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138 results for “Geospatial”
Geospatial micro-estimates of slum populations in 129 Global South countries using machine learning and public data
<p><span>Reliable estimation of populations living in slums or slum-like conditions is crucial for urban planning, humanitarian resource allocation, and human well-being improvement. We generate the micro-estimate of slum population at a neighborhood level (~</span><span>3.63 arc-minutes</span><span>, preserving the privacy of vulnerable people) for 129 Global South countries in 2018. The estimates are built based on the Sustainable Development Goals 11.1 indicator framework and machine learning algorithms to heterogeneous data from household-based surveys and satellite images, as well as grided population data. Our integrated regional models show strong predictive capabilities for cluster-level slums proxy, explaining 82% to 96% of the variation in ground-truth surveys conducted in Global South countries, with root mean squared error ranging from 4.85% to 10.47%. The models perform match or surpass benchmarks established by previous studies.</span><span> </span><span>Cross-comparison with independent data sources at multi-scales suggest that our approach can yield reliable and consistent slum population estimates.</span></p>
Data from: Incorporating explicit geospatial data shows more species at risk of extinction than the current Red List
The IUCN (International Union for Conservation of Nature) Red List classifies species according to their risk of extinction, informing global to local conservation decisions. Unfortunately, important geospatial data do not explicitly or efficiently enter this process. Rapid growth in the availability of remotely sensed observations provides fine-scale data on elevation and increasingly sophisticated characterizations of land cover and its changes. These data readily show that species are likely not present within many areas within the overall envelopes of their distributions. Additionally, global databases on protected areas inform how extensively ranges are protected. We selected 586 endemic and threatened forest bird species from six of the world's most biodiverse and threatened places (Atlantic Forest of Brazil, Central America, Western Andes of Colombia, Madagascar, Sumatra, and Southeast Asia). The Red List deems 18% of these species to be threatened (15 critically endangered, 29 endangered, and 64 vulnerable). Inevitably, after refining ranges by elevation and forest cover, ranges shrink. Do they do so consistently? For example, refined ranges of critically endangered species might reduce by (say) 50% but so might the ranges of endangered, vulnerable, and nonthreatened species. Critically, this is not the case. We find that 43% of species fall below the range threshold where comparable species are deemed threatened. Some 210 bird species belong in a higher-threat category than the current Red List placement, including 189 species that are currently deemed nonthreatened. Incorporating readily available spatial data substantially increases the numbers of species that should be considered at risk and alters priority areas for conservation.
Geospatial based model for malaria risk prediction in Kilombero Valley, south-eastern Tanzania
<div> <p><strong>Background</strong>: Malaria continues to pose a major public health challenge in tropical regions. Despite significant efforts to control malaria in Tanzania, there are still residual transmission cases. Unfortunately, little is known about where these residual malaria transmission cases occur and how they spread. In Tanzania, for example, the transmission is heterogeneously distributed. In order to effectively control and prevent the spread of malaria, it is essential to understand the spatial distribution and transmission patterns of the disease. This study seeks to predict areas that are at high risk of malaria transmission so that intervention measures can be developed to accelerate malaria elimination efforts.</p> </div> <p><strong>Methods</strong>: This study employs a geospatial-based model to predict and map out malaria risk area in Kilombero Valley. Environmental factors related to malaria transmission were considered and assigned valuable weights in the Analytic Hierarchy Process (AHP), an online system using a pairwise comparison technique. The malaria hazard map was generated by a weighted overlay of the altitude, slope, curvature, aspect, rainfall distribution, and distance to streams in Geographic Information Systems (GIS). Finally, the risk map was created by overlaying components of malaria risk including hazards, elements at risk, and vulnerability.</p> <p><strong>Results</strong>: The study demonstrates that the majority of the study area falls under the moderate-risk level (61%), followed by the low-risk level (31%), while the high-malaria risk area covers a small area, which occupies only 8% of the total area.</p> <p><strong>Conclusion</strong>: The findings of this study are crucial for developing spatially targeted interventions against malaria transmission in residual transmission settings. Predicted areas prone to malaria risk provide information that will inform decision-makers and policymakers for proper planning, monitoring, and deployment of interventions.</p>
Test cases for the Geospatial Probabilistic Estimation Package (GPEP)
<p>The dataset contains test cases for the Geospatial Probabilistic Estimation Package (GPEP, https://github.com/NCAR/GPEP) and relevant publications. The two current test cases are based on prior implementations for GMET version 2.0 (https://github.com/NCAR/GMET and https://zenodo.org/records/5498408) for applications, and include:</p><ul><li>a GMET version 2.0 test case of daily meteorological forcing generation at 1/16th degree resolution for February 2017 for a region of the US Southwest encompassing California, US (Bunn et al, 2022).</li><li>a test case for daily meteorological forcing generation at 0.01 degree resolution from 1970-2022 for a smaller region of the US Southwest encompassing the upper Colorado River basin and Colorado Front Range.</li></ul><p>The associated meteorological station forcings and gridded parameter inputs were developed for water resources research projects at NCAR (PI A. Wood) sponsored by the United States Army Corps of Engineers Climate Preparedness and Resilience Program and the United States Bureau of Reclamation Science and Technology Program. </p><p>References:</p><p>Bunn, PTW, AW Wood, AJ Newman, H Chang, CL Castro, MP Clark and JR Arnold, 2022, Improving station-based ensemble surface meteorological analyses using numerical weather prediction: A case study of the Oroville Dam crisis precipitation event. <i>J. Hydromet.</i> 23(7), 1155-1169. https://doi-org.cuucar.idm.oclc.org/10.1175/JHM-D-21-0193.1<br> </p>
Geospatial data from: Identifying opportunity hot spots for reducing the risk of wildfire-caused carbon loss in western US conifer forests
<p>The geospatial dataset includes raster and vector data for visualizing the spatial distribution of risk of wildfire-caused carbon loss in Peeler et al. 2023. Raster data evaluate carbon exposure, sensitivity, and vulnerability at the pixel-level across western US carbon forests. Vector data aggregate pixel-level findings into project area and fireshed spatial units to identify target geographies (or "opportunity hot spots") where proactive forest management could reduce the greatest risk from wildfire to carbon. Vector data also identifies firesheds in which proactive forest management could simultaneously reduce the risk from wildfire to carbon and human communities.</p>
Surprise Canyon Creek wild and scenic river remote sensing and geospatial database
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Data from: Incorporating explicit geospatial data shows more species at risk of extinction than the current Red List
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Geospatial based model for malaria risk prediction in Kilombero Valley, south-eastern Tanzania
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Fish slough remote sensing and geospatial database
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Geospatial data from: Identifying opportunity hot spots for reducing the risk of wildfire-caused carbon loss in western US conifer forests
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FIGURE 4 in Quantifying vertebrate zoogeographical regions of Australia using geospatial turnover in the species composition of mammals, birds, reptiles and terrestrial amphibians
FIGURE 4. The zoogeographical regions proposed by Cracraft (1991, thick black lines) and superimposed over the three clusters found in this study.
urbisphere-Berlin Analysis Ready Geospatial dataset
<p>Analysis-Ready (ARD) Geospatial dataset (v3) for the administrative boundaries of Berlin produced by FORTH in the framework of urbisphere. The dataset includes a) Digital Surface Model (DSM) b) Digital Terrain Model (DTM) c) Building Heights d) Vegetation Heights e) Land Cover (SUEWS format i.e. 1=Paved/Impervious, 2=Buildings, 3=Evergreen Trees and Shrubs, 4=Deciduous Trees and Shrubs , 5=Grass, 6=Bare Soil, 7=Water) in raster format (.tif).</p> <p>Resolution: 1m<br>CRS: EPSG:25833<br>noData: -999 / -9999<br>compression: LZW</p> <p>Reference year: 2021</p> <p>LC reported overall accuracy 89% (v2)</p>
Geospatial_Ocean color
<p>This is dataset for the Geospatial class.</p> <p>Source: ECMWF</p>
Data from: A data-driven geospatial workflow to map species distributions for conservation assessments
<p>We developed a geospatial workflow that refines the distribution of a species from its extent of occurrence (EOO) to area of habitat (AOH) within the species range map. The range maps are produced with an inverse distance weighted (IDW) interpolation procedure using presence and absence points derived from primary biodiversity data (GBIF and eBird hotspots respectively). Here we provide sample data to run the geospatial workflow for nine forest species across Mexico and Central America.</p>
Metadata for Geospatial Mapping Tools, Indicators and Metrics for Fish Habitat in the Pacific Region
<p>Metadata on geospatial tools, indicators, metrics and scoring benchmarks useful for assessing the status of threats to freshwater fish habitat in British Columbia</p>
Data from: Breeding system and geospatial variation shape the population genetics of Triodanis perfoliata
<p><span>Both intrinsic and extrinsic forces work together to shape connectivity and genetic variation in populations across the landscape. Here we explored how geography, breeding system traits, and environmental factors influence the population genetic patterns of <em>Triodanis perfoliata</em>, a widespread mix-mating annual plant in the contiguous US. By integrating population genomic data with spatial analyses and modeling the relationship between breeding system and genetic diversity, we illustrate the complex ways in which these forces shape genetic variation. Specifically, we used 4,705 single nucleotide polymorphisms to assess genetic diversity, structure, and evolutionary history among 18 populations. Populations with more obligately selfing flowers harbored less genetic diversity (π: R<sup>2</sup> = 0.63, P = 0.01, n = 9 populations), and we found significant population structuring (F<sub>ST</sub> = 0.48). Both geographic isolation and environmental factors played significant roles in predicting the observed genetic diversity: we found that corridors of suitable environment appear to facilitate gene flow between populations, and that environmental resistance is correlated with increased genetic distance between populations. Last, we integrated our genetic results with species distribution modeling to assess likely patterns of connectivity among our study populations. Our landscape and evolutionary genetic results suggest that <em>T. perfoliata</em> experienced a complex demographic and evolutionary history, particularly in the center of its distribution. As such, there is no singular mechanism driving this species' evolution. Together, our analyses support the hypothesis that breeding system, geography, and environmental variables shape the patterns of diversity and connectivity of <em>T. perfoliata</em> in the US. </span></p>
Supplementary data- Detailed Geogenic Radon Potential Mapping Using Geospatial Analysis of Multiple Geo-variables. A Case Study from a High-Risk Area in SE Ireland.
<p>Supplementary Data: all the data produced and used, statistical analysis, mathematical proofs diagnostic tests, regression models and additional charts and tables</p> <p>Detailed Geogenic Radon Potential Mapping Using Geospatial Analysis of Multiple Geo-variables. A Case Study from a High-Risk Area in SE Ireland. </p>
Supplementary material 7 from: Seltmann K, Lafia S, Paul D, James S, Bloom D, Rios N, Ellis S, Farrell U, Utrup J, Yost M, Davis E, Emery R, Motz G, Kimmig J, Shirey V, Sandall E, Park D, Tyrrell C, Thackurdeen R, Collins M, O'Leary V, Prestridge H, Evelyn C, Nyberg B (2018) Georeferencing for Research Use (GRU): An integrated geospatial training paradigm for biocollections researchers and data providers. Research Ideas and Outcomes 4: e32449. https://doi.org/10.3897/rio.4.e32449
This document contains an annotated set of data quality checks that participants report they use when evaluating and cleaning datasets. These items outline how participants are judging if the data suits their purpose.
Supplementary material 3 from: Seltmann K, Lafia S, Paul D, James S, Bloom D, Rios N, Ellis S, Farrell U, Utrup J, Yost M, Davis E, Emery R, Motz G, Kimmig J, Shirey V, Sandall E, Park D, Tyrrell C, Thackurdeen R, Collins M, O'Leary V, Prestridge H, Evelyn C, Nyberg B (2018) Georeferencing for Research Use (GRU): An integrated geospatial training paradigm for biocollections researchers and data providers. Research Ideas and Outcomes 4: e32449. https://doi.org/10.3897/rio.4.e32449
The informed consent request and workshop survey questions given to participants after the workshop each day for 4 consecutive days.
Supplementary material 2 from: Seltmann K, Lafia S, Paul D, James S, Bloom D, Rios N, Ellis S, Farrell U, Utrup J, Yost M, Davis E, Emery R, Motz G, Kimmig J, Shirey V, Sandall E, Park D, Tyrrell C, Thackurdeen R, Collins M, O'Leary V, Prestridge H, Evelyn C, Nyberg B (2018) Georeferencing for Research Use (GRU): An integrated geospatial training paradigm for biocollections researchers and data providers. Research Ideas and Outcomes 4: e32449. https://doi.org/10.3897/rio.4.e32449
This document shows just the questions we asked the applicants who applied to participate in this Georeferencing for Research Use workshop. We used a Google Form to deliver these questions and collect responses. It is both an application and serves as our pre-workshop survey.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.