bothrops jararaca. We evaluated three morphological characters of 342 specimens: number of ventral scales (proxy for vertebrae number), snout–vent length (a linear measure of body size) and stoutness (volumetric body size). We implemented spatial regressions to evaluate the variation of morphological dimensions using climatic predictors: the minimum temperature and evapotranspiration. SVL was poorly related to minimum temperature and evapotranspiration. However, stouter individuals were found in colder places with greater evapotranspiration, following Bergmann's rule and the water conservation hypothesis. Individuals in warmer locations also had a greater number of ventral scales, reversing Jordan's rule. We showed that different selective pressures act on different morphological dimensions. Although stoutness follows Bergmann's rule, its variation would arise from an energy storage demand rather than heat conservation. Also, stoutness variation along evapotranspiration gradients could represent a mechanism to avoid hydric stress in environments with considerable climatic variations. The variation in vertebrae number along temperature gradients could be related to ecological factors and foraging. We highlight that physioecological mechanisms to deal with climatic variation and ecological aspects could be identified in snakes through intraspecific analyses, contrasting with interspecific studies that can hardly detect general trends. Due to different environmental effects on body size, we shed new light on the importance of exploring multiple morphological dimensions in macroecological studies. [ABSTRACT FROM AUTHOR]

This paper contributes to the expansion of green energy production, which is being pursued in order to mitigate climate change and carbon emissions from energy production. It addresses the delineation of areas that are suitable for the application of photovoltaics in the context of agricultural areas, water bodies, and parking spaces. Three specific photovoltaic types are examined in order to find out which criteria influence their geographic potential and whether spatial multi-criteria decision analysis methods are suitable for identifying suitable areas. The proposed approach consists of four steps: (1) collecting factors through expert interviews and questionnaires; (2) mapping the criteria to the spatial datasets; (3) deriving weighted scores for individual criteria through expert interviews; (4) applying the multi-criteria decision analysis method to compute and aggregate the final scores. We test our methodology at selected sites in the state of Styria, Austria. The test sites represent the topographical characteristics of the state and are about 5% of the size of Styria, approximately 820 km2. The key contributions are a weighted set of criteria that are relevant for the geographic potential of alternative photovoltaic types and the developed methodology to determine this potential. [ABSTRACT FROM AUTHOR]

The aim of this study was to analyse the spatial patterns and factors associated with the incidence of tuberculosis-diabetes (TB-DM) in Brazil, from 2001 to 2019. Ecological study. Brazilian municipalities were used as the units of analysis. The local empirical Bayesian rate and the spatial autocorrelation test were calculated. Moran and Getis-Ord Gi∗ were used to identify spatial clusters, and spatially weighted regression was conducted. In total, 75,021 new cases of TB associated with DM were reported in Brazil during the study period. Most Brazilian municipalities had an average TB-DM incidence of 1.0–2.0/100,000 inhabitants. The regression showed that the Gini index (β = 0.85) and family health strategy coverage (β = −0.26) were the two indicators that had the most influence on TB-DM incidence in Brazil. This study identified spatial clusters of TB-DM in Brazil. The results also indicated that social inequalities played a key role in the incidence of TB. [ABSTRACT FROM AUTHOR]

Recent highly-publicized cases of police violence have raised broader discussions around understanding use-of-force as institutional racism. We explore how variation in police practices, including discretionary stops and targeting outdoor spaces, along with racialized understandings of crime and space, help explain use-of-force in neighborhoods. Using stop-and-frisk data from the New York City Police Department (NYPD) in Census tracts (N = 12675) between 2006 and 2012, we conduct a spatial analysis and estimate multilevel negative binomial regression models. We find relationships between use-of-force incidents and police organizational practices, where police use force more often in neighborhoods where they employ greater discretionary stops, and in neighborhoods where police conduct proportionally more indoor stops. Our findings also point to understanding stop-and-frisk as a spatial strategy concentrated largely in neighborhoods of color, where police use force more often in Black and Latinx neighborhoods above and beyond the racial disproportionately of individuals stopped. Police also use force more often in neighborhoods where they perceive more crime, even after accounting for the observed crime rate. We suggest that use-of-force by the NYPD is systematically produced through organizational practices paired with shared racialized understandings of crime and space that vary across neighborhoods. [ABSTRACT FROM AUTHOR]

Drought occurs when there is a sustained decrease in rainfall over an extended period, impacting the socio‐cultural and environmental aspects of humans and other living beings. The geographic distribution and timing of droughts play a crucial role in drought management and mitigation strategies. Identifying and predicting the onset of droughts in specific regions, especially in watershed areas, is a primary concern in the field of hydrology. This study focuses on how the spatiotemporal patterns of drought are developing in Turkish Basins using detailed data on Terrestrial Water Storage (TWS), precipitation, and temperature at the pixel level. GRACE (Gravity Recovery and Climate Experiment), PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), and WorldClim (World Climate) data sets are employed to assess long‐term changes of drought on a basin‐scale. Spatial analyses are conducted in a Geographic Information System (GIS) environment for the derivation of basinal monthly mean, minimum, and maximum statistics of TWS, precipitation, and temperature anomalies within Turkish Basins. Time series analyses are implemented to investigate the temporal evolution of droughts in these basins, for the basinal monthly mean, minimum, and maximum statistics obtained. The Mann–Kendall trend test and Pettitt change point detection tests are used to assess the statistical significance of the calculated trends and to expose the existence of any change point therein, respectively. The findings of the study indicate that Turkiye faces a significant risk of drought development in nearly all its basins, particularly after 2016. The GRACE dataset provides realistic insights into the temporal behaviour of hydrological droughts. PERSIANN is effective in identifying years with extreme meteorological conditions, and the standardized precipitation index (SPI) shows similar effectiveness, while they are ineffective in exposing significant trends due to the nature of the precipitation data. WorldClim data proves insufficient for modelling the temporal behaviour of droughts in these basins. [ABSTRACT FROM AUTHOR]

Our knowledge of biodiversity hinges on sufficient data, reliable methods, and realistic models. Without an accurate assessment of species distributions, we cannot effectively target and stem biodiversity loss. Species range maps are the foundation of such efforts, but countless studies have failed to account for the most basic assumptions of reliable species mapping practices, undermining the credibility of their results and potentially misleading and hindering conservation and management efforts. Here, we use examples from the recent literature and broader conservation community to highlight the substantial shortfalls in current practices and their consequences for both analyses and conservation management. We detail how different decisions on data filtering impact the outcomes of analysis and provide practical recommendations and steps for more reliable analysis, whilst understanding the limits of what available data will reliably allow and what methods are most appropriate. Whilst perfect analyses are not possible for many taxa given limited data, and biases, ensuring we use data within reasonable limits and understanding inherent assumptions is crucial to ensure appropriate use. By embracing and enacting such best practices, we can ensure both the accuracy and improved comparability of biodiversity analyses going forward, ultimately enhancing our ability to use data to facilitate our protection of the natural world. [ABSTRACT FROM AUTHOR]

The integration of shared e-scooters with public transit is a promising solution for urban mobility's first/last-mile challenge. This study explores spatiotemporal factors influencing this integration, using 35-day e-scooter trip data from Chicago. Employing a random-effect negative binomial approach, we modeled the frequency of e-scooter trips to access/egress to/from bus stops and train stations. Results indicate that weather conditions, design features like intersection density, and multimodal network density significantly influence usage. The transit system characteristics such as service frequency have a positive effect on the integration of e-scooters and trains while a similar effect for bus and e-scooter integration was not significant. Furthermore, safety-related variables such as accident and crime rates as well as demographic characteristics were also revealed to be significant factors in our study. These findings offer vital insights to urban planners and policymakers for infrastructure, safety enhancements, and interventions to encourage efficient e-scooter-public transit integration. [ABSTRACT FROM AUTHOR]

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Heavy metal contamination in urban land has become a serious environmental problem in large cities. Visible and near-infrared spectroscopy (vis-NIR) has emerged as a promising method for monitoring copper (Cu), which is one of the heavy metals. When using vis-NIR spectroscopy, it is crucial to consider sample similarity. However, there is limited research on studying sample similarities and determining their relative importance. In this study, we compared three types of similarities: spectral, compositional, and spatial similarities. We collected 250 topsoil samples (0–20 cm) from Shenzhen City in southwest China and analyzed their vis-NIR spectroscopy data (350–2500 nm). For each type of similarity, we divided the samples into five groups and constructed Cu measurement models. The results showed that compositional similarity exhibited the best performance ( R p 2 = 0.92, RPD = 3.57) and significantly outperformed the other two types of similarity. Spatial similarity ( R p 2 = 0.73, RPD = 1.88) performed slightly better than spectral similarity ( R p 2 = 0.71, RPD = 1.85). Therefore, we concluded that the ranking of the Cu measurement model's performance was as follows: compositional similarity > spatial similarity > spectral similarity. Furthermore, it is challenging to maintain high levels of similarity across all three aspects simultaneously. [ABSTRACT FROM AUTHOR]

In the post-industrial era, many cities have experienced the decline of heavy industry and traditional manufacturing, leading to the widespread emergence of brownfields. These often cluster geographically, forming "brownfield clusters" characterized by shared spatial and functional traits. Our research examined these phenomena within 10 resource-exhausted cities in China, employing kernel density analysis to explore the spatial dynamics within and among these clusters and their urban contexts. We identified three distinct spatial relationships between brownfield clusters and their host cities (coupling, juxtaposition, and encircling), with a detailed case study in Huangshi City further classifying the clusters into five categories based on their dominant factors, spatial morphologies, types of brownfields, and internal dynamics. The study reveals that the spatial configurations of brownfield clusters are significantly influenced by geographic features, transportation infrastructure, and policy frameworks. Based on these findings, we propose targeted regeneration strategies for each cluster type. This research not only enhances our understanding of brownfield challenges and opportunities in China's resource-exhausted cities but also serves as a valuable reference for other cities and regions worldwide facing similar challenges. [ABSTRACT FROM AUTHOR]

Visual quality and impact assessments have historically relied on experts to formally evaluate the visual properties of a landscape. In contrast, environmental psychologists have studied subjective landscape preferences using ratings and surveys. These two approaches represent, respectively, the "objectivist" and "subjectivist" paradigms within visual landscape research. A gap, however, exists between these approaches: actual observation behaviors. In this paper, we argue for the inclusion of eye-tracking research in visual landscape assessments as a critical bridge between objective landscape qualities and subjective visual experiences. We describe the basics of eye-tracking methods and data types to introduce the role of eye movements in landscape preference formation. Three-dimensional immersive virtual environments are particularly useful for collecting these types of data, as they allow for quantification of the viewed environment's spatial and scene metrics in addition to providing eye-tracking capabilities at sufficient resolutions. These environmental and behavioral data can then be consolidated and analyzed within existing GIS platforms to draw conclusions about environmental influences on observation behaviors. While eye tracking may eventually contribute directly to the practice of visual quality or impact assessments, the near-term benefits of this work will most likely center around contributing to the objectivity and defensibility of assessments through validation and methodological recommendations. [ABSTRACT FROM AUTHOR]

Accessibility is closely related to residents' well-being and quality of life and is a potential indicator of social equity. This study aims to present a methodology for assessing the combined equity of living service amenities (LSAs) based on accessibility. This study focuses on fourteen types of LSAs in six dimensions and improves the three-step floating catchment area (3SFCA) model by considering the behavioral demand characteristics of different age groups. Taking the main built-up area of Xi'an as an example, the modified 3SFCA model is applied to assess the accessibility of LSAs, and the supply–demand index is used to measure the supply and demand level of the horizontal dimension. Furthermore, random forest regression was used to screen the key socioeconomic indicators affecting the accessibility of LSAs, and then the binary spatial correlation local index was used to reveal the spatial distribution characteristics between LSA accessibility and key socioeconomic indicators in the vertical dimension. Finally, the comprehensive equity of LSAs is evaluated by space superposition. The results showed that there was a serious imbalance between the supply and demand of LSAs in Xi'an's main built-up area, with polarized oversupply and weak supply areas, especially for accessibility to low-grade LSAs. Accessibility is relatively low for children and young and middle-aged groups, and intergenerational inequalities were particularly pronounced among them. The lower-income group was generally at a disadvantage in accessing the high-demand amenities, and those who resided in affordable housing in the periphery of the city were more likely to face social exclusion. This study emphasizes the importance of distributing urban resources equitably among different social groups, which can help decision makers achieve a balance between horizontal equity and vertical equity in the allocation of urban LSAs and promote spatial equity and sustainable social development. [ABSTRACT FROM AUTHOR]

We assess the relationship between food insecurity and land inequality in Colombia during the period 2005–2015. We use spatially controlled system estimation at the level of Departamentos (subnational administrative units), verifying its advantages when compared to linear non-spatially controlled estimation. The analysis builds upon the idea that there is a direct impact of land inequality on food insecurity, besides its indirect impact through its effect on general inequality and poverty. The paper describes the historical-geographical processes that have determined high concentration of land ownership in Colombia, and subsequently, it uses regression analyses to verify that there is a spatially controlled effect of land inequality on food insecurity, and not the other way around. This result holds under different spatial specifications and the inclusion of corresponding control variables. The direct connection between these variables calls for policy measures that directly address land inequality as a food insecurity determinant, in addition to the indirect solution that current poverty alleviation policies might have on food insecurity. [ABSTRACT FROM AUTHOR]

This study investigates the social and economic factors influencing the enrollment rates of women and men in Science, Technology, Engineering, and Mathematics (STEM) fields across all 81 provinces in Turkey, utilizing a spatial econometric analysis model. This model, considering regional characteristics and spatial relationships, allows for a comprehensive understanding of the geographical distribution of interest in STEM fields and the gender disparities within. Analyses at both global and local levels were conducted to test for the presence of spatial patterns. The impact of social and economic factors on gender-specific enrollment rates in STEM fields was assessed using Ordinary Least Squares analysis, without accounting for spatial autocorrelation. The dataset, based on provincial social and economic indicators for the year 2022, revealed that both women and men in the provinces exhibit similar tendencies towards settling in STEM fields. The preference for STEM fields is lower in the eastern regions, while more pronounced in coastal and industrialized cities. The findings indicate a positive correlation with per capita gross domestic product, the number of teachers in higher education institutes, central government budget expenditure, and the population ratio in the province. [ABSTRACT FROM AUTHOR]

Visceral Leishmaniasis (VL), commonly known as Kala-azar, is a tropical, endemic, and neglected infection in over 65 countries. It is caused by Leishmania donovani and L. infantum, which are protozoa transmitted by sandflies between humans and other mammals. It is a prevalent disease in impoverished regions, primarily affecting people in conditions of social vulnerability. This study aims to identify risk areas for visceral leishmaniasis and its relationship with socioeconomic indicators in the state of Tocantins, a state influenced by the Brazilian Amazon. An ecological observational study was conducted to analyze the spatial distribution, risk areas, and time series for visceral leishmaniasis cases in the municipalities of Tocantins, Brazil, from 2011 to 2020. The state showed a decreasing trend for the disease, but still with high incidence levels, especially in the health regions in the northern part of the state. These regions presented risk areas for the disease transmission and a strong relationship between the indices and the studied socioeconomic indicators. Despite the efforts of the local and regional healthcare systems, the disease persists as a significant public health challenge in several cities of the state, requiring interdisciplinary measures, as the problem is closely related to social indicators that directly impact the population's health reality. [ABSTRACT FROM AUTHOR]

Safranbolu, located in the Western Black Sea region of northern Turkey, consists of three historical settlements distinguished by monumental buildings and especially traditional houses. For this reason, the city was declared a 'World Heritage Site' by UNESCO in 1994. Traditional Safranbolu houses are examples of Turkish houses built with mixed techniques. In research on Turkish residences and traditional Safranbolu houses, the focus has been on the origin of dwelling construction culture, the spatial reading of the parts of the house, the sociocultural life of the owners, and the surveying of traditional construction techniques. In this study, the design principles affecting the spatial arrangement of Safranbolu houses were evaluated from a holistic perspective. Dwellings selected from Safranbolu sites were analyzed at the urban scale according to the environmental design data of the residential area. It can be seen that houses were designed in a certain order according to these factors. In the building scale analyses, the diversity in the domestic space configuration was surveyed by the drawings of immediate environments at the plans and sections of the houses. The study contributes by explaining the relationship between parcel characteristics and house design. [ABSTRACT FROM AUTHOR]

The issue of environmental injustice has received critical attention in sustainable development. Whereas, there is still a lack of research on exploring the complexity of environmental injustice related to the spatial mismatch of ecosystem services (ES) supply–demand. Hence, we firstly built a framework consisting of 10 types of ES supply and 3 types of ES demand to evaluate the ES supply–demand ratio (ESDR) at county scale in three mega cities in China. Then we adopted spatial autocorrelation analysis and MGWR model to explore the spatial heterogeneity and driving mechanisms of the environmental injustice on ESDR. Results showed that most counties in mega cities were suffering from ES shortages clustered in the city centers. Natural and socio-economic factors including temperature, precipitation, NDVI, housing price and vulnerable group were significantly correlated with the spatial heterogeneity of ESDR. Finally, we discuss the possible intervention policies for discision-makers to mitigate server environmental injustice caused by the spatial imbalance of ESDR. [ABSTRACT FROM AUTHOR]

Forest managers need inventory data and information to address sustainability concerns over extended temporal horizons. In situ information is usually derived from field data and computed using appropriate equations. Nonetheless, fieldwork is time-consuming and costly. Thus, new technologies like Light Detection and Ranging (LiDAR) have emerged as an alternative method for forest assessment. In this study, we evaluated the accuracy of geostatistical methods in predicting the Site Index (SI) using LiDAR metrics as auxiliary variables. Since primary variables, which were obtained from forestry inventory data, were used to calculate the SI, secondary variables obtained from LiDAR surveying were considered and multivariate kriging techniques were tested. The ordinary cokriging (CK) method outperformed the simple cokriging (SK) and Inverse Distance Weighted (IDW) methods, which was interpolated using only the primary variable. Aside from having fewer SI sample points, CK was proven to be a trustworthy interpolation method, minimizing interpolation errors due to the highly correlated auxiliary variables, highlighting the significance of the data's spatial structure and autocorrelation in predicting forest stand attributes, such as the SI. CK increased the SI prediction accuracy by 36.6% for eucalyptus, 62% for maritime pine, 72% for pedunculate oak, and 43% for cork oak compared to IDW, outperforming this interpolation approach. Although cokriging modeling is challenging, it is an appealing alternative to non-spatial statistics for improving forest management sustainability since the results are unbiased and trustworthy, making the effort worthwhile when dense secondary variables are available. [ABSTRACT FROM AUTHOR]

The paper aims to analyse a statistical procedure for the definition of territorial indicators, such as the biometric function of life expectancy of citizens of a territory e0, applying a methodology of Top–Down spatial disaggregation, using census data from 2011 in Italy. Through spatial regressions with the methodology proposed in 1971 by Chow and Lin with the use of ISTAT elaborations of annual mortality tables, which are structured from the national level to the regional level, up to the smallest details of the main level, as a dependent variable and predictors a number k of census variables plus accidents in regression models, life expectancy can also be defined at municipal levels (not elaborated by ISTAT) and even at sub-municipal levels (Census Area). The structure of the 2011 census was characterized by 152 variables, collected with CAPI and universal CAWI survey on all the survey units, throughout the national territory, divided into administrative areas of competence and 402,677 more granular areas in census sections. This structure represents a very relevant and useful information asset for applying a spatial disaggregation of indicators, based on three fundamental assumptions: Structural similarity, whereby the aggregate model and the disaggregate model are structurally similar, i.e., the relationships between the variables are valid both at the aggregate (Top) and at the disaggregate (Down) level, with the consequence that the regression parameters are the same in the two models. Error similarity: for spatially correlated errors they present the same structure at both aggregate (Top) and disaggregate (Down) levels, significantly testing for zero spatial correlation; Reliable indicators: the variables in the regression models are efficient predictors at both aggregates (Top) and disaggregate (Down) levels, estimable from model efficiency tests. As we see in the following, compared to others, the best predictors of the census and income variables show us a good interaction in terms of active regressors on the estimation variable. [ABSTRACT FROM AUTHOR]

The emergence of lumpy skin disease (LSD) among cattle in India is concerning. District-level data on LSD cases in Uttar Pradesh between 2021 and 2022 were analyzed. A stepwise spatial analytical approach was followed by first constructing yearly and monthly disease maps for LSD incidence rates (IRs), then spatially interpolating the LSD IRs, followed by evaluating the global and local clustering of LSD IRs and finally conducting spatial regression modeling. Overall, 5784 LSD cases from 6 districts and 112,226 cases from 33 districts were detected in 2021 and 2022, respectively. In the incremental spatial autocorrelation analysis, the highest global clustering of LSD IRs for the 2022 outbreak was detected at 196.49 km. For the 2021 LSD outbreak, one district with high-low and nine districts with low-high LSD IRs were identified in the eastern region of the state. For the 2022 LSD outbreak, 13 districts with high-high and 7 districts with low-high LSD IRs were identified in the western part of the state. A geographically weighted regression model identified the impact of climate (temperature and humidity) and land cover (pasture, fallow, and non-agricultural land) on LSD IRs. The study results can aid animal health authorities in developing LSD prevention and control programs. [ABSTRACT FROM AUTHOR]