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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.

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

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]

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]

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 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]

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]

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]

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]

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]

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]

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]

Copyright of Revista Ciência & Saúde Coletiva is the property of Associacao Brasileira de Pos-Graduacao em Saude Coletiva 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 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]

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]

Background To analyse the temporal trends and spatiotemporal distribution of leprosy relapse in Brazil from 2001 to 2021. Methods An ecological study with a temporal trend approach and space–time analysis of leprosy relapse in Brazil was carried out with data from the Notifiable Diseases Information System. Results A total of 31 334 patients who experienced leprosy relapse were identified. The number of recurrent cases tended to increase throughout the study period, and this increase was significant among females and in almost all age groups, except for those <15, 50–59 and ≥70 y. Several clusters of high- and low-risk patients were identified across all regions with a heterogeneous distribution. Conclusions The burden of relapse showed an increasing trend in some groups and was distributed in all regions. [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]

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]

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]

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]

Objective: To assess the evolution of the COVID-19 pandemic in Brazil and its macro-regions, considering disease incidence and mortality rates, as well as identifying territories with still rising disease indices and evaluating vaccine coverage and population adherence to COVID-19 immunization. Methods: An ecological study conducted in Brazil with COVID-19 cases and deaths reported between February 2020 and April 2024, obtained through the Coronavirus Panel. Historical series were constructed from incidence and mortality rates to assess the pandemic's evolution, and temporal trends were estimated using the Seasonal Trend Decomposition using Loess (STL) method. The Spatial Variation in Temporal Trends (SVTT) technique was employed to identify clusters with significant variations in temporal trends. Vaccination was analyzed considering the percentage of vaccinated and unvaccinated population in each municipality of the country. Results: Brazil recorded a total of 38,795,966 cases and 712,038 deaths from COVID-19 during the study period. Incidence and mortality rates showed three waves of the disease, with a fourth wave of smaller amplitude. Four clusters with significant case growth and two with increased deaths were identified. Vaccine coverage varied among municipalities, with some regions showing low vaccination rates and others with high immunization adherence. Conclusion: The study provided a comprehensive overview of coronavirus behavior in Brazil, and its results highlight the ongoing importance of vaccination and the need to direct efforts and resources to areas of higher risk. [ABSTRACT FROM AUTHOR]

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco 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.)

Educational institutions in Poland often struggle with various problems, such as the lack of an adequate number of rooms or the poor technical condition of buildings. This is due to many factors, such as the age of the buildings, demographic trends, migration, political, social, historical, and cultural conditions and, above all, financial conditions. In order to address these problems, the Krakow University of Technology undertook the implementation of the "Green Classroom" scientific project as part of the "Science for Society" programme of the Ministry of Education and Science. The Green Classroom is a mobile, free-standing educational facility consisting of a geometric arrangement of four basic modules. Integrating this type of facility into existing infrastructure, especially in urban areas, requires the availability of suitable land, taking into account hydro-meteorological and wastewater conditions. This study presents a method using geographic information system (GIS) tools to select school areas where it is possible to locate "Green Classrooms", taking into account sustainable land retention. Based on typical rainfall for the city of Krakow, stormwater runoff was calculated taking into account the adopted "Green Classroom" module. An additional sealed surface (a "Green Classroom" system) increases the rainwater runoff by approximately 1 m3. In order to balance the rainwater runoff, it is recommended to install a rainwater collection tank with a capacity of 1 m3 next to the "Green Classroom" module. In order to relieve the storm sewer system, especially in highly sealed urban areas, it is recommended to use aboveground or underground stormwater tanks. The size of the tanks should depend on the impervious surfaces and their number on the site conditions. Nomograms for the city of Krakow have been developed to estimate the size of tanks. [ABSTRACT FROM AUTHOR]

Flood hazard is one of America's most frequent and expensive natural hazards and causes enormous economic losses in the United States every year. Flood hazards disproportionately affect marginalized and socioeconomically disadvantaged populations. This disproportionate flood exposure constitutes a form of environmental injustice. Few studies have undertaken a large-scale assessment of the long-term change of flood exposure. To fill this gap, this study utilized land use and flood zone data from 2001 to 2019 at a 5-year interval to analyze spatiotemporal changes in flood exposure in the Contiguous United States (CONUS). Two indices, the Deviational Exposure Index and Socioeconomic Disparity Index, were introduced to measure flood exposure and the socioeconomic disparities associated with flood exposure. At the national level, the overall flood exposure in the CONUS decreased in the past two decades, indicating increasing awareness of flood risk in the country. But the local variations of flood exposure and its changing trends vary among communities. In general, coastal and riverine counties show a general avoidance of developing urban areas in flood zones, while inland counties show an opposite tendency of urban development in floodplains. The results of this study reveal socioeconomic and demographic disparities between communities in and out of flood zones and evaluate environmental injustice among disadvantaged populations. The knowledge learned from this study can not only help address environmental justice issues but also benefit the decision-making of the federal government and local authorities in urban development and smart growth when faced with flood risk. [ABSTRACT FROM AUTHOR]

Introduction: This study sought to analyze the relationships between cutaneous leishmaniasis and its epidemiological, environmental and socioeconomic conditions, in the 22 microregions of Pará state, Brazil, for the period from 2017 to 2022. Methodology: In this ecological and exploratory study, the microregions were used as spatial units because they are formed by contiguous municipalities with similar characteristics. The epidemiological, environmental, socioeconomic, and public health policy data employed were obtained from the official information systems at the Ministry of Health, National Institute for Space Research, and Brazilian Institute of Geography and Statistics. A fuzzy system was developed to identify risk factors for the disease, using Python programming language. The results were analyzed with the bivariate Global Moran spatial analysis technique. Results: It was observed that the Altamira microregion had the highest risk percentage for the disease, while Breves had the lowest, with significant differences in the relevance of its conditioning factors, mainly related to land use and cover patterns, in addition to demography and living conditions index, education and public health policies. Conclusions: The fuzzy system associated with the geostatistical technique was satisfactory for identifying areas with health vulnerability gradients related to deforestation, pasture, poverty, illiteracy, and health services coverage, as its conditioning variables. Thus, it was demonstrated that deforestation was the main risk factor for the disease. The system can also be used in environmental and epidemiological surveillance. [ABSTRACT FROM AUTHOR]

Background: Tuberculosis is one of the major burdens to developing nations. India is among the countries categorized by the World Health Organization (WHO) as experiencing a significant burden of TB, HIV-associated TB, and MDR/RR-TB within the context of low- and middle-income nations. Globally South East Asian and African region alone accounted for 82% of death (HIV negative), where India accounts for 36% of TB fatalities. Materials and Methods: The data was extracted from NIKSHAY web portal on TB notification cases in UP, India. The random effects meta-analysis was performed to estimate pooled proportion of TB cases in public and private notified during both the periods. Spatial analysis was used to display the spread of TB cases across the state, during both the periods. Results: A total 75 districts data on TB notified cases were available for current investigation. The pooled proportion of TB cases were 0.24 (0.21, 0.27) and 0.76 (0.73, 0.79) during COVID-19 period for private and public hospitals, respectively. Similarly, the pooled proportion of TB cases were 0.26 (0.23, 0.29) and 0.74 (0.71, 0.77) post-COVID-19 for private and public hospitals, respectively. Conclusion: The proportion of TB cases were more in public hospital as compared to private hospitals during COVID-19 period. Similarly, result was obtained in post-COVID-19 period. [ABSTRACT FROM AUTHOR]

This study introduced the application of concepts and methods from extreme value theory (EVT) to estimate the probability that daily minimum temperatures exceed springtime critical temperature thresholds for Pinot noir buds and young shoots as a function of springtime phenology. The springtime frost risk estimates were computed spatially for Pinot noir throughout the Willamette Valley (WV) American Viticultural Area (AVA) using a gridded dataset of historical daily minimum surface air temperature data. EVT-based springtime frost risk maps can inform vineyard-management operations by identifying those locations throughout a wine region with a low risk for any cold injury where remedial action is likely not necessary when there is a forecasted frost event. Frost risk estimates were computed for 1991–2021 and 1991–2022 to examine a potentially changed risk profile for springtime frost events throughout the WV AVA due to the April 2022 advective frost event. The April 2022 advective frost event influenced the risk profile throughout the AVA such that an event of its magnitude is now modelled to occur more frequently. The EVT-based risk analysis can be readily updated each year as new data become available. While spatially varying budbreak calculations facilitated computation of the spring frost risk estimates, the EVT approach profiled in this study does not necessarily depend on potentially uncertain predetermined budbreak date estimates. Gridded maps of extreme daily minimum temperature exceedances, reclassified relative to the springtime phenology critical temperature thresholds for Pinot noir, were readily combined with a ripening potential map to identify optimal areas for vineyard site selection throughout the WV AVA. When simultaneously evaluating Pinot noir ripening potential with springtime frost risk using historical data, the limiting factor for vineyard site selection throughout the WV AVA was frost risk, not ripening potential. The study approach is also applicable for other winegrape-growing regions, assessments of winter freeze risk and summertime heatwaves, and with non-gridded observed temperature datasets. [ABSTRACT FROM AUTHOR]

Exposure to PM2.5 pollution poses substantial health risks, with the precise quantification of exposure being fundamental to understanding the environmental inequalities therein. However, the absence of high-resolution spatiotemporal ambient population data, coupled with an insufficiency of attribute data, impedes a comprehension of the environmental inequality of exposure risks at a fine scale. Within the purview of a conceptual framework that interlinks social strata and citizenship identity with environmental inequality, this study examines the environmental inequality of PM2.5 exposure with a focus on the city of Xi'an. Quantitative metrics of the social strata and citizenship identities of the ambient population are derived from housing price data and mobile phone big data. The fine-scale estimation of PM2.5 concentrations is predicated on the kriging interpolation method and refined by leveraging an advanced dataset. Employing geographically weighted regression models, we examine the environmental inequality pattern at a fine spatial scale. The key findings are threefold: (1) the manifestation of environmental inequality in PM2.5 exposure is pronounced among individuals of varying social strata and citizenship identities within our study area, Xi'an; (2) nonlocal residents situated in the northwestern precincts of Xi'an are subject to the most pronounced PM2.5 exposure; and (3) an elevated socioeconomic status is identified as an attenuating factor, capable of averting the deleterious impacts of PM2.5 exposure among nonlocal residents. These findings proffer substantial practical implications for the orchestration of air pollution mitigation strategies and urban planning initiatives. They suggest that addressing the wellbeing of the marginalized underprivileged cohorts, who are environmentally and politically segregated under the extant urban planning policies in China, is of critical importance. [ABSTRACT FROM AUTHOR]

This study aims to provide a precise assessment of the distribution of educational facilities within the Guangdong–Hong Kong–Macao Greater Bay Area, serving as a crucial foundation for managing educational resource allocation and enhancing the quality of educational services. Utilizing a kernel density analysis, global autocorrelation analysis, and geographic detectors, this research systematically analyzes the spatial distribution characteristics and influencing factors of educational facilities in the area. The findings reveal significant geographical disparities in facility distribution with dense clusters in urban centers such as Guangzhou and Shenzhen, and less dense distributions in peripheral areas like Zhongshan and Macau. These facilities exhibit a multi-center cluster pattern with strong spatial autocorrelation, mainly influenced by the population size and economic and urban development levels. The results provide actionable insights for refining educational planning and resource allocation, contributing to the enhancement of educational quality across diverse urban landscapes. [ABSTRACT FROM AUTHOR]

Given the rapid expansion of offshore wind development in the United States (US), the accurate mapping of benthic habitats, specifically surficial sediments, is essential for mitigating potential impacts on these valuable ecosystems. However, offshore wind development has outpaced results from environmental monitoring efforts, compelling stakeholders to rely on a limited set of public geospatial data for conducting impact assessments. The present study therefore sought to develop and evaluate a systematic workflow for generating regional-scale sediment maps using public geospatial data that may pose integration and modeling challenges. To demonstrate this approach, sediment distributions were characterized on the northeastern US continental shelf where offshore wind development has occurred since 2016. Publicly available sediment and bathymetric data in the region were processed using national classification standards and spatial tools, respectively, and integrated using a machine learning algorithm to predict sediment occurrence. Overall, this approach and the generated sediment composite effectively predicted sediment distributions in coastal areas but underperformed in offshore areas where data were either scarce or of poor quality. Despite these shortcomings, this study builds on benthic habitat mapping efforts and highlights the need for regional collaboration to standardize seafloor data collection and sharing activities for supporting offshore wind energy decisions. [ABSTRACT FROM AUTHOR]

Local statistical analysis has long been of interest to social and environmental scientists who analyze geographic data. Research into local spatial statistics experienced a step-change in the mid-1990s, which provided a large class of local statistical methods and models. The local Moran statistic is one commonly used local indicator of spatial association, able to detect both areas of similarity and observations that are very dissimilar from their surroundings. From this, many further local statistics have been developed to characterize spatial clusters and outliers. These statistics have seen limited adoption because they do not sufficiently model the relationships involved in confounded spatial data, where the analyst seeks to understand the local spatial structure of a given outcome variable that is influenced by one or more additional factors. Recent innovations used to do joint multivariate local analysis also do not model this kind of conditional local structure in data. This article provides tools to rigorously characterize confounded local inference and a new and different class of multivariate conditional local Moran statistics that can account for confounding. To do this, we return to the Moran scatterplot as the critical tool for local Moran-style covariance statistics. Extending this concept, a new method is available directly from a "Moran-form" multiple regression. We show the empirical and theoretical properties of this statistic, show how some existing heuristic approaches arise naturally from this framework, and show how the use of conditional inference can change interpretations in an empirical analysis of rent and housing stock in a rapidly changing neighborhood. [ABSTRACT FROM AUTHOR]

Urbanization is rapidly occupying green spaces, making it crucial to understand implicit conflicts between urbanization and greenness. This study proposes an ecological greenness index (EGI) and a comprehensive urbanization index (CUI) and selects Nanjing, a megacity in China, as the study area to research the spatial and temporal evolutionary trends of the EGI and CUI in the context of land use/land cover (LULC) changes from 2000 to 2020. Meanwhile, the conflicts and complex interaction characteristics of the EGI and CUI are discussed from both static and dynamic perspectives, and their driving mechanisms are investigated by combining specific indicators. The results demonstrate that over the past 20 years, LULC in Nanjing was dominated by cultivated land, forest land, and artificial surfaces. The encroachment of artificial surfaces on green space was strengthened, resulting in a decrease in the proportion of cultivated land from 70.09% in 2000 to 58.00% in 2020. The CUI increased at a change rate of 0.6%/year, while the EGI showed significant browning (change rate: −0.23%/year), mainly concentrated within the main urban boundaries. The relationship between the CUI and EGI made the leap from "primary coordination" to "moderate coordination", but there remains a risk of further deterioration of the decoupling relationship between the CUI and ecological pressures. The multi-year average contribution of the CUI to the EGI was 49.45%. Urbanization activities that dominate changes in greenness have changed over time, reflecting the timing of urban conflict management. The results provide important insights for urban ecological health monitoring and management. [ABSTRACT FROM AUTHOR]

Objectives: Although the link between poverty and tuberculosis (TB) is widely recognised, limited studies have investigated the association between neighbourhood factors and TB incidence. Since the factors influencing different episodes of TB might be different, this study focused on the first episode of TB disease (first‐episode TB). Methods: All first episodes in previously linked and geocoded TB notification data from 2007 to 2015 in Cape Town, South Africa, were aggregated at the neighbourhood level and merged with the 2011 census data. We conducted an ecological study to assess the association between neighbourhood incidence of first‐episode TB and neighbourhood factors (total TB burden [all episodes] in the previous year, socioeconomic index, mean household size, mean age, and percentage males) using a negative binomial regression. We also examined the presence of hotspots in neighbourhood TB incidence with the Global Moran's I statistic and assessed spatial dependency in the association between neighbourhood factors and TB incidence using a spatial lag model. Results: The study included 684 neighbourhoods with a median first‐episode TB incidence rate of 114 (IQR: 0–345) per 100,000 people. We found lower neighbourhood socioeconomic index (SEI), higher neighbourhood total TB burden, lower neighbourhood mean household size, and lower neighbourhood mean age were associated with increased neighbourhood first‐episode TB incidence. Our findings revealed a hotspot of first‐episode TB incidence in Cape Town and evidence of spatial dependency in the association between neighbourhood factors and TB incidence. Conclusion: Neighbourhood TB burden and SEI were associated with first‐episode TB incidence, and there was spatial dependency in this association. Our findings can inform targeted interventions to reduce TB in high‐risk neighbourhoods, thereby reducing health disparities and promoting health equity. [ABSTRACT FROM AUTHOR]

Supplemental Digital Content is Available in the Text. County-level prevalences of arthritis and arthritis-attributable pain outcomes have significant spatial clustering patterns, and factors shaping these patterns are different for different outcomes. Research on the geographic distribution of pain and arthritis outcomes, especially at the county level, is limited. This is a high-priority topic, however, given the heterogeneity of subnational and substate regions and the importance of county-level governments in shaping population health. Our study provides the most fine-grained picture to date of the geography of pain in the United States. Combining 2011 Behavioral Risk Factor Surveillance System data with county-level data from the Census and other sources, we examined arthritis and arthritis-attributable joint pain, severe joint pain, and activity limitations in US counties. We used small area estimation to estimate county-level prevalences and spatial analyses to visualize and model these outcomes. Models considering spatial structures show superiority over nonspatial models. Counties with higher prevalences of arthritis and arthritis-related outcomes are mostly clustered in the Deep South and Appalachia, while severe consequences of arthritis are particularly common in counties in the Southwest, Pacific Northwest, Georgia, Florida, and Maine. Net of arthritis, county-level percentages of racial/ethnic minority groups are negatively associated with joint pain prevalence, but positively associated with severe joint pain prevalence. Severe joint pain is also more common in counties with more female individuals, separated or divorced residents, more high school noncompleters, fewer chiropractors, and higher opioid prescribing rates. Activity limitations are more common in counties with higher percentages of uninsured people. Our findings show that different spatial processes shape the distribution of different arthritis-related pain outcomes, which may inform local policies and programs to reduce the risk of arthritis and its consequences. [ABSTRACT FROM AUTHOR]

When the popularity of the populist radical right increases, questions arise. Where are these parties successful? Does the geographic distribution of their support change along with changes in their primary issues? This article analyzes spatial aspects in the support of populist radical right parties in Slovakia and Czechia and their change over time. The spatial distribution was examined using spatial autocorrelation.The results showed that these parties have strong support in "left-behind" regions and peripheral rural regions and weak support in the surroundings of largest cities. The distribution of support for parties also changed more significantly when the parties changed the presented main issues. In Slovakia there was visible spatial competition between the traditional populist radical right party, the Slovak National Party (SNS), and newer and adaptable party, the People's Party Our Slovakia (ĽSNS). ĽSNS succeeded in areas with traditionally strong support for SNS thanks to changes in its main electoral themes. In Czechia, the distribution of support between Dawn and Freedom and Direct Democracy (SPD) has changed. Dawn had a support distribution similar to the populist party Public Affairs, which shared ideas of direct democracy. SPD's support distribution was closer to areas traditionally supporting the populist radical right. [ABSTRACT FROM AUTHOR]

Strategies to address the decline and marginalization of rural areas are emerging as a central issue of European and national policies. Yet, the conceptualization of decline and the recognition of forms of decline specific to distinct rural areas, resulting in conditions of intra-rural divide, are fundamental aspects for developing place-based and targeted strategies. The article thus focuses on the concept of intra-rural divide. It introduces the categories of declining, adapting, stabilizing, and developing regions as spatial clusters that present specific population trends and require specific place-based policies. These policies are defined as adaptation, mitigation, and development strategies. The aim of the study is to develop a procedure, based on a set of indicators of population trends and population structure, to investigate the impact of active decline resulting from out-migration and legacy decline resulting from demographic imbalances produced by past migration. The objective is to understand the intra-rural divide and identify clusters of declining transition and prosperous rural areas. Six Italian regions were selected as study areas, including Abruzzo, Basilicata, Calabria, Campania, Molise, and Puglia. The Municipalities are selected as the unit of analysis. The study's findings provide relevant information for defining targeted policies and balancing adaptation, mitigation, and development strategies. [ABSTRACT FROM AUTHOR]

In this research, the spatial correlation between the variables representing Bouguer gravity anomaly and seismicity in Iran is evaluated. For this purpose, the gravity anomalies and seismicity data of this region have been analyzed statistically as well as geostatistically, for the period 1975-2021. Based on the findings of this study, it can be concluded that the significant correlation observed between the variables of gravity anomaly variations and the seismicity-related variables, especially the variables related to the frequency of earthquake occurrences, suggests that the gravity anomaly variations can be considered as an affective factor in seismic activity of this region. The Iranian Plateau is one of the most seismically active regions on the Earth because of its geologic and tectonic setting. This plateau is marked by high topography relief and also by great changes in gravitational and isostatic anomalies across it. Many researchers have studied the variations of gravitational anomalies across Iran, and some have pointed to the relationships between these anomalies and seismicity in this region. The Bouguer gravity anomaly is obtained by making the necessary corrections to measurements taken directly from the ground stations, and well reflects the deep density variations in the crust. This anomaly can also clearly show changes in crustal thickness in different regions, such that areas with high crustal thicknesses show negative anomalies and areas with low crustal thicknesses show positive anomalies. In this study, at first, the study region was divided into rectangles with dimensions of 0.5 by 0.5 geographical degrees and then the variables related to the seismicity and gravity anomalies were calculated and computed for each cell. Pearson correlation coefficients between these variables were computed and validated using statistical software Minitab (ver. 16.2.2). Also, maps representing the spatial distribution pattern of these variables were prepared. The remarkable similarity between the spatial patterns of variations of these variables indicates a strong correlation between the Bouguer gravity anomaly and seismicity in this region. The Pearson correlation coefficient values calculated between the variables also confirm this correlation. These values indicate that both variables of average Bouguer anomaly and the range of variations of this anomaly show a significant positive correlation with the seismicity-related variables. This degree of correlation is stronger for the variable of the Bouguer anomaly variation and moreover, this variable is more correlated with the seismicity variables associated with the frequency of earthquakes. In the next step, variograms were prepared. The results obtained show that among the seismicity-related variables of the region, the b seismicity parameter (from the Gutenberg-Richter relation) has more spatial variability and show high spatial autocorrelation up to long distances. On the other hand, the other variables related to earthquake frequency and magnitude of earthquakes show less spatial autocorrelation. The variograms provided for the two variables representing the bouguer anomaly also show remarkable similarity to the seismicityrelated diagrams. This similarity is more pronounced for the variable of the Bouguer anomaly variation. The remarkable similarities of the variograms, along with the similarities of the spatial distribution maps of these variables, may indicate a close relationship between these two series of variables. Finally, it can be concluded that gravitational forces, especially forces caused by isostatic imbalances, can play an important role in the process of earthquake occurrences in Iran. [ABSTRACT FROM AUTHOR]

Using a dynamic spatial panel model applied to 377 US Metropolitan Statistical Areas (MSAs), estimated over the period 2011-2021, significant differences are found between large MSAs regarding the relationship between labour productivity and economic mass, as measured by GDP. The methodology adopted illustrates the state of the art for spatial econometric modeling as it is often needed in practice, allowing for multiple endogenous regressors and dynamic effects. The estimation method applies synthetic instruments designed to limit negative effects of instrument overabundance. [ABSTRACT FROM AUTHOR]

Background: Spatial epidemiology has emerged as an important subfield of epidemiology over the past quarter century. We trace the origins of spatial epidemiology and note that its emergence coincided with technological developments in spatial statistics and geography. We hypothesize that spatial epidemiology makes important contributions to descriptive epidemiology and analytic risk-factor studies but is not yet aligned with epidemiology’s current focus on causal inference and intervention. Methods: We conducted a systematic review of studies indexed in PubMed that used the term “spatial epidemiolog*” in the title, abstract, or keywords. Excluded articles were not written in English, examined disease in animals, or reported biologic pathogen distribution only. We coded the included papers into five categories (review, demonstration of method, descriptive, analytic, and intervention) and recorded the unit of analysis (i.e., individual vs. ecological). We additionally examined articles coded as analytic ecologic studies using scales for lexical content. Results: A total of 482 articles met the inclusion criteria, including 76 reviews, 117 demonstrations of methods, 122 descriptive studies, 167 analytic studies, and 0 intervention studies. Demonstration studies were most common from 2006 to 2014, and analytic studies were most common after 2015. Among the analytic ecologic studies, those published in later years used more terms relevant to spatial statistics (incidence rate ratio =1.3; 95% confidence interval [CI] = 1.1, 1.5) and causal inference (incidence rate ratio =1.1; 95% CI = 1.1, 1.2). Conclusions: Spatial epidemiology is an important and growing subfield of epidemiology. We suggest a re-orientation to help align its practice with the goals of contemporary epidemiology. [ABSTRACT FROM AUTHOR]

The impact of climate change is devastating in developing countries where flood protection and insurance schemes are limited. Certain parts of Ho Chi Minh City, Vietnam are under the constant threat of inundation due to sea-level rise. We integrate the hedonic property model in a difference-in-differences framework and spatial econometric analysis into a single analytical framework to estimate the economic effect of pluvial flooding. We find prices for affected houses were discounted by 9% after a large flood event on 30 September 2017. This research contributes to the existing literature as follows. First, we study the economic impact of pluvial floods, which has received less attention in existing studies where large and irregular floods are their focus. Second, the inclusion of legal status as a control variable accounts for the unique character of the Vietnamese housing market. Third, we also identify the recovery of house prices after the flood event. [ABSTRACT FROM AUTHOR]

Understanding the distribution of tegumentary leishmaniasis (TL) in different periods enables the adequate conduction of actions at the public health level. The present study analyzes the spatiotemporal evolution of TL incidence rates in the municipalities of Brazil and identifies priority areas from 2001 to 2020. Notifications of new cases were analyzed employing space-time scan statistics and Local Indicators of Spatial Association. As TL incidence rates presented a downward trend in most Brazilian municipalities, spatiotemporal clusters of high relative risks (RR) were more frequent in the first decade of the series. There was a concentration of those clusters in the North and Northeast regions, mainly in the Legal Amazon area. More recent high-RR areas were identified in municipalities of different regions. The number of priority municipalities showed a stable trend in Brazil. There was a great concentration of such municipalities in the states of Acre, Mato Grosso, Rondônia, Pará, and Amapá, as well as large areas in Roraima, Amazonas, Maranhão, and Tocantins, and smaller areas in the states of Goiás, Ceará, Bahia, Minas Gerais, São Paulo, and Paraná. The present study contributes to the understanding of the historical evolution of TL in Brazil and subsidizes actions to combat the disease. [ABSTRACT FROM AUTHOR]

N atural hazards are a part of critical issues affecting people and the environment. One of these natural hazards is snow avalanches. With the increase in the world population, it has emerged that decision-makers should take precautions against such natural hazards for population movements, construction, transportation, and tourism. Essential solution parts of this problem lay behind surveying, GIS, and spatial analysis-planning. This situation will be primarily due to the snow conditions, but certain terrain areas are susceptible. Snow avalanches' release mechanism depends on many factors, such as terrain, meteorological reports, snowpack, and other triggering parameters. Areas with certain topographical features that allow the deposition of snow masses are called avalanche-release areas. GIS helps to make decisions concerning spatial planning within avalanche release areas and finding risky zones. This study aimed to determine the potential avalanche release areas in the GIS environment in Rize, Türkiye, which was chosen as the pilot region. In the study, the detection of these avalanche areas was estimated using a mathematical equation model proposed by Hreško (1998) and determined with the help of GIS. Factors such as elevation, curvature, aspect, slope, and land cover type were used to estimate avalanche risk areas. A Model Builder workflow has also been created to automate the process stages. As a result of the study, avalanche risk areas were determined and mapped for the Rize mountainous region. [ABSTRACT FROM AUTHOR]