Search

INTRODUCTION: Adverse social determinants of health have been shown to be associated with a greater chance of developing chronic conditions. Although there has been increased focus on screening for health-related social needs (HRSNs) in health care delivery systems, it is seldom examined if the provision of needed services to address HRSNs is sufficiently available in communities where patients reside. METHODS: The authors used geospatial analysis to determine how well a newly formed health system and community-based organizations (CBOs) social care coordination network covered the areas in which a high number of patients experiencing HRSNs live. Geospatial clusters (hotspots) were constructed for Kaiser Permanente Northwest members experiencing any of the following 4 HRSNs: transportation needs, housing instability, food insecurity, or financial strain. Next, a geospatial polygon was calculated indicating whether a member could reach a social care provider within 30 minutes of travel time. RESULTS: A total of 185,535 Kaiser Permanente Northwest members completed a HRSN screener between April 2022 and April 2023. Overall, the authors found that among Kaiser Permanente Northwest members experiencing any of the 4 HRSNs, 97% to 98% of them were within 30 minutes of a social care provider. A small percentage of members who lived greater than 30 minutes to a social care provider were primarily located in rural areas. DISCUSSION AND CONCLUSION: This study demonstrates the importance of health system and community-based organization partnerships and investment in community resources to develop social care coordination networks, as well as how patient-level HRSN can be used to assess the coverage and representativeness of the network.

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.

AbstractMost implementations of Bayesian additive regression trees (BART) one-hot encode categorical predictors, replacing each one with several binary indicators, one for every level or category. Regression trees built with these indicators partition the discrete set of categorical levels by repeatedly removing one level at a time. Unfortunately, the vast majority of partitions cannot be built with this strategy, severely limiting BART’s ability to partially pool data across groups of levels. Motivated by analyses of baseball data and neighborhood-level crime dynamics, we overcame this limitation by re-implementing BART with regression trees that can assign multiple levels to both branches of a decision tree node. To model spatial data aggregated into small regions, we further proposed a new decision rule prior that creates spatially contiguous regions by deleting a random edge from a random spanning tree of a suitably defined network. Our re-implementation, which is available in the flexBART package, often yields improved out-of-sample predictive performance and scales better to larger datasets than existing implementations of BART. [ABSTRACT FROM AUTHOR]

Integrating spatial inequality perspectives in strategic decision-making can ensure positive impacts on resource distribution for public welfare and sustainable development. This study aims to apply evidence-based approaches in deploying permanent deacons. The empirical case study has been conducted at the St Helens denary of the Liverpool archdiocese, UK. Assisting with charitable works is one of three served areas by the Roman Catholic Church facilitated by deacons. The deployment of permanent deacons could benefit from being evidence-based so that a deacon can serve to ease the socio-economic (e.g., population density, long-term health conditions, housing system, employment status, education level, social status) inequality in the most deprived area. We used geographic information system (GIS) based algorithms, Getis-Ord Gi* for hot spot analysis to find the clustered area by considering the socio-economic factors. The open/freely available government census dataset was found to help extract socio-economic parameters. Furthermore, a GIS-based multi-criteria assessment technique was conducted by applying map algebra (raster calculator) to identify the deprived area with ranks considering multiple socio-economic conditions, where served areas by the existing deacons were considered to constrain. The served areas were estimated by applying network analysis where OpenStreetMap and location existing deacons were used as input. Our empirical case study identified the central and northern parts of the deanery as the most and least deprived areas, respectively. Finally, Liverpool archdiocese could consider deploying new permanent deacons in St Helens denary based on suggested deprivation ranks. Therefore, the appropriate number of deacons in the deprived areas can quickly and effectively respond to the needy and enhance communities' resilience and sustainable development by ensuring proportionate resource distribution. [ABSTRACT FROM AUTHOR]

Limited healthcare availability impacts population health. Regional disparities in GP density across Germany raise questions about their association with regional socioeconomic characteristics. This longitudinal nationwide ecological German study used regional data at the county level (n = 401) from 2015 to 2019 provided by the Federal Institute for Research on Building, Urban Affairs and Spatial Development (BBSR). The outcome was general practitioners (GPs) density, defined as the number of GPs per 10,000 inhabitants. Univariate Moran's I, cluster analysis (LISA), and spatial lag of X (SLX) models were employed to analyse the spatial distribution of GP density and its correlation with various regional socioeconomic characteristics from a cross-sectional and longitudinal perspective. In contrast to the univariate analysis, rural counties showed the highest GP density the multivariate model. Several counties were identified as embedded in low- or high-GP-density clusters. In 2015 and 2019, larger household size (2015: std. β = −2.31, p = 0.021; 2019: std. β = −4.14, p < 0.001) and higher unemployment rate (2015: std. β = −2.84, p = 0.005; 2019: std. β = −5.47, p < 0.001) were associated with lower GP density. In the longitudinal model, a greater increase in the unemployment rate was related to a greater decrease in GP density (std. β = −2.17, p = 0.030). A higher regional unemployment rate is linked to lower GP availability in Germany, and a greater increase in the unemployment rate was related to a greater decrease in GP availability over time. This necessitates policy intervention to avoid socioeconomic disparities in GP care. [ABSTRACT FROM AUTHOR]

Introduction: Following the legalisation of non‐medical cannabis in 2018, the number of cannabis stores in Canada has rapidly expanded with limited regulation on their geographic placement. This study characterised the clustering of cannabis stores in Canadian cities and evaluated the association of clustering with provincial policy and sociodemographic variables. Methods: Cross‐sectional spatial analysis of cannabis store density in dissemination areas ('neighbourhoods', n = 39,226) in Canadian cities in September 2022. Cannabis store density was defined as the count of stores within 1000 m of a neighbourhood centre. Clusters of high‐density cannabis retail were identified using Local Indicators of Spatial Autocorrelation. Associations between provincial policy (privatised vs. public market), sociodemographic variables and cannabis store density were evaluated using multivariable regression. Results: Clusters of high‐density cannabis retail were identified in 86% of Canadian cities, and neighbourhoods in clusters had a median of 5 stores within 1000 m. Toronto, Canada's most populous city, had the most extreme clustering where neighbourhoods in clusters had a median of 10 stores (and a maximum of 25 stores) within 1000 m. Neighbourhoods in private versus public retail markets had a significantly higher neighbourhood‐level density of cannabis stores (adjusted rate ratio [aRR] 63.37, 95% confidence interval [CI] 25.66–156.33). Lower neighbourhood income quintile was also associated with a higher neighbourhood‐level density of cannabis stores (Q5 vs. Q1, aRR 1.28, 95% CI 1.17–1.40). Discussion and Conclusions: Since cannabis was legalised, clusters of high‐density cannabis retail have emerged in most Canadian cities and were more likely to form lower income neighbourhoods and in private retail markets. [ABSTRACT FROM AUTHOR]

This paper investigates the role of municipal and provincial public social spending for local income inequality after taxes and transfers in Austria. We utilize a spatial multi‐level model, which allows us to analyze the contribution of three spatial scales (municipal, district, and provincial level) to municipal income inequality. Our analysis shows that the effect of public social spending on local Gini indices does not only differ across provinces but also across municipalities which indicates that the potential cushioning effect of social expenditure is highly localized. Further splitting total public social expenditure into three distinct categories (education, health, social protection) reveals that spending on social protection has the highest effect on local inequality across all provinces, while health spending does not exert a discernible influence in any province. The method and results presented in this paper are of international interest for policymakers and researchers who aim to investigate whether the same patterns hold true in other countries. [ABSTRACT FROM AUTHOR]

While conflict pervades virtually every aspect of society, there have been relatively few crossovers between Regional Science and Peace Science. This paper pays tribute to Walter Isard, pioneer of peace science research, and Kieran Donaghy, who has contributed to research on arms race dynamics and macroeconomic stability. We review studies that examine the impact of (i) trade on conflict, (ii) the economy on militarized disputes, and (iii) conflict on economic development. The analysis develops a structural equation model (SEM) to test the hypotheses simultaneously using the Correlates of War project data. Consistent with the liberal proposition, we found that the pacifying effect of trade is robust across alternative SEM specifications. Controlling for standard explanatory variables, the SEM estimates reveal that the indirect impact of economic development on conflict mediated by trade is statistically significant at the 0.1-percent level. Trade is, therefore, a critical intervening variable that transforms conflict-inducing economic expansion into a pacifying influence on militarized disputes. The spatial version of the SEM confirms that democracies do not attack each other. While trade does not appear to have a local spillover effect on conflict, proximity to neighboring democracies does lead to fewer conflicts. The final hypothesis argues that conflict affects national economic performance, which Donaghy refuted in his 1995 Conflict Management and Peace Science paper. Consistent with Donaghy's finding, the estimates reject the claim that conflict disrupts economic stability. The conclusion section discusses the implications for Kieran Donaghy's larger body of work. [ABSTRACT FROM AUTHOR]

Reports of child maltreatment vary by neighborhood characteristics, yet the influence of housing affordability is less understood. The current study examines the relationship between reports of suspected child maltreatment and the spatial distribution of affordable housing across 2,341 census tracts in Los Angeles County, California. Bayesian conditionally autoregressive model results indicate that neighborhoods where residents paid a greater share of their income in rent had fewer reports of suspected child maltreatment, while neighborhoods with a higher number of subsidized rental units had more reports. These findings suggest that higher cost neighborhoods provide supportive resources and amenities to families which may reduce risk of child maltreatment. Subsidized housing units are more likely to be located in high poverty, under resourced neighborhoods, thus undermining the benefits of these programs to families. These findings have implications for equitable housing policy that promotes inclusive communities as a primary prevention strategy for child maltreatment. [ABSTRACT FROM AUTHOR]

The study explores the dynamics of public transport regulation, deregulation, and reform within the context of Tver in Russia. As developing nations face increased transportation demand and industrialized regions grapple with environmental concerns policymakers are challenged to choose appropriate regulatory frameworks. Historically, different stages of economic development have led to diverse approaches, ranging from publicly owned enterprises to natural monopoly regulation. Primary objective of the study is to conduct a spatial analysis of the new public transport route network in Tver and its suburb, examining alterations, accessibility, and travel times. To achieve this, methodologies, and data parameters such as Geographic Information System (GIS) tools for spatial analysis, creation of buffer zones around public transport stops to assess accessibility, population coverage analysis to understand the demographic impact, and isochrones mapping to evaluate changes in travel times and accessibility were employed. These methods allowed for a detailed assessment of the network's alterations, accessibility, and the impact on travel times following the transfer of planning authority. Additionally, population access to transport services was assessed through demographic coverage analysis, determining the proportion of the population within a designated distance from stops. These quantifications provided insights into the effects of transferring planning authority on the regional public transport system's accessibility and efficiency. Tver's experience illustrates the potential outcomes of transport reform, with intracity and intraregional routes merged into a unified passenger transportation system. This transformation holds promise for regional development. In summary, this study offers insights into public transport regulation and reform dynamics, using the Tver case to analyze the consequences for accessibility and service quality. [ABSTRACT FROM AUTHOR]

Leptospirosis and enteric fever are prevalent tropical acute bacterial febrile illnesses in Kelantan, Malaysia, that exhibit overlapping features and shared transmission dynamics, yet their spatial relationship remains understudied. This study aimed to analyze their spatial distribution, investigating potential interactions and intersecting patterns. A total of 212 laboratory-confirmed cases of enteric fever and 1106 of leptospirosis between 2016 and 2022, were retrieved from the national e-Notifikasi registry. Point pattern analysis revealed clustering of both diseases in the northern region, but leptospirosis was predominant in the south, exhibiting higher spatial risk. Seven co-infection cases were identified in overlapping hotspot areas. Spatial dependence between the diseases was identified within 4 km distance on average, with varying patterns over time and regions. Recognizing spatial dependence has implications for accurate diagnosis, timely intervention, and tailored public health strategies. The findings underscore the need for multi-disease interventions to address shared risk factors and co-infections in similar geographical contexts. [ABSTRACT FROM AUTHOR]

The Yuan River Basin in Hunan Province, known for its rich history and cultural heritage, is home to traditional village landscapes characterized by distinct cultural genes and regional features. This study focuses on 21 traditional villages in Xupu County, Huaihua, and develops four types of traditional village landscape cultural genome atlases at three hierarchical levels: individual villages, multiple villages, and a specific research area. These atlases include genetic linkage maps, spatial sequence maps, distribution pattern maps, and geographical pattern maps. Using Yangquepo in Zhumucun Village as a case study, we constructed a genetic linkage map for the landscape cultural genome of a single traditional village, based on the arrangement structure of courtyard layouts. Spatial sequence maps were derived from the overall spatial configuration, while distribution pattern maps and geographical pattern maps for the 21 villages were developed using kernel density analysis in ArcGIS 10.8 software. The results reveal that traditional village landscapes in the region possess a diverse cultural genome, marked by distinct regional characteristics and spatial organization patterns. [ABSTRACT FROM AUTHOR]

This study examined the decline of traditional villages due to urbanization, focusing on their spatial patterns and architectural characteristics in China, particularly in the Guanzhong region. Using ArcGIS tools, kernel density and nearest-neighbor analyses quantitatively assessed the spatial distribution of these villages at macro- and micro-levels. Additionally, 3D laser scanning was employed to qualitatively analyze architectural features. The study demonstrated that (1) traditional villages are unevenly clustered nationwide, primarily in the southeast and southwest, creating a "three cores and multiple points" spatial pattern. (2) In the Guanzhong region, traditional village distribution also shows clustering with diverse patterns, including regiment, belt, and point formations. Higher densities are found in the eastern and northern regions, while the west and south are sparsely populated. Most villages are located at altitudes of 501–700 m, on slopes of 6–15°, and near water sources. (3) The basic residential structures in Guanzhong included the single, vertical multi-entry, and horizontal coupled courtyards, as well as the vertical and horizontal interleaved layouts; these buildings typically featured the foundations and walls made of earth, stone, and brick, combined with various wooden frames and single- or double-sloped roofs. This study overcomes the limitations of the traditional literature and field surveys by quantitatively and qualitatively analyzing the spatial patterns of traditional villages and the architectural forms of residential buildings from an architectural perspective. It graphically presents the data to provide an efficient and practical theoretical basis for the heritage preservation and development of traditional villages. [ABSTRACT FROM AUTHOR]

Understanding the fine‐scale spatial distribution of heavy metal contamination is crucial for effective environmental capacity control and targeted treatment of polluted areas. This article presents the latest dataset on the occurrence of common heavy metals in the soils of the Yangtze River Basin. The dataset was compiled by reviewing peer‐reviewed literature published between 2000 and 2020. Rigorous quality control procedures were employed to ensure the accuracy of the data, including the extraction of detailed geographic locations and concentrations of heavy metals. The dataset includes 7867 records of heavy metal occurrences (Zn: 1045, Cu: 1140, Pb: 1261, Cr: 980, Cd: 1242, Ni: 649, As: 821, Hg: 729) in the soils of the Yangtze River Basin, distributed at four scale levels: province, prefecture, county, and township or finer. The results indicate that the distribution of heavy metal concentrations is relatively scattered, with higher concentrations in cities and regions with developed industry and agriculture. Cd has the highest exceedance rate (33.90%), indicating significant local contamination. Heavy metals, such as Zn at 11.96%, Ni at 12.63%, and As at 9.74%, also exceeded standard levels at certain sampling points. Cr had the lowest exceedance rate of 1.33%. This updated dataset provides essential information on the current status of heavy metals contamination in the soils of the Yangtze River Basin. It can be used for further ecological and health risk assessments and for developing strategies to remediate and prevent heavy metal contamination in the region. [ABSTRACT FROM AUTHOR]

Across countries, mounting housing pressures contribute to a growing number of young adults living in the parental home. Patterns at the micro‐level and cross‐nationally are well charted, but less is known about intra‐country differences. Drawing on the case of the Netherlands, we use full‐population register data to examine co‐residence patterns of 25–34‐year‐olds for the 2005–2020 period. Through descriptive, GIS and multivariate analyses, we explain patterns in co‐residence according to income, across space and over time. Results reveal substantial spatial differences in patterns of co‐residence and rates of growth, with the strongest increases in the largest cities and directly adjacent regions. Patterns are most pronounced and intensified for low‐income young adults, who increasingly struggle to realise residential independence in and around economic pull regions and high‐priced urban areas. These findings point to increasing socio‐spatial inequalities in co‐residence, contributing to literature on the interaction between class and space. [ABSTRACT FROM AUTHOR]

Background: Smear-positive TB patients greatly contribute to community-level transmission of this disease. Locating hotspots would make it easier to prioritize and target control interventions. This study aimed to assess the spatial distribution of smear-positive index TB cases and their secondary cases and the predictors of clustering of smear-positive TB cases. Methods: This study was conducted in the Silti Zone of Central Ethiopia from 2020 to 2022. The data of smear-positive index TB patients were collected from the unit TB registries of healthcare facilities. Contacts of all index TB patients were screened in the community and tested to identify secondary TB patients. We performed spatial analysis, including Moran's I statistic, the Getis-Ord Gi* statistic and geographically weighted regression (GWR), to assess the global distribution, local clustering and predictors of clustering of smear-positive TB patients, respectively. Additionally, we used inverse distance weighting (IDW) interpolation to predict the distribution of smear-positive TB cases and develop a continuous raster map for places with no data. Results: Spatial autocorrelation analysis revealed that the distribution of smear-positive TB patients exhibited significant clustering (Moran's I = 0.70029; p value < 0.000). The Getis-Ord Gi* output indicated the presence of statistically significant hotspots as well as cold spots in the study area. Significant hotspots were found in 11 Kebeles of the Silti, Dalocha and Misrak Silti districts. Significant coldspots were also found in five kebeles of the Silti and Misrak districts. GWR analysis revealed that no education, primary education, family size and thatched roof houses were significant predictors of the spatial clustering of smear-positive TB cases. We also found that the majority of the secondary TB cases were found in hotspots identified through spatial analysis. Conclusion: The study revealed a heterogenous distribution of smear positive TB in the study area and it could act as a model that can be replicated in other regions. The identified hotspots of TB could be targeted through location-based interventions such as systematic active screening in the form of outreach programs to improve the performance of TB prevention and control, including reducing the transmission of TB. Educational status, family size and housing type were some of the factors that significantly influenced the spatial distribution of smear-positive TB in the study area. The distribution of secondary TB cases found through household contact screening coincided with the identified hotspots, indicating greater transmission of the disease in these places. [ABSTRACT FROM AUTHOR]

ABSTRACT Advancements in space exploration and computing have accelerated progress in remote sensing studies, where imaging satellites capture extensive datasets globally, particularly in identifying green areas on agricultural lands essential for monitoring natural resources, promoting sustainable agriculture, and mitigating climate change. Large‐volume satellite images from 2020 were obtained from https://tile.kayseri.bel.tr/tilecache/Cache/2020UYDU3857/z/x/y.jpeg. The images are stored on the server address of Kayseri Metropolitan Municipality. Traditional techniques struggle with classifying big data from these satellite views, necessitating innovative approaches like DGAG (Detect Green Areas with Geolocation), a novel method that combines interdisciplinary techniques to detect and geographically delineate green areas on agricultural lands globally. DGAG utilizes map‐based open‐source software to convert large‐scale satellite views into processable images with spatial information, employing segmentation‐based deep learning techniques such as You Only Look Once version 5 (YOLOv5) and Mask Region‐based Convolutional Neural Network (Mask R‐CNN) to extract green areas and determine pixel boundaries. The pixel borders are then transformed into spatial polygon data, providing hectare‐level spatial information. Testing on actual satellite views of Kayseri province yielded promising results, with DGAG YOLOv5 and Mask R‐CNN achieving F1 scores of 0.917 and 0.922, respectively. Notably, DGAG Mask R‐CNN outperformed YOLOv5 by detecting 834626.42 square meters more green area. In terms of runtime, DGAG Mask R‐CNN detected green areas in approximately 0.031 s, while DGAG YOLOv5 operated roughly twice as fast, detecting green areas in about 0.015 s. [ABSTRACT FROM AUTHOR]

Cervical cancer, despite being preventable through primary and secondary prevention strategies, remains one of the leading causes of morbidity and mortality among women in Brazil. This study aimed to analyze the temporal, spatial, and space-time patterns of cervical cancer mortality in Brazil. An ecological study was conducted using temporal, spatial, and space-time analysis techniques, using death certificates with cervical cancer as the underlying cause or associated condition among females in Brazil from 2000 to 2021. Death certificate and population data were provided by the Department of Health Informatics of the Unified Health System (DATASUS) and the Brazilian Institute of Geography and Statistics (IBGE), respectively. A total of 123,306 deaths associated with cervical cancer among women were registered during the study period. A rising trend in mortality was detected since 2014 onwards, after 14 years of decline. Particularly, an increase in mortality was observed among the younger age groups, and in the North and Northeast regions regardless of age. Heterogeneity in the spatial distribution of cervical cancer mortality was observed, with high mortality clusters around the country, but mostly concentrated in the North and Northeast regions. These findings suggest a need and an opportunity to develop efficient and effective health policies targeting those regions and groups of women at higher risk which in turn will allow for fast and significant reductions in cervical cancer mortality in Brazil. [ABSTRACT FROM AUTHOR]

The enteric nervous system (ENS) consists of an extensive network of neurons and glial cells embedded within the wall of the gastrointestinal (GI) tract. Alterations in neuronal distribution and function are strongly associated with GI dysfunction. Current methods for assessing neuronal distribution suffer from undersampling, partly due to challenges associated with imaging and analyzing large tissue areas, and operator bias due to manual analysis. We present the Gut Analysis Toolbox (GAT), an image analysis tool designed for characterization of enteric neurons and their neurochemical coding using two-dimensional images of GI wholemount preparations. GAT is developed in Fiji, has a user-friendly interface, and offers rapid and accurate segmentation via custom deep learning (DL)-based cell segmentation models developed using StarDist, as well as a ganglia segmentation model in deepImageJ. We apply proximal neighborbased spatial analysis to reveal differences in cellular distribution across gut regions using a public dataset. In summary, GAT provides an easy-to-use toolbox to streamline routine image analysis tasks in ENS research. GAT enhances throughput, allowing rapid unbiased analysis of larger tissue areas, multiple neuronal markers and numerous samples. [ABSTRACT FROM AUTHOR]

The Andes region has an exceptional record of high-altitude settlements integrated within widespread regional chains of mobility and exchange. The Sierra de Aconquija (NW Argentina, south-central Andes) is an effective climatic barrier that has afforded an enduring indigenous approach to land use, mobility, and exchange over millennia. Despite this rich history, the Sierra has been largely considered marginal in pre-Columbian regional cultural developments. Today, the expansion of extractive industries threatens the region's heritage and the sustainable futures of local communities. Innovative, integrative methodologies are needed for landscape characterisation, heritage assessment, and sustainable policy development. Building on earlier work, we undertook the first comprehensive mapping of archaeological features over 3800 sq. km of the Sierra using interpreter-led assessment of commercial and open-access satellite imagery and DSM data, to verify earlier assumptions and to identify previously unnoticed trends in the aggregation, distribution, and connectivity of archaeological features. The mapping identified 6794 features distributed unevenly but with clear tendencies towards maximising topographic, ecologic, and connectivity advantages expressed consistently across the study area. The outcomes confirm the important role the Sierra had in pre-Hispanic times, highlighting the significance of ancient indigenous practices for the sustainability of vulnerable upland landscapes both in the Andes and worldwide. [ABSTRACT FROM AUTHOR]

The literature shows that communities under different socio-economic conditions suffer different levels of damage in disasters. In addition to the physical intensity of hazards, such differences are also related to the varying abilities of communities to prepare for and respond to disasters. This study analyzes the spatial patterns of building damage in Hurricane Ian in 2022 and investigates the socio-economic disparities related to the damage. Specifically, this study employs NASA's Damage Proxy Map (DPM2) to analyze spatial patterns of building damage caused by the hurricane. Then, it uses statistical analysis to assess the relationships between building damage and hurricane intensity, building conditions, and socio-economic variables at the building and census tract levels. Furthermore, the study applies geographically weighted regression (GWR) to examine the spatial variation of the damage factors. The results provide valuable insights into the potential factors related to building damage and the spatial variation in the factors. The results also reveal the uneven distribution of building damage among different population groups, implying socio-economic inequalities in disaster adaptation and resilience. Moreover, the study provides actionable information for policymakers, emergency responders, and community leaders in formulating strategies to mitigate the impact of future hurricanes by identifying vulnerable communities and population groups. [ABSTRACT FROM AUTHOR]

Improving spatial access to healthcare facilities is of great interest in urban planning. However, most facility location models fall short of explicitly incorporating the spatial accessibility measure in designing facility locations. Thus, this paper develops a novel framework to directly improve spatial access to healthcare facilities, by integrating spatial analysis with an optimization model. Specifically, by leveraging multiple types of data, this framework could: (1) estimate healthcare demands using machine learning models from large-scale mobile phone data; (2) calibrate the travel time decay effect based on human mobility patterns and road networks; (3) calculate spatial accessibility scores; and (4) optimize the location of hospitals with the consideration of efficiency and equity criteria. To demonstrate the applicability of the proposed framework, we used Erie County in the State of New York as a case study. This study could help urban planners determine the optimal locations to add new hospitals, with the hope of improving equitable access to healthcare services. [ABSTRACT FROM AUTHOR]

Background: Understanding the geographic variation of unintended pregnancy is crucial for informing tailored policies and programs to improve maternal and child health outcomes. Although spatial analyses of unintended pregnancy have been conducted in several developing countries, such research is lacking in India. This study addresses this gap by investigating the geographic distribution and determinants of unintended pregnancy in India. Methods: We analysed data from the National Family Health Survey-5 encompassing 232,920 pregnancies occurring between 2014 and 2021 in India. We conducted a spatial analysis to investigate the distribution of unintended pregnancies at both state and district levels using choropleth maps. To assess spatial autocorrelation, Global Moran's I statistic was employed. Cluster and outlier analysis techniques were then utilized to identify significant clusters of unintended pregnancies across India. Furthermore, we employed Spatial Lag Model (SLM) and Spatial Error Model (SEM) to investigate the factors influencing the occurrence of unintended pregnancies within districts. Results: The national rate of unintended pregnancy in India is approximately 9.1%, but this rate varies significantly between different states and districts of India. The rate exceeded 10% in the states situated in the northern plain such as Haryana, Delhi, Uttar Pradesh, Bihar, and West Bengal, as well as in the Himalayan states of Himachal Pradesh, Uttarakhand, Sikkim, and Arunachal Pradesh. Moreover, within these states, numerous districts reported rates exceeding 15%. The results of Global Moran's I indicated a statistically significant geographical clustering of unintended pregnancy rates at the district level, with a coefficient of 0.47 (p < 0.01). Cluster and outlier analysis further identified three major high-high clusters, predominantly located in the districts of Arunachal Pradesh, northern West Bengal, Bihar, western Uttar Pradesh, Haryana, Delhi, alongside a few smaller clusters in Odisha, Madhya Pradesh, Uttarakhand, and Himachal Pradesh. This geographic clustering of unintended pregnancy may be attributed to factors such as unmet needs for family planning, preferences for smaller family sizes, or the desire for male children. Results from the SEM underscored that parity and use of modern contraceptive were statistically significant predictors of unintended pregnancy at the district level. Conclusion: Our analysis of comprehensive, nationally representative data from NFHS-5 in India reveals significant geographical disparities in unintended pregnancies, evident at both state and district levels. These findings underscore the critical importance of targeted policy interventions, particularly in geographical hotspots, to effectively reduce unintended pregnancy rates and can contribute significantly to improving reproductive health outcomes across the country. [ABSTRACT FROM AUTHOR]

Transport systems are essential for the path toward sustainable urbanisation and the transition to more sustainable living. Recently, European cities have undergone substantial changes, and suburbanisation is posing new challenges. Suburban areas are often more affordable in terms of housing, but these neighbourhoods tend to be car-oriented. This leads to higher commuter costs, immobility, transport and time poverty, pollution, higher accident rates and a lack of social interactions. To offer sustainable mobility options to citizens, we must comprehensively understand, together with their individual characteristics, their specific mobility practices and the built environment where they live. This study is centred on the Barcelona Metropolitan Region, which has a public transport network that covers its entire area. The aim of this study is to examine the relationships between travel behaviour, transport mode use, individual characteristics and built environment characteristics in the place of residence using detailed information sources. Herein, we used data from the 2018 to 2021 annual travel survey conducted in the Barcelona region, together with land use and sociodemographic information. Our findings suggest that transport policies have encouraged sustainable mobility practices, particularly in the centre of Barcelona. Despite the positive results, considerable disparities exist between the inner and outer city, with a notable decline in sustainable mobility practices in the latter, due to the uneven distribution of basic services and uneven provision of public transport, together with lower density areas. Our results demonstrate that this uneven distribution reduces the available sequence profiles of inhabitants. In conclusion, the promotion of sustainable mobility policies necessitates further advances in transport, city and land-use planning that consider equity, gender, the socioeconomic profiles of citizens and mixed urban planning. [ABSTRACT FROM AUTHOR]

Energy poverty has impressive negative effects on people's health. Alleviating energy poverty is crucial for a just and equitable transition. However, policies and attempts to reduce energy poverty present a challenge to researchers and policymakers due to its complexity. The lack of a clear definition, of a common set of metrics to assess its multiple dimensions, and of spatially explicit assessments represent serious shortcomings that hinder effective policy design. This paper aims to explore the relevance and spatial distribution of the determinants of vulnerability to energy poverty to support the design of effective responses at different scales. To this end, a principal component (PCA) and a geographically weighted principal component analysis (GWPCA) are conducted on more than 1300 municipalities in 15 Italian metropolitan areas, to identify the spatial patterns of vulnerability to energy poverty and its causes. The PCA highlights three main components of vulnerability to energy poverty in the study areas, respectively, related to the job condition and to individual and households' socioeconomic factors, which provide relevant insights for policies at the national level, The GWPCA provides more detailed information to effectively support policies at the local level. The novelty of this work is the comparison of results from a PCA and a GWPCA of their different contributions to policy design at different scales. [ABSTRACT FROM AUTHOR]

Solar energy has become a prominent and crucial source of clean energy due to the increasing global demand for electricity. There is a prevailing global trend towards the construction of solar farms as a means of energy generation. This study aims to assess multiple criteria encompassing urban, environmental, and social aspects in order to ascertain the optimal site for constructing solar farms. The evaluation primarily focuses on characteristics such as topography, solar radiation, accessibility, land use, and proximity to roads and power stations. In order to acquire the necessary spatial fit model, the data was evaluated utilizing a Geographic Information System (GIS) program, and the criteria were subsequently consolidated into an integrated geographic information system. A comprehensive review of the existing body of literature reveals that environmental factors, specifically solar radiation and aspect, play a crucial role in determining the suitable places for the establishment of solar energy collection projects (Merrouni et al. in Energy Procedia 49:2270-2279, 2013; McKinney in J Student Res Environ Sci Appalac 4:1-14, 2014). Furthermore, other analysis factors such as proximity to built-up regions, closeness to power lines, and proximity to roadways are taken into account (Hott et al. in GIS-based Spatial Analysis For LargeScale Solar Power And Transmission Line Issues: Case Study of Wyoming, U.S. In: Proceedings of the 41st American Solar Energy Society Meeting, 2012; Effat in Int J Adv Remote Sens GIS 2:205-220, 2013). These criteria have an impact on the cost of solar farms. The objective of this study was to assess several aspects influencing the selection of an optimal location for a solar energy farm, taking into consideration the aforementioned criteria. The study was carried out in New Aswan city, utilizing data obtained from the Urban Planning Authority within the Ministry of Housing and Urban Development, as well as the New Aswan City AuthorityI generated cartographic representations and compiled quantitative data, which informed our selection of places that met the established criteria through the utilization of a Geographic Information System (GIS) software. Upon the conclusion of the study, numerous locations that satisfied the established criteria were selected. The present study focuses on regions with high capacity, and the findings are depicted in the form of spatial and objective maps. One of the primary benefits associated with the utilization of this methodology lies in its versatility, as it can be implemented across several domains. Furthermore, a straightforward adjustment of the criteria facilitates its application in the selection of optimal locations for wind farms. [ABSTRACT FROM AUTHOR]

Ecological vulnerability assessment is crucial for environment protection, ecological restoration and resource utilization. However, many former studies have limitations in the indicator system of the assessment, which were not comparable for different types of ecologically vulnerable areas. It is difficult to apply directly to the ecological vulnerability assessment of different types and in various regions. Aiming to solve these problems, the study proposed a well-established and comprehensive indicator system for ecological vulnerability assessment and conducted ecological vulnerability assessment application of five types of typical ecologically vulnerable areas of China based on remote sensing, meteorological, geographic and other data. The results show that the average EVIs value of Zhangbei County ranging from 0.525 to 0.559 are the highest among the five research areas during the four periods, followed by Zoige region and Xiamen bay. However, the region with the lowest average EVI value varies. In 2005 and 2015, it was Taihe County, while in 2010 and 2020, it was the Sanjiangyuan region. The variation of average EVIs in the five typical areas presents slight fluctuation and remains generally stable from 2005 to 2020. It indicates that the environmental protection measures and projects undertaken by the Chinese government in recent years have had a striking effect, curbing the trend of ecological environment deterioration. [ABSTRACT FROM AUTHOR]

Single‐cell RNA sequencing and spatial transcriptomics enable unprecedented insight into cellular and molecular pathways implicated in human skin aging and regeneration. Senescent cells are individual cells that are irreversibly cell cycle arrested and can accumulate across the human lifespan due to cell‐intrinsic and ‐extrinsic stressors. With an atlas of single‐cell RNA‐sequencing and spatial transcriptomics, epidermal and dermal senescence and its effects were investigated, with a focus on melanocytes and fibroblasts. Photoaging due to ultraviolet light exposure was associated with higher burdens of senescent cells, a sign of biological aging, compared to chronological aging. A skin‐specific cellular senescence gene set, termed SenSkin™, was curated and confirmed to be elevated in the context of photoaging, chronological aging, and non‐replicating CDKN1A+ (p21) cells. In the epidermis, senescent melanocytes were associated with elevated melanin synthesis, suggesting haphazard pigmentation, while in the dermis, senescent reticular dermal fibroblasts were associated with decreased collagen and elastic fiber synthesis. Spatial analysis revealed the tendency for senescent cells to cluster, particularly in photoaged skin. This work proposes a strategy for characterizing age‐related skin dysfunction through the lens of cellular senescence and suggests a role for senescent epidermal cells (i.e., melanocytes) and senescent dermal cells (i.e., reticular dermal fibroblasts) in age‐related skin sequelae. [ABSTRACT FROM AUTHOR]

Identifying optimal locations for sustainable commercial street development is crucial for driving economic growth and enhancing social vitality in cities. This study proposes a data-driven approach to predict potential sites for commercial streets in Foshan City, China, utilizing Points of Interest (POI) big data and machine learning techniques. Decision tree algorithms are employed to quantitatively assess and predict optimal locations at a fine-grained spatial resolution, dividing the study area into 9808 grid cells. The analysis identifies 2157 grid cells as potential sites for commercial street development, highlighting the significant influence of Medical Care, Shopping, and Recreation and Entertainment POIs on site selection. The study underscores the importance of considering population base, human activity patterns, and cultural elements in sustainable urban development. The main contributions include providing a novel decision-support method for data-driven and sustainable commercial street site selection and offering insights into the complex interplay between urban land use, human activities, and commercial development. The findings have important implications for urban planning and policy-making, showcasing the potential of data-driven approaches in guiding sustainable urban development and fostering vibrant commercial areas. [ABSTRACT FROM AUTHOR]

Airborne lidar (light detection and ranging), which produces three-dimensional models of ground surfaces under the forest canopy, has become an important tool in archaeological research. On a microscale, lidar can lead to a new understanding of building shapes and orientations that were not recognized previously. On a medium scale, it can provide comprehensive views of settlements, cities, and polities and their relationships to the topography. It also facilitates studies of diverse land use practices, such as agricultural fields, roads, and canals. On a macroscale, lidar provides a means to comprehend broad spatial patterns beyond individual sites, including the implications of vacant spaces. A significant challenge for archaeologists is the integration of historical and temporal information in order to contextualize lidar data in the framework of landscape archaeology. In addition, a rapid increase in lidar data presents ethical issues, including the question of data ownership. [ABSTRACT FROM AUTHOR]

Simple Summary: The platypus (Ornithorhynchus anatinus) is a monotreme, a rare kind of mammal that lays eggs and suckles its young. It is amphibious, lives in freshwater, and is found only in Australia, where it is closely protected. During prior conservation studies, several pathogens had been discovered in the platypus, including leptospires, which are a kind of bacterium that can cause severe disease in animals and people. We evaluated the available data concerning this infection in platypuses, tested blood samples in the Platypus Serum Bank, and combined the results with historical records. We found evidence of leptospirosis in 50% of platypuses from 14 river basins in southeastern Australia. Leptospiral infection did not stop females from breeding and juvenile platypuses were recruited to the population each year. The high seroprevalence, evidence of ongoing exposure to leptospires, lack of disease, mild kidney pathology, and visible leptospires in the kidney tubules suggest that the platypus may be a reservoir host for leptospires rather than an accidental host that suffers from the disease. Cattle on adjacent farms also had leptospirosis, but the evidence was not convincing that cattle caused the infection in platypuses. A landscape-wide study is recommended to determine the actual infection pathway. The platypus (Ornithorhynchus anatinus) is an amphibious, egg-laying mammal of high conservation value that is found only in Australia. The zoonotic bacterium Leptospira interrogans serovar Hardjo was discovered in platypuses in prior studies, but little is known about its epidemiology. Samples in the Platypus Serum Bank were tested in 2023 and the results were combined with historical records. Antibodies against L. interrogans serovar Hardjo were found in 50% of 464 serum samples from 411 platypuses collected from 14 river basins in southeastern Australia between 1981 and 2012; prevalence remained high over three decades in the Shoalhaven River population. Seroprevalence increased with age, suggesting environmental exposure. Individual platypuses had persistent titres, some for six years. Seropositive females lactated, juveniles were recruited into the population, and there were no reports of clinical leptospirosis. Three necropsied platypuses were seropositive and had mild nephritis with leptospires in the renal tubules. The high seroprevalence, persistent titres, lack of disease, mild renal lesions, and renal colonisation suggest the platypus may be a maintenance host. Sympatric cattle had L. interrogans serovar Hardjo titres, but the spatial association with seropositive platypuses was statistically weak. Other mammalian wildlife species and sheep also have L. interrogans serovar Hardjo titres; therefore, a complex ecological network must be considered. A landscape-wide study is recommended to properly assess transmission pathways and confirm who is infecting whom. [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]

Anomaly detection of three-dimensional (3D) topographic data is a challenging problem in spatial data analysis. In this paper, we investigate spatial patterns of 3D surface data that exhibit multiple in-control modes. In complex manufacturing processes, surfaces of final products could contain different topographic features from one in-control surface to another, thus making it difficult to monitor the surface with existing approaches, which rely on the assumption of the presence of single mode surface topography. We propose a novel anomaly detection approach for monitoring local topographic variations in the presence of multimode surface topography. We present a binarization model to capture the generic behavior of the multimode surfaces and enhance the representation of the surface. To systematically monitor the surface, we introduce a new probabilistic distance measure (PDM) that quantifies the similarity of spatial patterns between two binarized surfaces. The proposed PDM takes advantage of identifying local variations by utilizing the order neighbor statistics, which captures the local property on the surface. Experimental results with numerical simulation data and real-life paper surface data are provided to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

While it is clear that space matters when it comes to explaining crime, there is a lack of research exploring whether such spatial effects remain significant if victim characteristics are introduced into the analysis. This paper addresses this gap by applying risk terrain modeling to predict assaults for victims of different racial backgrounds and local versus non-local residents. The results show spatial risk factors have different impact on the likelihood of victimization for individuals of different racial backgrounds and those who live in the city where the crime occurs versus those who are visiting. The findings suggest that more attention should be given to integrating the victim characteristics with the characteristics of space when predicting violent crime using spatial analysis. [ABSTRACT FROM AUTHOR]

Over the years, ecologically sensitive areas in cities in the global south have deteriorated due to weak enforcement of development control regulations. The present study assesses the level of awareness of the buffer regulations and offers a critique of the regulations on ecologically sensitive areas in Ghana. The researchers obtained primary data from respondents whose houses were located within 200 feet of the buffers of River Wiwi, River Sisai and River Daaban in Kumasi. Secondary data was also obtained from documents from regulatory agencies such as the Environmental Protection Agency. Overall, the results of the study point to disparities in the buffer regulations among agencies such as the Land Use and Spatial Planning Authority and Environmental Planning Agency. The differences in the buffer regulations among these regulatory institutions make the enforcement of the regulations complex and ineffective. The results further show that seven in ten of the residents of the Kumasi city who have located within the buffers of wetlands were unaware of the buffer regulations despite the policy's objective of ensuring public awareness of the policy. The authors suggest the need to consolidate the buffer regulations of the various regulatory agencies within the Ghanaian context. [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.)

The extent to which incidence rates of asthma-related emergency department (ED) visits vary from neighborhood to neighborhood and predictors of neighborhood-level asthma ED visit burden are not well understood. We aimed to describe the census tract–level spatial distribution of asthma-related ED visits in Central Texas and identify neighborhood-level characteristics that explain variability in neighborhood-level asthma ED visit rates. Conditional autoregressive models were used to examine the spatial distribution of asthma-related ED visit incidence rates across census tracts in Travis County, Texas, and assess the contribution of census tract characteristics to their distribution. There were distinct patterns in ED visit incidence rates at the census tract scale. These patterns were largely unexplained by socioeconomic or selected built environment neighborhood characteristics. However, racial and ethnic composition explained 33% of the variability of ED visit incidence rates across census tracts. The census tract predictors of ED visit incidence rates differed by racial and ethnic group. Variability in asthma ED visit incidence rates are apparent at smaller spatial scales. Most of the variability in census tract–level asthma ED visit rates in Central Texas is not explained by racial and ethnic composition or other neighborhood characteristics. [ABSTRACT FROM AUTHOR]

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

The main purpose of the current research is to investigate the nighttime LST climatology in Iran. The study of the seasonal regime and annual temperature differences along with the identification of the role of different temperature thresholds and the spatial analysis of Iran's cold spots are among the climatic features studied in the current research. For this purpose, the MYD11A1 product (version 6.1) of the MODIS which is a key instrument onboard the Aqua Satellite with a horizontal resolution of 1000 meters has been used for a period of 19 years (2003-2021). This product is related to the nighttime passage of the satellite at 1:30 local time. Satellite data were validated using daily soil temperature data from 174 meteorological stations. The accuracy of satellite data showed that the area average values of root mean square error (RMSE) and mean bias error (MBE) compared with station data are 1.7℃ and 1.39℃ respectively. Also, the PBias statistics results show that the underestimation prevails in the night LST data compared to the soil temperature. In total, the MODIS sensor estimates LST in most regions of the country with an error of less than 10%, which indicates the high efficiency of the MYD11A1 product in assessing Iran's nighttime LST. The findings showed that the combination of altitude and higher latitude has significant importance in the seasonal changes of nighttime minimum LST in Iran. So, the combination of these two factors plays an important role in the occurrence of the lowest night temperatures in the country in the middle parts of central Alborz and the highlands of Azerbaijan. Thus, the longest period of dominance of low temperatures in Iran is related to 38°N and 36°N latitudes, respectively. The results also indicate that the prevailing temperature zones in Iran fluctuate from the minimum temperature in January to the maximum temperature in July. In the transitional seasons, only April and October have distinct and independent temperature identities, and the rest of the months of the transitional seasons are either connected to winter or connected to summer according to the pattern. Examining the course of intra-annual temperature changes shows that the highest intra-annual temperature concentration and the highest homogeneity are observed in the summer season. On the other hand, the highest level of heterogeneity and the lowest level of temperature concentration are related to the autumn season. Examining the average nighttime temperature difference in Iran also shows that the highest values of the earth surface temperature difference occur in the winter season and the lowest values in the summer season. In a general view, the temperature difference curve fluctuates between a maximum of 56.6℃ in January and a minimum of 46.5℃ in July. Spatial analysis of nighttime temperature difference showed that the Lut desert experiences the highest average values of nighttime temperature difference throughout the year. In general, lack of moisture, very low percentage of cloud cover, lack of vegetation cover, and very low soil moisture seem to have led to high nighttime temperature fluctuations in the Lut desert. On the other hand, a narrow strip along the southern coastline of the Caspian Sea experiences the lowest nighttime temperature difference in the country. Clearly, the high humidity of the southern coastline of the Caspian Sea plays the main role in reducing the temperature fluctuations of this region. The geographical analysis of cold spots indicates that the coldest spots of Iran are observed in the high peaks, located in high latitudes. Also, the highest geographical concentration of cold spots in Iran is related to the summer season. Meanwhile, the largest spatial distribution of Iran's cold spots can be seen in the winter season, especially in December. The findings also indicate the existence of a regular annual cycle in the spatial arrangement of cold spots in Iran. In this way, in the warm period of the year, the cold spots of Iran are concentrated only in the middle part of the central Alborz. With the beginning of the cold period of the year, we are witnessing a northwest shift of cold spots toward the highlands of Azerbaijan. So that a bimodal pattern replaces the summer concentrated pattern. The bimodal pattern continues at the height of winter with the dispersion of cold spots in Alborz and Azerbaijan, but with the arrival of the warm period of the year, a southeastward shift of cold spots towards the Alborz Mountains begins, with the cold spots completely concentrated on the Damavand peak, this annual cycle ends in the summer season. [ABSTRACT FROM AUTHOR]

Background and aims: Colorectal cancer is the third most common cancer worldwide, accounting for 10% of cancer deaths. Therefore, this study was performed with the aim of spatial analysis of risk factors for colorectal cancer in Iran. Method: This study was conducted ecologically using STEPS information (The WHO Stepwise Approach to NCD Risk Factor Surveillance) in Iran. To analyze the data, the researcher used cluster analysis and Geographically Weighted Regression methods with the help of ArcGIS version 10. Results: The results of OLS analysis showed that there was a significant relationship between tobacco consumption (B = 0.571, p‐value = 0.044) and smoking (B = 0.772, p‐value = 0.010) and the incidence of colon cancer (CC). There was also a significant relationship between abdominal obesity and the incidence of rectal cancer (RC) (B = 0.061, p‐value = 0.027). Conclusion: This study showed that (CC) high‐risk areas are located in central and northern parts of Iran, and the significant risk factors related to CC and RC were found to be tobacco use, cigarette smoking, and abdominal obesity. These findings are helpful to inform policymakers to plan screening services to reduce CC and RC, especially in high‐risk populations. [ABSTRACT FROM AUTHOR]

The Hudson Bay seasonal sea ice record has been well known since the advent of satellite reconnaissance, with a continuous record since 1971. To extend the record to earlier decades, a thermal variability framework is used with the surface temperature climatological records from four climate stations along the Hudson Bay shoreline: Churchill, Manitoba; Kuujjurapik, Quebec; Inukjuak, Quebec; and Coral Harbour, Nunavut. The day-to-day surface temperature variation for the minimum temperature of the day was found to be well correlated to the known seasonal sea ice distribution in the Bay. The sea ice/thermal variability relationship was able to reproduce the existing sea ice record (the average breakup and freeze-up dates for the Bay) largely within the error limits of the sea ice data (1 week), as well as filling in some gaps in the existing sea ice record. The breakup dates, freeze-up dates, and ice-free season lengths were generated for the period of 1922 to 1970, with varying degrees of confidence, adding close to 50 years to the sea ice record. Key periods in the spring and fall were found to be critical, signaling the time when the changes in the sea conditions are first notable in the temperature variability record, often well in advance of the 5/10th ice coverage used for the sea ice record derived from ice charts. These key periods in advance of the breakup and freeze-up could be potentially used, in season, as a predictor for navigation. The results are suggestive of a fundamental change in the nature of the breakup (faster) and freeze-up (longer) in recent years. [ABSTRACT FROM AUTHOR]

Precipitation is crucial for water resource renewal, but climate change alters their frequency and amounts, challenging societies for correct and effective water management. However, modifications of precipitation dynamics appear to be not uniformly distributed, both in space and time. Even in relatively small areas, precipitation shows the coexistence of positive and negative trends. Local topography seems to be a strong driver of precipitation changes. Understanding precipitation changes and their relationship with local topography is crucial for society's resilience. Taking advantage of a dense and long-lasting (1920–2019) meteorological monitoring network, we analyzed the precipitation changes over the last century in a sensitive and strategic area in the Mediterranean hotspot. The study area corresponds to northern Tuscany (Italy), where its topography comprises mountain ridges and coastal and river plains. Forty-eight rain gauges were selected with continuous annual precipitation time series. These were analyzed for trends and differences in mean annual precipitation between the stable period of 1921–1970 and the last 30-year 1990–2019. The relationship between precipitation changes and local topography was also examined. The results show the following highlights: (i) A general decrease in precipitation was found through the century, even if variability is marked. (ii) The mountain ridges show the largest decrease in mean annual precipitation. (iii) The precipitation change entity over the last century was not homogenous and was dependent on topography and geographical setting. (iv) A decrease in annual precipitation of up to 400 mm was found for the mountainous sites. [ABSTRACT FROM AUTHOR]