Alcohol Use Amongst Rural Adolescents and Young Adults: A Brief Review of the Literature

The sociodevelopmental periods of adolescence and young adulthood are rife with alcohol use. However, much of the literature demonstrating this comes from 'traditional' settings and college campuses (i.e., large suburban/urban campuses, or those containing their own infrastructure). Alcohol culture in rural areas has largely been understudied, which may be problematic given the unique stressors they face (e.g., economic hardship, lack of social activities, healthcare inequality). There has also been difficulty both within and across fields classifying rural versus urban geographical locations; no distinct system used broadly, making ittrea difficult to generalize and accurately collect data. The geographic categorizations are often viewed as homogenous identifiers; however, diversity occurs both within and outside of these classification systems. It appears that rurality may be a risk factor for increased drinking both earlier and later in life, but the research has failed to extend to the formative college years. This short review has two main focuses: attempting to disentangle the definition of rurality and reviewing the literature regarding alcohol use in rural areas, with a specific focus on adolescents and young adults. Identifying the mechanisms responsible for substance use in rural areas is a crucial component of prevention and treatment programs.

A new commercial boundary dataset for metropolitan areas in the USA and Canada, built from open data

The purpose of this study is to define the geographic boundaries of commercial areas by creating a consistent definition, combining various commercial area types, including downtowns, retail centres, financial districts, and other employment subcentres. Our research involved the collection of office, retail and job density data from 69 metropolitan regions across USA and Canada. Using this data, we conducted an unsupervised image segmentation model and clustering methods to identify distinctive commercial geographic boundaries. As a result, we identified 23,751 commercial areas, providing a detailed perspective on the commercial landscape of metropolitan areas in the USA and Canada. In addition, the generated boundaries were successfully validated through comparison with previously established commerce-related boundaries. The output of this study has implications for urban and regional planning and economic development, delivering valuable insights into the overall commercial geography in the region. The commercial boundary and used codes are freely available on the School of Cities Github, and users can reuse, reproduce and modify the boundaries.

YJMob100K: City-scale and longitudinal dataset of anonymized human mobility trajectories

Modeling and predicting human mobility trajectories in urban areas is an essential task for various applications including transportation modeling, disaster management, and urban planning. The recent availability of large-scale human movement data collected from mobile devices has enabled the development of complex human mobility prediction models. However, human mobility prediction methods are often trained and tested on different datasets, due to the lack of open-source large-scale human mobility datasets amid privacy concerns, posing a challenge towards conducting transparent performance comparisons between methods. To this end, we created an open-source, anonymized, metropolitan scale, and longitudinal (75 days) dataset of 100,000 individuals' human mobility trajectories, using mobile phone location data provided by Yahoo Japan Corporation (currently renamed to LY Corporation), named YJMob100K. The location pings are spatially and temporally discretized, and the metropolitan area is undisclosed to protect users' privacy. The 90-day period is composed of 75 days of business-as-usual and 15 days during an emergency, to test human mobility predictability during both normal and anomalous situations.

High spatial resolution elevation change dataset derived from ICESat-2 crossover points on the Tibetan Plateau

Understanding elevation changes on the Tibetan Plateau is crucial to comprehend the changes in topography, landscape, climate, environmental conditions, and water resources. However, some of the current products that track elevation changes only cover specific surface types or limited areas, and others have low spatial resolution. We propose an algorithm to extract ICESat-2 crossover points dataset for the Tibetan Plateau, and form a dataset. The crossover points dataset has a density of 2.015 groups/km², and each group of crossover points indicates the amount of change in elevation before and after a period of time over an area of approximately 17 meters in diameter. Comparing ICESat-2 crossover points data with existing studies on glaciers and lakes, we demonstrated the reliability of the derived elevation changes. The ICESat-2 crossover points provide a refined data source for understanding high-spatial-resolution elevation changes on the Tibetan Plateau. This dataset can provide validation data for various studies that require high-precision or high-resolution elevation change data on the Tibetan Plateau.

Tuberculosis prevalence and demographic characteristics of population in Azad Jammu and Kashmir (Pakistan): A retrospective study

Tuberculosis (TB) remains a serious problem for public health and a leading cause of death after COVID-19 and superior to even HIV/AIDS. It is a social health issue and can cause stigma and economic loss as the person cannot perform professionally due to lethargy caused by disease. It is a retrospective study done on data from National TB program Muzaffarabad chapter. The details were noted on SPSS and analysis was done to find important demographic characteristics. The total number of patients was 3441; among which 48.76% were males. Most of them (81.11%) belonged to the Muzaffarabad division of Azad Jammu and Kahmir (AJK). The microbiologically or culture positive cases were 440. Rifampicin resistance was present in 147 cases, further categorized as high (n = 143), very high (n = 3), or true positive (n = 1) resistance. Muti drug resistance was found in 19 cases. The microscopy culture is more sensitive (AUC = 0.511) than MTB/RIF or serology (AUC = 0.502) according to ROC. The rate of positive smear results is not very satisfactory in the present study as it cannot detect dormant or latent cases. There is a need to establish more sensitive tests for detection of cases and more research to combat the disease.

Three years of weekly DEMs, aerial orthomosaics and surveyed shoreline positions at Waikīkī Beach, Hawai'i

In this dataset, we present 128 coastal surveys conducted between 2018 and 2021 at Kahaloa Beach, also known as the Royal Hawaiian Beach, in Waikīkī, Hawai'i. Surveys were conducted on a near-weekly basis, providing a 0.5 m digital elevation model, an orthorectified image mosaic with 0.03 m resolution, and shoreline vectors at MHHW and MSL, along with a surveyed shoreline position for each survey. We captured overlapping images using a small Unoccupied Aerial System (sUAS), processing the imagery with photogrammetric software to produce orthomosaics and Digital Terrain Models (DTM). Simultaneously, the shoreline position and reference points for sUAS-derived products were surveyed using total station and rod-mounted surveying prism. A quality assessment of 424 randomly sampled points across two surveys showed normally distributed errors of DTM elevations (µ1 = 0.0060 m; σ1 = 0.0998 m; µ2 = 0.0035 m; σ2 = 0.0680). Elevation uncertainties were quantified as 95% confidence intervals (±0.0130 m and ±0.0095 m). These data are intended to encourage research on reef-fringed beaches and provide a dataset for evaluating the accuracy of satellite-derived shorelines at reef-fringed beaches.

A comprehensive historical and geolocalized database of mining activities in Canada

This paper introduces the MinCan database that presents mine-level estimates for the Canadian mining industry with a persistent annual coverage between 1950 and 2022. These estimates are based on archival maps and a selection of historical sources, which follows a hierarchy of criteria-based credibility and standardization. The information contained in MinCan covers 947 mines and provides information about their location (longitude and latitude in decimal), the company ownership, the principal commodities produced, and the years of operation (opening and closing dates). It is the first open access database to propose an exhaustive, free, and reliable compilation of the principal past and present mines producing in Canada. The geographic coordinates enable matching with other local, regional, and national databases, and allow for a wide range of research objectives to be met.

Harmonized disposable income dataset for Europe at subnational level

In recent decades, detailed country-level estimates of income and wealth have become widely available and inform us about the evolution of inequality between and within countries. But a substantial portion of these available datasets lack sub-national geographical information, precluding the exploration of the spatial distribution and evolution of inequalities within countries. We present here a new dataset of disposable income for Europe at the subnational level. It has been compiled from existing income data (gross income, gross earnings, equivalised income, etc.) published by national statistical institutes at different geographical levels. We used linear regressions and numerical operations to estimate disposable income from other available socio-economic statistics (e.g. household size, tax rates). We developed a harmonization and adjustment procedures to ensure of the consistency of statistical units, income indicators, costs of living and inflation. The dataset covers 42 European countries distributed over more than 120,000 geographical entities on the 1995 to 2021 period (most of the data being available for the 2010-2020 decade). This new dataset opens avenues for investigating the links between income inequality and other socio-economic or ecological processes.

Trade-offs in land-based carbon removal measures under 1.5 °C and 2 °C futures

Land-based carbon removals, specifically afforestation/reforestation and bioenergy with carbon capture and storage (BECCS), vary widely in 1.5 °C and 2 °C scenarios generated by integrated assessment models. Because underlying drivers are difficult to assess, we use a well-known integrated assessment model, GCAM, to demonstrate that land-based carbon removals are sensitive to the strength and scope of land-based mitigation policies. We find that while cumulative afforestation/reforestation and BECCS deployment are inversely related, they are both typically part of cost-effective mitigation pathways, with forestry options deployed earlier. While the CO2 removal intensity (removal per unit land) of BECCS is typically higher than afforestation/reforestation over long time horizons, the BECCS removal intensity is sensitive to feedstock and technology choices whereas the afforestation/reforestation removal intensity is sensitive to land policy choices. Finally, we find a generally positive relationship between agricultural prices and removal effectiveness of land-based mitigation, suggesting that some trade-offs may be difficult to avoid.

Intranational synergies and trade-offs reveal common and differentiated priorities of sustainable development goals in China

Accelerating efforts for the Sustainable Development Goals requires understanding their synergies and trade-offs at the national and sub-national levels, which will help identify the key hurdles and opportunities to prioritize them in an indivisible manner for a country. Here, we present the importance of the 17 goals through synergy and trade-off networks. Our results reveal that 19 provinces show the highest trade-offs in SDG13 (Combating Climate Change) or SDG5 (Gender Equality) consistent with the national level, with other 12 provinces varying. 24 provinces show the highest synergies in SDG1 (No Poverty) or SDG6 (Clean Water and Sanitation) consistent with the national level, with the remaining 7 provinces varying. These common but differentiated SDG priorities reflect that to ensure a coordinated national response, China should pay more attention to the provincial situation, so that provincial governments can formulate more targeted policies in line with their own priorities towards accelerating sustainable development.

Emergency department crowding: An examination of older adults and vulnerability

Emergency departments in many nations worldwide have been struggling for many years with crowding and the subsequent provision of care in hallways and other unconventional spaces. While this issue has been investigated and analyzed from multiple perspectives, the ethical dimensions of the place of emergency department care have been underexamined. Specifically, the impacts of the place of care on patients and their caregivers have not been robustly explored in the literature. In this article, a feminist ethics and human geography framing is utilized to argue that care provision in open and unconventional spaces in the emergency department can be unethical, as vulnerability can be amplified by the place of care for patients and their caregivers. The situational and pathogenic vulnerability of patients can be heightened by the place of the emergency department and by the constraints to healthcare providers' capacity to promote patient comfort, privacy, communication, and autonomy in this setting. The arrangements of care in the emergency department are of particular concern for older adults given the potential increased risks for vulnerability in this population. As such, hallway healthcare can reflect the normalized inequities of structural ageism. Recommendations are provided to address this complicated ethical issue, including making visible the moral experiences of patients and their caregivers, as well as those of healthcare providers in the emergency department, advocating for a systems-level accounting for the needs of older adults in the emergency department and more broadly in healthcare, as well as highlighting the need for further research to examine how to foster autonomy and care in the emergency department to reduce the risk for vulnerabilities.

Evaluating evidence for co-geography in the Anopheles-Plasmodium host-parasite system

The often tight association between parasites and their hosts means that under certain scenarios, the evolutionary histories of the two species can become closely coupled both through time and across space. Using spatial genetic inference, we identify a potential signal of common dispersal patterns in the Anopheles gambiae and Plasmodium falciparum host-parasite system as seen through a between-species correlation of the differences between geographic sampling location and geographic location predicted from the genome. This correlation may be due to coupled dispersal dynamics between host and parasite but may also reflect statistical artifacts due to uneven spatial distribution of sampling locations. Using continuous-space population genetics simulations, we investigate the degree to which uneven distribution of sampling locations leads to bias in prediction of spatial location from genetic data and implement methods to counter this effect. We demonstrate that while algorithmic bias presents a problem in inference from spatio-genetic data, the correlation structure between A. gambiae and P. falciparum predictions cannot be attributed to spatial bias alone and is thus likely a genetic signal of co-dispersal in a host-parasite system.

DeltaDTM: A global coastal digital terrain model

Coastal elevation data are essential for a wide variety of applications, such as coastal management, flood modelling, and adaptation planning. Low-lying coastal areas (found below 10 m +Mean Sea Level (MSL)) are at risk of future extreme water levels, subsidence and changing extreme weather patterns. However, current freely available elevation datasets are not sufficiently accurate to model these risks. We present DeltaDTM, a global coastal Digital Terrain Model (DTM) available in the public domain, with a horizontal spatial resolution of 1 arcsecond (∼30 m) and a vertical mean absolute error (MAE) of 0.45 m overall. DeltaDTM corrects CopernicusDEM with spaceborne lidar from the ICESat-2 and GEDI missions. Specifically, we correct the elevation bias in CopernicusDEM, apply filters to remove non-terrain cells, and fill the gaps using interpolation. Notably, our classification approach produces more accurate results than regression methods recently used by others to correct DEMs, that achieve an overall MAE of 0.72 m at best. We conclude that DeltaDTM will be a valuable resource for coastal flood impact modelling and other applications.

Fine-grained urban blue-green-gray landscape dataset for 36 Chinese cities based on deep learning network

Detailed and accurate urban landscape mapping, especially for urban blue-green-gray (UBGG) continuum, is the fundamental first step to understanding human-nature coupled urban systems. Nevertheless, the intricate spatial heterogeneity of urban landscapes within cities and across urban agglomerations presents challenges for large-scale and fine-grained mapping. In this study, we generated a 3 m high-resolution UBGG landscape dataset (UBGG-3m) for 36 Chinese metropolises using a transferable multi-scale high-resolution convolutional neural network and 336 Planet images. To train the network for generalization, we also created a large-volume UBGG landscape sample dataset (UBGGset) covering 2,272 km2 of urban landscape samples at 3 m resolution. The classification results for five cities across diverse geographic regions substantiate the superior accuracy of UBGG-3m in both visual interpretation and quantitative evaluation (with an overall accuracy of 91.2% and FWIoU of 83.9%). Comparative analyses with existing datasets underscore the UBGG-3m's great capability to depict urban landscape heterogeneity, providing a wealth of new data and valuable insights into the complex and dynamic urban environments in Chinese metropolises.

Rural-urban differences in health care unaffordability

PURPOSE

To compare health care unaffordability in rural versus urban contexts while also examining the impact of sociodemographic/socioeconomic variables on this relationship.

METHODS

We examined survey responses from the 2019-2021 National Health Interview Survey (n = 90,761). We conducted chi-squared tests comparing urban and rural subsamples and multivariable logistic regression analyses examining the associations between rurality and 3 measures of health care unaffordability while also including interactions between rurality and individual characteristics of respondents.

FINDINGS

In bivariate analyses, compared to their urban counterparts, rural residents were more likely to report problems paying medical bills (15.0% vs 11.5%, P <.001) and being unable to pay medical bills (9.3% vs 7.1%, P < .001). In fully adjusted multivariable regression analyses, rural residents were significantly less likely than their urban counterparts to report being worried about paying medical bills (AOR: .915, CI: .871-.961, P < .001). We found significant interactions between rural residency and insurance type, age, income to poverty ratio, and race/ethnicity for the outcome of problems paying medical bills; and significant interactions between rural residency and income to poverty ratio and race and ethnicity for the outcome of being unable to pay medical bills.

CONCLUSION

Rural residents report higher rates of 2 measures of health care unaffordability as compared to their urban counterparts. In multivariable logistic models, rural residency is not associated with higher rates of health care unaffordability; however, significant interactions exist between rural residency and individual variables demonstrating the heterogenous experiences of health care unaffordability based on these intersectional identities.

GEOWEALTH-US: Spatial wealth inequality data for the United States, 1960-2020

Wealth inequality has been sharply rising in the United States and across many other high-income countries. Due to a lack of data, we know little about how this trend has unfolded across locations within countries. Examining the subnational geography of wealth is crucial because, from one generation to the next, it shapes the distribution of opportunity, disadvantage, and power across individuals and communities. By employing machine-learning-based imputation to link national historical surveys conducted by the U.S. Federal Reserve to population survey microdata, the data presented in this article addresses this gap. The Geographic Wealth Inequality Database ("GEOWEALTH-US") provides the first estimates of the level and distribution of wealth at various geographical scales within the United States from 1960 to 2020. The GEOWEALTH-US database enables new lines of investigation into the contribution of spatial wealth disparities to major societal challenges including wealth concentration, income inequality, social mobility, housing unaffordability, and political polarization.

A simulated 'sandbox' for exploring the modifiable areal unit problem in aggregation and disaggregation

We present a spatial testbed of simulated boundary data based on a set of very high-resolution census-based areal units surrounding Guadalajara, Mexico. From these input areal units, we simulated 10 levels of spatial resolutions, ranging from levels with 5,515-52,388 units and 100 simulated zonal configurations for each level - totalling 1,000 simulated sets of areal units. These data facilitate interrogating various realizations of the data and the effects of the spatial coarseness and zonal configurations, the Modifiable Areal Unit Problem (MAUP), on applications such as model training, model prediction, disaggregation, and aggregation processes. Further, these data can facilitate the production of spatially explicit, non-parametric estimates of confidence intervals via bootstrapping. We provide a pre-processed version of these 1,000 simulated sets of areal units, meta- and summary data to assist in their use, and a code notebook with the means to alter and/or reproduce these data.

A 10-m national-scale map of ground-mounted photovoltaic power stations in China of 2020

We provide a remote sensing derived dataset for large-scale ground-mounted photovoltaic (PV) power stations in China of 2020, which has high spatial resolution of 10 meters. The dataset is based on the Google Earth Engine (GEE) cloud computing platform via random forest classifier and active learning strategy. Specifically, ground samples are carefully collected across China via both field survey and visual interpretation. Afterwards, spectral and texture features are calculated from publicly available Sentinel-2 imagery. Meanwhile, topographic features consisting of slope and aspect that are sensitive to PV locations are also included, aiming to construct a multi-dimensional and discriminative feature space. Finally, the trained random forest model is adopted to predict PV power stations of China parallelly on GEE. Technical validation has been carefully performed across China which achieved a satisfactory accuracy over 89%. Above all, as the first publicly released 10-m national-scale distribution dataset of China's ground-mounted PV power stations, it can provide data references for relevant researchers in fields such as energy, land, remote sensing and environmental sciences.

A benchmark GaoFen-7 dataset for building extraction from satellite images

Accurate building extraction is crucial for urban understanding, but it often requires a substantial number of building samples. While some building datasets are available for model training, there remains a lack of high-quality building datasets covering urban and rural areas in China. To fill this gap, this study creates a high-resolution GaoFen-7 (GF-7) Building dataset utilizing the Chinese GF-7 imagery from six Chinese cities. The dataset comprises 5,175 pairs of 512 × 512 image tiles, covering 573.17 km2. It contains 170,015 buildings, with 84.8% of the buildings in urban areas and 15.2% in rural areas. The usability of the GF-7 Building dataset has been proved with seven convolutional neural networks, all achieving an overall accuracy (OA) exceeding 93%. Experiments have shown that the GF-7 building dataset can be used for building extraction in urban and rural scenarios. The proposed dataset boasts high quality and high diversity. It supplements existing building datasets and will contribute to promoting new algorithms for building extraction, as well as facilitating intelligent building interpretation in China.

Spatial, geographic, and demographic factors associated with adolescent and youth suicide: a systematic review study

Background

Suicide is a public health issue and a main cause of mortality among adolescents and the youth worldwide, particularly in developing countries.

Objectives

The present research is a systematic review aiming to investigate the spatial, geographical, and demographic factors related to suicide among adolescents and the youth.

Methods

In this systematic review, two researchers examined PsycINFO, Web of Science, Scopus, and PubMed databases on December 7th, 2022 with no time limits from the beginning of publication until 2022 to identify the primary studies on spatial and geographic analysis on adolescent and youth suicides. Once duplicate studies were identified and removed, the titles and abstracts of studies were examined and irrelevant studies were also removed. Finally, 22 studies were reviewed based on the inclusion criteria.

Results

Our findings show that suicide rates are generally higher among men, residents of rural and less densely populated regions, coastal and mountainous regions, natives, 15-29 age group, less privileged populations with social fragmentation, unemployed, divorced or lonely people, those who live in single parent families, people with mental health issues, and those with low levels of education.

Conclusions

Stronger evidence supports the effects of geographic and demographic variables on youth and adolescent suicide rates as compared with spatial variables. These findings suggest that policy makers take spatial and demographic factors into consideration when health systems allocate resources for suicide prevention, and that national policymakers integrate demographic and geographic variables into health service programs.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42023430994.

Global surveillance of antimicrobial resistance in food animals using priority drugs maps

Antimicrobial resistance (AMR) in food animals is a growing threat to animal health and potentially to human health. In resource-limited settings, allocating resources to address AMR can be guided with maps. Here, we mapped AMR prevalence in 7 antimicrobials in Escherichia coli and nontyphoidal Salmonella species across low- and middle-income countries (LIMCs), using 1088 point-prevalence surveys in combination with a geospatial model. Hotspots of AMR were predicted in China, India, Brazil, Chile, and part of central Asia and southeastern Africa. The highest resistance prevalence was for tetracycline (59% for E. coli and 54% for nontyphoidal Salmonella, average across LMICs) and lowest for cefotaxime (33% and 19%). We also identified the antimicrobial with the highest probability of resistance exceeding critical levels (50%) in the future (1.7-12.4 years) for each 10 × 10 km pixel on the map. In Africa and South America, 78% locations were associated with penicillins or tetracyclines crossing 50% resistance in the future. In contrast, in Asia, 77% locations were associated with penicillins or sulphonamides. Our maps highlight diverging geographic trends of AMR prevalence across antimicrobial classes, and can be used to target AMR surveillance in AMR hotspots for priority antimicrobial classes.

Not just crop or forest: an integrated land cover map for agricultural and natural areas

Due to the key role surrounding landscape plays in ecological processes, a detailed characterization of land cover is critical for researchers and conservation practitioners. Unfortunately, in the United States, land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this gap, we merged two datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce integrated 'Spatial Products for Agriculture and Nature' (SPAN). Our workflow leveraged strengths of the NVC and the CDL to create detailed rasters comprising both agricultural and natural land-cover classes. We generated SPAN annually from 2012-2021 for the conterminous United States, quantified agreement and accuracy of SPAN, and published the complete computational workflow. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved most conflicts, leaving only 0.6% of agricultural pixels unresolved in SPAN. These ready-to-use rasters characterizing both agricultural and natural land cover will be widely useful in environmental research and management.

A 31-year (1990-2020) global gridded population dataset generated by cluster analysis and statistical learning

Continuously monitoring global population spatial dynamics is crucial for implementing effective policies related to sustainable development, including epidemiology, urban planning, and global inequality. However, existing global gridded population data products lack consistent population estimates, making them unsuitable for time-series analysis. To address this issue, this study designed a data fusion framework based on cluster analysis and statistical learning approaches, which led to the generation of a continuous global gridded population dataset (GlobPOP). The GlobPOP dataset was evaluated through two-tier spatial and temporal validation to demonstrate its accuracy and applicability. The spatial validation results show that the GlobPOP dataset is highly accurate. The temporal validation results also reveal that the GlobPOP dataset performs consistently well across eight representative countries and cities despite their unique population dynamics. With the availability of GlobPOP datasets in both population count and population density formats, researchers and policymakers can leverage the new dataset to conduct time-series analysis of the population and explore the spatial patterns of population development at global, national, and city levels.

An open dataset on individual perceptions of transport policies

Many cities are facing challenges caused by the increasing use of motorised transport and Hanoi, Vietnam, is no exception. The proliferation of petrol powered motorbikes has caused serious problems of congestion, pollution, and road safety. This paper reports on a new survey dataset that was created as part of the Urban Transport Modelling for Sustainable Well-Being in Hanoi (UTM-Hanoi) project. The survey of nearly 30,000 respondents gathers data on households' demographics, perceptions, opinions and stated behaviours. The data are informative in their own right and have also been used to experiment with multi-scale spatial statistics, synthetic population generation and machine learning approaches to predicting an individual's perceptions of potential government policies. The paper reports on the key findings from the survey and conducts a technical validation to contrast the outcomes to similar datasets that are available.

Shoreline classification maps and ground truth data for the Neuse River Estuary, North Carolina

Estuaries provide essential ecosystem services and economic value but are facing widespread degradation due to changing anthropogenic and climatic factors. In North Carolina, coastal structures, like bulkheads and riprap, are widely used by property owners throughout the Albemarle-Pamlico estuary to stop erosion and reclaim lost land following storm events. While coastal development is tightly governed, limited historical and no up-to-date data report on the spatial distribution of coastal structures throughout the Albemarle-Pamlico estuary. Here we describe the development of a dataset that classifies and catalogues 67 km of shoreline type along the Neuse River Estuary (NRE), a large tributary of the Albemarle-Pamlico. We used available LiDAR digital elevation models (DEMs), aerial imagery, and a ground truthing field campaign to determine shoreline type present along the NRE as of 2020. We validated these results using an intensive manual editing procedure that comparatively examines DEMs, LiDAR derived slope, aerial imagery, and ground truth photography of the shoreline. This dataset is available for public download.

Public transport accessibility indicators to urban and regional services in Great Britain

Public transport accessibility to urban and regional services has been found to relate to various social and economic processes, such as unemployment, transport mode choice, property prices, and public health. A frequent type of measures representing accessibility are location-based. While these offer advantages, like flexibility and ease of interpretation, their estimation usually requires specialized skills and substantial computational resources. To lower these barriers, we have prepared a suite of accessibility indicators for key services across Great Britain at a spatially disaggregated level. The dataset includes ready-to-use public transport accessibility indicators for employment, general practitioners (GP, or family physician), hospitals, grocery stores, supermarkets, primary and secondary schools, and urban centres. It also includes the raw travel time matrix from each origin to every potential destination, a primary input for such indicator estimation. Altogether, this resource offers various levels of application, from direct input into a range of research topics to the foundation for creating comprehensive custom indicators.

Optimized spatial information for 1990, 2000, and 2010 U.S. census microdata

We report on the successful completion of a project to upgrade the positional accuracy of every response to the 1990, 2000, and 2010 U.S. decennial censuses. The resulting data set, called Optimized Spatial Census Information Linked Across Time (OSCILAT), resides within the restricted-access data warehouse of the Federal Statistical Research Data Center (FSRDC) system where it is available for use with approval from the U.S. Census Bureau. OSCILAT greatly improves the accuracy and completeness of spatial information for older censuses conducted prior to major quality improvements undertaken by the Bureau. Our work enables more precise spatial and longitudinal analysis of census data and supports exact tabulations of census responses for arbitrary spatial units, including tabulating responses from 1990, 2000, and 2010 within 2020 block boundaries for precise measures of change over time for small geographic areas.

Sociodemographic Factors and the Risk of Pediatric Out-of-Hospital Cardiac Arrest in Ontario, Canada: A Province-Wide Case-Control Study

BACKGROUND

Pediatric out-of-hospital cardiac arrest (POHCA) is associated with significant mortality and poor neurological outcomes. We aimed to describe the association between sociodemographic factors and POHCA risk in Ontario, Canada.

METHODS AND RESULTS

We conducted a province-wide case-control study at ICES, where patient records are linked across administrative databases. The case group included children (aged 1 day to 17 years) who experienced an out-of-hospital cardiac arrest between 2004 and 2020. Controls were matched up to 1:4 on age, sex, index date, and key comorbidities. We used conditional logistic regression to measure the association between sociodemographic indicators and POHCA risk. The case and control groups included 1826 and 7254 children, respectively. Children living in areas with the highest levels of material deprivation (adjusted odds ratio [aOR], 2.35 [95% CI, 1.94-2.85]) and dependency (aOR, 1.22 [95% CI, 1.01-1.48]) had a higher odds of POHCA, relative to children living in regions with the lowest levels of material deprivation and dependency, respectively. Children living in neighborhoods with the lowest levels of ethnic diversity had a higher odds of POHCA (aOR, 1.62 [95% CI, 1.30-2.01]), relative to children living in neighborhoods with the highest levels of ethnic diversity. The odds of POHCA were lower in immigrants (aOR, 0.67 [95% CI, 0.47-0.95]), relative to the general population. Northern urban residence was associated with a higher odds of POHCA (aOR, 1.45 [95% CI, 1.13-1.87]), relative to southern urban residence.

CONCLUSIONS

Children living in neighborhoods with high levels of marginalization may have an elevated risk of experiencing POHCA. These findings highlight the importance of addressing disparities through targeted prevention and intervention efforts.

Who Serves Where: A Geospatial Analysis of Access to Endocrine Surgeons in the United States and Puerto Rico

BACKGROUND

The association between surgical volume and patient outcome is well established, with higher case volume associated with a lower risk of complications. We hypothesized that the geographic distribution of endocrine/head and neck surgeons with an endocrine focus in the United States and Puerto Rico may limit access to many potential patients, particularly in rural areas.

METHODS

We used web-based directories from the American Association of Endocrine Surgeons, American Head and Neck Society, and the American Academy of Otolaryngology-Head and Neck Surgery to identify endocrine surgery specialists in the United States and Puerto Rico. Using geographic coordinates and OpenStreetMap and Valhalla software, we calculated the areas within a 60-, 90-, or 120-minute driving distance from specialist offices. We used 2020 U.S. Census Data to calculate census tract populations inside or outside the accessible areas.

RESULTS

Excluding duplicate providers across organizations, we geocoded 603 specialist addresses in the United States and Puerto. We found that 23.76% (78.3 million) of Americans do not have access to a society-affiliated endocrine/head and neck surgeon with an endocrine focus within a 60-minute drive, 14.37% (47.4 million) within a 90-minute drive, and 8.38% (27.6 million) within a 120-minute drive. We observed that the areas of coverage are primarily focused on metropolitan areas.

CONCLUSION

Nearly one-third of Americans do not have access to a society-affiliated endocrine/head and neck surgeon with an endocrine focus within a 1-hour drive, highlighting a concerning geographic barrier to care. Further work is needed to facilitate patient access and mitigate disparities in quality care.

Spatial Epidemiology of Signet-ring Cell Colorectal Cancer in India

Background

Signet-ring cell colorectal carcinoma (SRCC) is an extremely aggressive yet uncommon histologic subtype of colorectal cancer (CRC) with an unknown etiology. There is a stark difference in the prevalence of signet cancers between Western countries and the Indian subcontinent; however, India itself is a vast and diverse country with variable cancer incidence.

Objective

To study the spatial epidemiology of SRCC in India for identifying regions with high prevalence.

Methods

This retrospective study included all patients diagnosed with colorectal adenocarcinoma at Tata Memorial Hospital, the largest colorectal cancer referral unit in India, between January 2020 and December 2022. Geocoding based on the location of the residence was done to map the incidences. Comparisons were performed between the proportion of signet cell and non-signet colorectal cancers.

Results

A total of 4100 patients with colon or rectal adenocarcinomas were included, of which signet cell histology was found in 624 (15%) patients. SRCC accounted for the highest proportions of CRCs in the Central (19%) and Northern (19%) regions, and the lowest in the North-Eastern (10%) and Western (12%) regions of India (P < 0.001), with non-overlapping confidence intervals. Compared with patients with non-signet CRCs, those with SRCC more commonly had colon cancers (22% vs. 17%; P = 0.003) and belonged to a lower socioeconomic background (67% vs. 59%; P < 0.001).

Conclusions

This study found that SRCCs accounted for a significant proportion of CRC cases in India, but there was no substantial disparity in distribution across regions.

Hospital- and county-level characteristics explain geographic variability in prices of cancer-related procedures: Implications for policy and interventions

BACKGROUND

Healthcare costs in the U.S. are high and variable, which can hinder access and impact health outcomes across communities. This study examined hospital- and county-level characteristics to identify factors that explain geographic variation in prices for four cancer-related procedures.

METHODS

Data sources included Turquoise Health, which compiles publicly-available price data from U.S. hospitals. We examined list prices for four procedures: abdominal ultrasound, diagnostic colonoscopy, brain MRI, and pelvis CT scan, which we linked to characteristics of hospitals (e.g., number of beds) and counties (e.g., metropolitan status). We used multilevel linear regression models to assess multivariable relationships between prices and hospital- and county-level characteristics. Supplementary analyses repeated these models using procedures prices for commercial insurance plans.

RESULTS

For each procedure, list prices varied across counties (intraclass correlation: abdominal ultrasound = 23.2%; colonoscopy = 17.1%; brain MRI = 37.2%; pelvis CT = 50.9%). List prices for each procedure were associated with hospital ownership (all p < 0.001) and percent of population without health insurance (all p < 0.05). For example, list prices for abdominal ultrasound were higher for proprietary versus Government-owned hospitals (β = 539.10, 95% confidence interval [CI]: 256.12, 822.08, p < 0.001) and for hospitals in counties with more uninsured residents (β = 23.44, 95% CI: 2.55, 44.33, p = 0.03). Commercial insurance prices were negatively associated with metropolitan status.

CONCLUSIONS

Prices for cancer-related healthcare procedures varied substantially, with considerable heterogeneity associated with county location as well as county-level social determinants of health (e.g., health insurance coverage). Interventions and policy changes are needed to alleviate the financial burden of cancer care among patients, including geographic variation in prices for cancer-related procedures.

Mapping the transcriptome: Realizing the full potential of spatial data analysis

RNA sequencing in situ allows for whole-transcriptome characterization at high resolution, while retaining spatial information. These data present an analytical challenge for bioinformatics-how to leverage spatial information effectively? Properties of data with a spatial dimension require special handling, which necessitate a different set of statistical and inferential considerations when compared to non-spatial data. The geographical sciences primarily use spatial data and have developed methods to analye them. Here we discuss the challenges associated with spatial analysis and examine how we can take advantage of practice from the geographical sciences to realize the full potential of spatial information in transcriptomic datasets.

Enhanced glacial lake activity threatens numerous communities and infrastructure in the Third Pole

Glacial lake outburst floods (GLOFs) are among the most severe cryospheric hazards in the Third Pole, encompassing the Tibetan Plateau and surrounding Himalayas, Hindu Kush, and Tianshan Mountains. Recent studies on glacial lake changes and GLOF characteristics and risks in this region have shown scattered and insufficiently detailed features. Here, we conduct an appraisal of the GLOF risks by combining high-resolution satellite images, case-by-case high-precision GLOF modeling, and detailed downstream exposure data. The glacial lake changes from 2018 to 2022 in the region were primarily driven by the accelerated expansion of proglacial lakes. The GLOF frequency has exhibited a significant increasing trend since 1980, with intensified activity in Southeastern Tibet and the China-Nepal border area over the past decade. Approximately 6,353 km2 of land could be at risk from potential GLOFs, posing threats to 55,808 buildings, 105 hydropower projects, 194 km2 of farmland, 5,005 km of roads, and 4,038 bridges. This study directly responds to the need for local disaster prevention and mitigation strategies, highlighting the urgent requirement of reducing GLOF threats in the Third Pole and the importance of regional cooperation.

Geography, inequities, and the social determinants of health in transplantation

Among the causes of inequity in organ transplantation, geography is oft-cited but rarely defined with precision. Traditionally, geographic inequity has been characterized by variation in distance to transplant centers, availability of deceased organ donors, or the consequences of allocation systems that are inherently geographically based. Recent research has begun to explore the use of measures at various geographic levels to better understand how characteristics of a patient's geographic surroundings contribute to a broad range of transplant inequities. Within, we first explore the relationship between geography, inequities, and the social determinants of health. Next, we review methodologic considerations essential to geographic health research, and critically appraise how these techniques have been applied. Finally, we propose how to use geography to improve access to and outcomes of transplantation.

Georeferenced dataset of maritime piracy in the Gulf of Guinea from 2010 to 2021

Piracy has been a global concern and a threat to the safety of people performing maritime trade around the globe. Since ancient times maritime piracy has been a common practice that, unfortunately, has not ended in the current days. A georeferenced dataset providing the position, meteorologic conditions, and a description of the occurrence can provide essential information for analyzing this global phenomenon. The dataset focuses on the Gulf of Guinea (GoG) as an area dominated by corruption and weak supervision capacity by the local authorities. The time interval considered in this paper is between 2010 and 2021. Using this simple dataset, it is possible to analyze attributes such as when the piracy occurred or if the illegal activity involved deaths or kidnapping. The accuracy of the data was guaranteed by cross-referencing data sources, so we have 595 pirate attacks accurately described. This dataset can easily be used for data mining, allowing further analysis of the patterns and trends of pirate attacks in the GoG over time.

Unveiling patterns in human dominated landscapes through mapping the mass of US built structures

Built structures increasingly dominate the Earth's landscapes; their surging mass is currently overtaking global biomass. We here assess built structures in the conterminous US by quantifying the mass of 14 stock-building materials in eight building types and nine types of mobility infrastructures. Our high-resolution maps reveal that built structures have become 2.6 times heavier than all plant biomass across the country and that most inhabited areas are mass-dominated by buildings or infrastructure. We analyze determinants of the material intensity and show that densely built settlements have substantially lower per-capita material stocks, while highest intensities are found in sparsely populated regions due to ubiquitous infrastructures. Out-migration aggravates already high intensities in rural areas as people leave while built structures remain - highlighting that quantifying the distribution of built-up mass at high resolution is an essential contribution to understanding the biophysical basis of societies, and to inform strategies to design more resource-efficient settlements and a sustainable circular economy.

Evaluation der Hautkrebsfrüherkennung in Deutschland - Raumzeitliche Assoziationen zwischen Hautkrebsfrüherkennung und Hautkrebsmortalität auf Grundlage ambulanter Abrechnungsdaten

Hintergrund

Es bestehen regionale Unterschiede in der Hautkrebsfrüherkennungsinanspruchnahme in Deutschland. Bislang ist ungeklärt, ob eine hohe Inanspruchnahme von Früherkennungsleistungen zu einer Senkung der Mortalität führt. Dieser Beitrag präsentiert Studienergebnisse zur Untersuchung raumzeitlicher Assoziationen von Hautkrebsfrüherkennung und Mortalität. Die angewendeten Methoden werden hinsichtlich ihrer Eignung diskutiert.

Material und Methodik

Grundlage sind ambulante Abrechnungsdaten zur Inanspruchnahme von Hautkrebsfrüherkennung sowie Daten zur Hautkrebsmortalität aus der Todesursachenstatistik der Jahre 2011–2015 auf Ebene der Kreise und kreisfreien Städte in Deutschland. Neben einer deskriptiven Auswertung wurden raumzeitliche Clusteranalysen und Regressionsmodelle angewendet, um den Zusammenhang zwischen der Inanspruchnahme von Früherkennung und Mortalität zu untersuchen. Dabei wurde neben Alter auch nach weiteren ausgewählten sozioökonomischen und ‐grafischen Variablen adjustiert.

Ergebnisse

Die deskriptiven Ergebnisse zeigen markante räumliche Muster der Hautkrebsfrüherkennung und Mortalität. Mittels Clusteranalysen konnten Regionen mit signifikant höheren und niedrigeren Fällen an Früherkennung und Hautkrebsmortalität identifiziert werden. Die raumzeitlichen Regressionsanalysen zeigen keine eindeutige Assoziation. Lediglich die Früherkennung beim Dermatologen, adjustiert nach Alter, zeigt eine Assoziation mit Mortalität.

Diskussion

Aus den Ergebnissen lässt sich kein eindeutiger Zusammenhang zwischen Hautkrebsfrüherkennung und ‐mortalität ableiten. Das verwendete Studiendesign mit einer raumzeitlichen Cluster‐ und Regressionsanalyse hat jedoch gezeigt, dass diese Methoden vertiefte Aussagen über den Zusammenhang von Hautkrebsfrüherkennung und ‐mortalität ermöglichen.

Using geospatial mapping to predict and compare gambling harm hotspots in urban, rural and coastal areas of a large county in England

BACKGROUND

Disordered gambling is a public health problem with interconnections with health and social inequality, and adverse impacts on physical and mental health. Mapping technologies have been used to explore gambling in the UK, though most were based in urban locations.

METHODS

We used routine data sources and geospatial mapping software to predict where gambling related harm would be most prevalent within a large English county, host to urban, rural and coastal communities.

RESULTS

Licensed gambling premises were most concentrated in areas of deprivation, and in urban and coastal areas. The aggregate prevalence of disordered gambling associated characteristics was also greatest in these areas.

CONCLUSIONS

This mapping study links the number of gambling premises, deprivation, and risk factors for disordered gambling, and highlights that coastal areas see particularly high density of gambling premises. Findings can be applied to target resources to where they are most needed.

An Australian housing conditions data infrastructure

For the past two decades, researchers and policy makers have known very little about conditions within Australia's housing stock due to a lack of systematic and reliable data. In 2022, a collaboration of Australian universities and researchers commissioned a large survey of 22,550 private rental, social rental and homeowner households to build a data infrastructure on the household and demographic characteristics, housing quality and conditions in the Australian housing stock. This is the third and largest instalment in a national series of housing conditions data infrastructures.

Economic valuation of temperature-related mortality attributed to urban heat islands in European cities

As the climate warms, increasing heat-related health risks are expected, and can be exacerbated by the urban heat island (UHI) effect. UHIs can also offer protection against cold weather, but a clear quantification of their impacts on human health across diverse cities and seasons is still being explored. Here we provide a 500 m resolution assessment of mortality risks associated with UHIs for 85 European cities in 2015-2017. Acute impacts are found during heat extremes, with a 45% median increase in mortality risk associated with UHI, compared to a 7% decrease during cold extremes. However, protracted cold seasons result in greater integrated protective effects. On average, UHI-induced heat-/cold-related mortality is associated with economic impacts of €192/€ - 314 per adult urban inhabitant per year in Europe, comparable to air pollution and transit costs. These findings urge strategies aimed at designing healthier cities to consider the seasonality of UHI impacts, and to account for social costs, their controlling factors, and intra-urban variability.

Global Frequency Analyses of Canine Progressive Rod-Cone Degeneration-Progressive Retinal Atrophy and Collie Eye Anomaly Using Commercial Genetic Testing Data

Hundreds of genetic variants associated with canine traits and disorders have been identified, with commercial tests offered. However, the geographic distributions and changes in allele and genotype frequencies over prolonged, continuous periods of time are lacking. This study utilized a large set of genotypes from dogs tested for the progressive rod-cone degeneration-progressive retinal atrophy (prcd-PRA) G>A missense PRCD variant (n = 86,667) and the collie eye anomaly (CEA)-associated NHEJ1 deletion (n = 33,834) provided by the commercial genetic testing company (Optigen/Wisdom Panel, Mars Petcare Science & Diagnostics). These data were analyzed using the chi-square goodness-of-fit test, time-trend graphical analysis, and regression modeling in order to evaluate how test results changed over time. The results span fifteen years, representing 82 countries and 67 breeds/breed mixes. Both diseases exhibited significant differences in genotype frequencies (p = 2.7 × 10-152 for prcd-PRA and 0.023 for CEA) with opposing graphical trends. Regression modeling showed time progression to significantly affect the odds of a dog being homozygous or heterozygous for either disease, as do variables including breed and breed popularity. This study shows that genetic testing informed breeding decisions to produce fewer affected dogs. However, the presence of dogs homozygous for the disease variant, especially for prcd-PRA, was still observed fourteen years after test availability, potentially due to crosses of unknown carriers. This suggests that genetic testing of dog populations should continue.

Canada Source Watershed Polygons (Can-SWaP): A dataset for the protection of Canada's municipal water supply

Over 80% of municipal (i.e., excluding industrial and agricultural) water use in Canada comes from streams, lakes, and reservoirs. These freshwater bodies and their catchments require adequate protection to secure drinking water supply for Canadians. Canada, like most countries, lacks a consolidated national dataset of municipal catchments, arguably due to gaps in data availability. Against this backdrop, we present the Canada Source Watershed Polygons dataset, or Can-SWaP. Can-SWaP was created using point locations of more than 3,300 municipal water licences defining rights to surface water withdrawal. Where possible, the resulting 1,574 catchments were assessed for accuracy in spatial coverage against provincial and local datasets. Each watershed in Can-SWaP has an estimated water volume used for municipal water purposes derived from licencing data, and several variables from RiverATLAS for investigating the integrity of surface drinking water sources in Canada. Furthermore, basing our method on the HydroSHEDS suite of global products offers a robust framework for the production of other national datasets following an established international standard.

Population mobility data provides meaningful indicators of fast food intake and diet-related diseases in diverse populations

The characteristics of food environments people are exposed to, such as the density of fast food (FF) outlets, can impact their diet and risk for diet-related chronic disease. Previous studies examining the relationship between food environments and nutritional health have produced mixed findings, potentially due to the predominant focus on static food environments around people's homes. As smartphone ownership increases, large-scale data on human mobility (i.e., smartphone geolocations) represents a promising resource for studying dynamic food environments that people have access to and visit as they move throughout their day. This study investigates whether mobility data provides meaningful indicators of diet, measured as FF intake, and diet-related disease, evaluating its usefulness for food environment research. Using a mobility dataset consisting of 14.5 million visits to geolocated food outlets in Los Angeles County (LAC) across a representative sample of 243,644 anonymous and opted-in adult smartphone users in LAC, we construct measures of visits to FF outlets aggregated over users living in neighborhood. We find that the aggregated measures strongly and significantly correspond to self-reported FF intake, obesity, and diabetes in a diverse, representative sample of 8,036 LAC adults included in a population health survey carried out by the LAC Department of Public Health. Visits to FF outlets were a better predictor of individuals' obesity and diabetes than their self-reported FF intake, controlling for other known risks. These findings suggest mobility data represents a valid tool to study people's use of dynamic food environments and links to diet and health.

Integrating human activity into food environments can better predict cardiometabolic diseases in the United States

The prevalence of cardiometabolic diseases in the United States is presumably linked to an obesogenic retail food environment that promotes unhealthy dietary habits. Past studies, however, have reported inconsistent findings about the relationship between the two. One underexplored area is how humans interact with food environments and how to integrate human activity into scalable measures. In this paper, we develop the retail food activity index (RFAI) at the census tract level by utilizing Global Positioning System tracking data covering over 94 million aggregated visit records to approximately 359,000 food retailers across the United States over two years. Here we show that the RFAI has significant associations with the prevalence of multiple cardiometabolic diseases. Our study indicates that the RFAI is a promising index with the potential for guiding the development of policies and health interventions aimed at curtailing the burden of cardiometabolic diseases, especially in communities characterized by obesogenic dietary behaviors.

The spatial dissimilarities and connections of the microbiota in the upper and lower respiratory tract of beef cattle

Bovine respiratory disease (BRD) causes morbidity and mortality in cattle. The critical roles of the respiratory microbiota in BRD have been widely studied. The nasopharynx was the most popular sampling niche for BRD pathogen studies. The oral cavity and other niches within the respiratory tract, such as nostrils and lung, are less assessed. In this study, oropharyngeal swabs (OS), nasal swabs (NS), nasopharyngeal swabs (NP), and bronchoalveolar lavage (BAL) were collected from calves located in four countries and analyzed for investigation of the dissimilarities and connections of the respiratory microbiota. The results showed that the microbial diversity, structure, and composition in the upper and lower respiratory tract in beef cattle from China, the USA, Canada, and Italy were significantly different. The microbial taxa for each sampling niche were specific and associated with their local physiology and geography. The signature microbiota for OS, NS, NP, and BAL were identified using the LEfSe algorithm. Although the spatial dissimilarities among the respiratory niches existed, the microbial connections were observed in beef cattle regardless of geography. Notably, the nostril and nasopharynx had more similar microbiomes compared to lung communities. The major bacterial immigration patterns in the bovine respiratory tract were estimated and some of them were associated with geography. In addition, the contribution of oral microbiota to the nasal and lung ecosystems was confirmed. Lastly, microbial interactions were characterized to reveal the correlation between the commercial microbiota and BRD-associated pathogens. In conclusion, shared airway microbiota among niches and geography provides the possibility to investigate the common knowledge for bovine respiratory health and diseases. In spite of the dissimilarities of the respiratory microbiota in cattle, the spatial connections among these sampling niches not only allow us to deeply understand the airway ecosystem but also benefit the research and development of probiotics for BRD.