Mapping habitat indices across river networks using spatial statistical modelling of River Habitat Survey data

Freshwater ecosystems are declining faster than their terrestrial and marine counterparts because of physical pressures on habitats. European legislation requires member states to achieve ecological targets through the effective management of freshwater habitats. Maps of habitats across river networks would help diagnose environmental problems and plan for the delivery of improvement work. Existing habitat mapping methods are generally time consuming, require experts and are expensive to implement. Surveys based on sampling are cheaper but provide patchy representations of habitat distribution. In this study, we present a method for mapping habitat indices across networks using semi-quantitative data and a geostatistical technique called regression kriging. The method consists of the derivation of habitat indices using multivariate statistical techniques that are regressed on map-based covariates such as altitude, slope and geology. Regression kriging combines the Generalised Least Squares (GLS) regression technique with a spatial analysis of model residuals. Predictions from the GLS model are ‘corrected’ using weighted averages of model residuals following an analysis of spatial correlation. The method was applied to channel substrate data from the River Habitat Survey in Great Britain. A Channel Substrate Index (CSI) was derived using Correspondence Analysis and predicted using regression kriging. The model explained 74% of the main sample variability and 64% in a test sample. The model was applied to the English and Welsh river network and a map of CSI was produced. The proposed approach demonstrates how existing national monitoring data and geostatistical techniques can be used to produce continuous maps of habitat indices at the national scale

Examining the correlates and drivers of human population distributions across low-and middle-income countries

Geographical factors have influenced the distributions and densities of global human population distributions for centuries. Climatic regimes have made some regions more habitable than others, harsh topography has discouraged human settlement, and transport links have encouraged population growth. A better understanding of these types of relationships enables both improved mapping of population distributions today and modelling of future scenarios. However, few comprehensive studies of the relationships between population spatial distributions and the range of drivers and correlates that exist have been undertaken at all, much less at high spatial resolutions, and particularly across the low-and middle-income countries. Here, we quantify the relative importance of multiple types of drivers and covariates in explaining observed population densities across 32 low-and middle-income countries over four continents using machine-learning approaches. We find that, while relationships between population densities and geographical factors show some variation between regions, theyare generally remarkably consistent,pointing to universal drivers of human population distribution. Here,we find that a set of geographical features relating to the built environment, ecology and topography consistently explain the majority of variability in population distributions at fine spatial scales across the low-and middle-income regions of the world.</p

The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy

Background: The ability to accurately predict operative duration has the potential to optimise theatre efficiency and utilisation, thus reducing costs and increasing staff and patient satisfaction. With laparoscopic cholecystectomy being one of the most commonly performed procedures worldwide, a tool to predict operative duration could be extremely beneficial to healthcare organisations. Methods: Data collected from the CholeS study on patients undergoing cholecystectomy in UK and Irish hospitals between 04/2014 and 05/2014 were used to study operative duration. A multivariable binary logistic regression model was produced in order to identify significant independent predictors of long (> 90 min) operations. The resulting model was converted to a risk score, which was subsequently validated on second cohort of patients using ROC curves. Results: After exclusions, data were available for 7227 patients in the derivation (CholeS) cohort. The median operative duration was 60 min (interquartile range 45–85), with 17.7% of operations lasting longer than 90 min. Ten factors were found to be significant independent predictors of operative durations > 90 min, including ASA, age, previous surgical admissions, BMI, gallbladder wall thickness and CBD diameter. A risk score was then produced from these factors, and applied to a cohort of 2405 patients from a tertiary centre for external validation. This returned an area under the ROC curve of 0.708 (SE = 0.013, p  90 min increasing more than eightfold from 5.1 to 41.8% in the extremes of the score. Conclusion: The scoring tool produced in this study was found to be significantly predictive of long operative durations on validation in an external cohort. As such, the tool may have the potential to enable organisations to better organise theatre lists and deliver greater efficiencies in care

Americas Datasets

Peer-reviewed raster-based population distribution datasets having a resolution of 3 arc seconds (approximately 100m at the equator) and created using a Random Forest-based dasymetric mapping approach (Stevens et al., 2015; see Other References in the Metadata) to disaggregate official population count data for 28 countries located in Latin America and the Caribbean – FILENAME CONVENTION: ISO_ppp/pph_v2b_YEAR_UNadj.tif = Country (identified by its unique ISO code) population per pixel (ppp)/per hectare (pph) dataset referring to a specific year (YEAR) adjusted to match United Nations national estimates (UNadj) and produced using the version 2b (v2b) of the WorldPop-RF code available at: http://dx.doi.org/10.6084/m9.figshare.1491490 (Stevens et al., 2015; see Related Material in the Metadata)

High-resolution gridded population datasets for Latin America and the Caribbean in 2010, 2015, and 2020.

The Latin America and the Caribbean region is one of the most urbanized regions in the world, with a total population of around 630 million that is expected to increase by 25% by 2050. In this context, detailed and contemporary datasets accurately describing the distribution of residential population in the region are required for measuring the impacts of population growth, monitoring changes, supporting environmental and health applications, and planning interventions. To support these needs, an open access archive of high-resolution gridded population datasets was created through disaggregation of the most recent official population count data available for 28 countries located in the region. These datasets are described here along with the approach and methods used to create and validate them. For each country, population distribution datasets, having a resolution of 3 arc seconds (approximately 100 m at the equator), were produced for the population count year, as well as for 2010, 2015, and 2020. All these products are available both through the WorldPop Project website and the WorldPop Dataverse Repository.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Americas Datasets

Peer-reviewed raster-based population distribution datasets having a resolution of 3 arc seconds (approximately 100m at the equator) and created using a Random Forest-based dasymetric mapping approach (Stevens et al., 2015; see Other References in the Metadata) to disaggregate official population count data for 28 countries located in Latin America and the Caribbean &ndash; FILENAME CONVENTION: ISO_ppp/pph_v2b_YEAR_UNadj.tif = Country (identified by its unique ISO code) population per pixel (ppp)/per hectare (pph) dataset referring to a specific year (YEAR) adjusted to match United Nations national estimates (UNadj) and produced using the version 2b (v2b) of the WorldPop-RF code available at: http://dx.doi.org/10.6084/m9.figshare.1491490 (Stevens et al., 2015; see Related Material in the Metadata).</span

Mapping road traffic crash hotspots using GIS-based methods: a case study of Muscat Governorate in the Sultanate of Oman

Objective:Road traffic crashes (RTCs) are a major global public health problem and cause substantial burden on national economy and healthcare. There is little systematic understanding of the geography of RTCs and the spatial correlations of RTCs in the Middle-East region, particularly in Oman where RTCs are the leading cause of disability-adjusted life years lost. The overarching goal of this paper is to evaluate the spatial and temporal dimensions, identifying the high risk areas or hot-zones where RTCs are more frequent, using the geocoded data from the Muscat governorate.Data:This study is based on data drawn from the Royal Oman Police (ROP) sample iMAAP database and the National Road Traffic Crash (NRTC) database, managed by the ROP and made available for research use by The Research Council of the Sultanate of Oman. The data covered the period from 1st January 2010 to 2nd November 2014. Only RTCs occurred in Muscat Governorate were included in the study. The study is based on 12,438 registered incidents, however, due to disconnections found on road network, RTCs occurred on disconnected parts were removed and the final analysis considered only 9,357 incidents.Methods:We considered an adjacency network analysis integrating GIS and RTC data using robust estimation techniques including: Kernel Density Estimation (KDE) of both 1-D and 2-D space dimensions, Network-based Nearest Neighbour Distance (Net-NND), Network-based K-Function, Random Forest Algorithm (RF) and spatiotemporal Hot-zone analysis.Findings:The analysis highlight evidence of spatial clustering and recurrence of RTC hot-zones on long roads demarcated by intersections and roundabouts in Muscat. The findings confirm that road intersections elevate the risk of RTCs than other effects attributed to road geometry features. The results from GIS application of NRTC data are validated using the sample data generated by iMAAP database.Conclusion:The findings of this study provide statistical evidence and confirm our research hypothesis that road intersections (roundabouts, crosses and bridges) represent higher risk of causing RTCs than other road geometric features. The results also demonstrate systematic quantitative evidence of spatio-temporal patterns associated with the crash risk over different locations on road networks in Muscat. More importantly, the findings clearly pinpoint the importance and influence of the road and traffic related features in road crash spatial analysis

High-resolution gridded population datasets for Latin America and the Caribbean in 2010, 2015, and 2020

The Latin America and the Caribbean region is one of the most urbanized regions in the world, with a total population of around 630 million that is expected to increase by 25% by 2050. In this context, detailed and contemporary datasets accurately describing the distribution of residential population in the region are required for measuring the impacts of population growth, monitoring changes, supporting environmental and health applications, and planning interventions. To support these needs, an open access archive of high-resolution gridded population datasets was created through disaggregation of the most recent official population count data available for 28 countries located in the region. These datasets are described here along with the approach and methods used to create and validate them. For each country, population distribution datasets, having a resolution of 3 arc seconds (approximately 100?m at the equator), were produced for the population count year, as well as for 2010, 2015, and 2020. All these products are available both through the WorldPop Project website and the WorldPop Dataverse Repository

Sub-national mapping of population pyramids and dependency ratios in Africa and Asia

The age group composition of populations varies substantially across continents and within countries, and is linked to levels of development, health status and poverty. The subnational variability in the shape of the population pyramid as well as the respective dependency ratio are reflective of the different levels of development of a country and are drivers for a country’s economic prospects and health burdens. Whether measured as the ratio between those of working age and those young and old who are dependent upon them, or through separate young and old-age metrics, dependency ratios are often highly heterogeneous between and within countries. Assessments of subnational dependency ratio and age structure patterns have been undertaken for specific countries and across high income regions, but to a lesser extent across the low income regions. In the framework of the WorldPop Project, through the assembly of over 100 million records across 6,389 subnational administrative units, subnational dependency ratio and high resolution gridded age/sex group datasets were produced for 87 countries in Africa and Asia

Subnational Dependency Ratios in Asia

Gridded, spatial datasets for Asia describing dependency ratios at sub-national level. Ratios are based on sub-national estimates for 5-year age group proportions. Dataverse comprised of 3 raster datasets: 1) The ratio of dependents (both young, 0 to 14, and old, 65+) upon the working age population; 2) The ratio of young dependents upon the working age population; 3) The ratio of older dependents upon the working age population