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19 results for “Geographic Information Systems”
Geographic Information System for marine aquaculture in Argentina
<p>Planning the use of marine areas for aquaculture through the development of Geographic Information Systems (GIS) has taken on great importance recently . This is because GIS allows decision-making through the analysis and integration of a large amount of data of various kinds gathered in a single database. This system allows the incorporation of information on optimal environmental conditions for farm species and relevant data to develop strategies throughout the entire production chain, from service providers and inputs to the final marketing of the product. The recommended actions of the strategic guidelines for a more sustainable and competitive EU aquaculture in 2021–2030 (EC 2021) stated explicitly the need to “<em>Develop a more detailed guidance document on the planning for space and access to water for marine, freshwater and land-based aquaculture</em>”, highlighting the importance of the GIS.</p> <p>Here you will find 4 files with the following information:<br>1) <strong><em>Metadata.doc</em></strong> file with the details of the metadata used to diagram the GIS layers.<br>2) <em><strong>GIS.gpkg</strong></em> file with each of the layers in raster and vector format.<br>3) <em><strong>Land-based model.gpkg</strong></em> file with examples of GIS modeling for land-based facilities.<br>4) <strong><em>Open-water model.gpkg</em></strong> file with examples of GIS modeling for facilities in open systems.</p> <p> </p>
Analysis of Determining the Location of Public Electric Battery Exchange Stations (SPBKLU) using The Buffer Method in The Geographical Information System (GIS) in The Central Jakarta Region (Case Study of PT. XYZ)
<p>Figure 1. The Existing Station Map in the Central Jakarta Area</p> <p><strong><span>Figure 2.</span></strong><span> The Suitability of Battery Replacement Station Location and Closeness to Alfamart Supermarket </span></p> <p><strong><span>Figure 3.</span></strong><span> The Suitability of Battery Replacement Station Location and Closeness to District Office </span></p> <p><strong><span>Figure 4.</span></strong><span> The Suitability of Battery Replacement Station Location in the Central Jakarta Area</span></p> <p><span>Data (The Variable Weight & The Variable Criteria)</span></p>
FIG. 3 in Development of a Geographical Information System for the marine resources of Rodrigues
FIG. 3. GIS frames illustrating how the survey sites (pink diamonds) in the Rodrigues lagoon are linked to a photograph of the site, a description of the biotope, a species list and photographs of the species found at that site.
FIG. 5. GIS frames illustrating a in Development of a Geographical Information System for the marine resources of Rodrigues
FIG. 5. GIS frames illustrating a section of the biotope map from the southern lagoon to show how polygons are labelled and hyperlinked to illustrated descriptions of the constituent biotopes.
FIG. 10 in Development of a Geographical Information System for the marine resources of Rodrigues
FIG. 10. Thematic map from the GIS to show all proposed Marine Protected Areas, including those shown in figure 9 and the approximate positions of the new MPAs (orange boundaries) proposed by Shoals Rodrigues in 2003 and the Terrestrial–Marine Protected Area (green boundary) proposed by the United Nations Development Programme in 2004. Underlying the MPA boundaries layer is a biotope layer showing the Coral (red and pink infill) and Lagoon mud (brown infill) biotopes only, for clarity.
FIG. 4 in Development of a Geographical Information System for the marine resources of Rodrigues
FIG. 4. The biotope map of the Rodrigues lagoon as a vector image with the Landsat 7 ETMz satellite image of the land area as a backdrop for orientation and interpretation purposes. The biotopes are illustrated using colour schemes according to habitat groups and biotopes, as described in figure 2.
FIG. 1. True colour Landsat 7 in Development of a Geographical Information System for the marine resources of Rodrigues
FIG. 1. True colour Landsat 7 ETMz satellite image of Rodrigues as a raster image in TIF file format to form the base layer for the GIS. The island of Rodrigues is about 18 km in length.
FIG. 2 in Development of a Geographical Information System for the marine resources of Rodrigues
FIG. 2. Hierarchical biotope classification for the reef-fringed lagoon of Rodrigues. The colour bar indicates the colour used to illustrate the habitat group of biotopes in the biotope map (see figure 4).
FIG. 9 in Development of a Geographical Information System for the marine resources of Rodrigues
FIG. 9. Thematic map from the GIS to show the existing Fisheries Reserves (blue boundaries) Fisheries Act 75 (1984) and proposed Marine Reserves (yellow boundaries) and Core Areas (pink boundaries) suggested by Pearson (1988). The underlay shows the major habitat types of Coral (red); Consolidated limestone (orange); Lagoon muds (brown); Sand and rubble with moderate to dense vegetation (dark green) and with sparse vegetation (light green).
Geographic Information System of structural elements in the Niobe-Aphrodite Map Area of Venus: a tool for structural and geologic analysis.
<p>The Niobe Aphrodite Map Area covers over 25% of the surface of Venus and extends from 57N to 57S and 60E to 180E. The structural-element map presented here is derived from the1:10 M-scale geologic maps of Niobe Planitia, U.S. Geological Survey I-2467 and Aphrodite Terra, U.S. Geological Survey I-2476. Both maps are in various stages of review and revision overseen by the U.S. Geological Survey on behalf of NASA.</p> <p>Here we present a Geographic Information System (GIS) that contain the different structural elements of the area (deformation structures and lithodemic units), that can be used to analyze relationships between and among suites of structural elements across this large portion of Venus’ surface.</p> <p>Base images and data on which determination of the structural element determination is based can be accessed and downloaded directly in GIS-ready formats through the USGS Map a Planet website (https://astrogeology.usgs.gov/tools/map-a-planet-2).</p>
Application of Geographical Information System Data in the Emergency Department, Effect on Trauma Team and Medical Emergency Team Wait
ClinicalTrials.gov study NCT02188966. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: New net zooplankton geographical information system in the Far East seas and adjacent waters of the Pacific Ocean
Open the record for dataset details and reuse information.
Data from: Modeling spatial patterns of soil respiration in maize fields from vegetation and soil property factors with the use of remote sensing and geographical information system
To examine the method for estimating the spatial patterns of soil respiration (Rs) in agricultural ecosystems using remote sensing and geographical information system (GIS), Rs rates were measured at 53 sites during the peak growing season of maize in three counties in North China. Through Pearson's correlation analysis, leaf area index (LAI), canopy chlorophyll content, aboveground biomass, soil organic carbon (SOC) content, and soil total nitrogen content were selected as the factors that affected spatial variability in Rs during the peak growing season of maize. The use of a structural equation modeling approach revealed that only LAI and SOC content directly affected Rs. Meanwhile, other factors indirectly affected Rs through LAI and SOC content. When three greenness vegetation indices were extracted from an optical image of an environmental and disaster mitigation satellite in China, enhanced vegetation index (EVI) showed the best correlation with LAI and was thus used as a proxy for LAI to estimate Rs at the regional scale. The spatial distribution of SOC content was obtained by extrapolating the SOC content at the plot scale based on the kriging interpolation method in GIS. When data were pooled for 38 plots, a first-order exponential analysis indicated that approximately 73% of the spatial variability in Rs during the peak growing season of maize can be explained by EVI and SOC content. Further test analysis based on independent data from 15 plots showed that the simple exponential model had acceptable accuracy in estimating the spatial patterns of Rs in maize fields on the basis of remotely sensed EVI and GIS-interpolated SOC content, with R2 of 0.69 and root-mean-square error of 0.51 µmol CO2 m−2 s−1. The conclusions from this study provide valuable information for estimates of Rs during the peak growing season of maize in three counties in North China.
Supplementary files for: A geographic information system-based global variable renewable potential assessment using spatially resolved simulation
<p>File 1: Supplementary materials on methods</p> <p>File 2: Definition of the zones in this study</p> <p>File 3: Statistics on land cover pixels updated from OSM</p> <p>File 4: Validation of the eligibility masks with existing installation sites</p> <p>File 5: Validation of the estimated CF profiles for the European countries</p> <p>File 6: Summary of the available area, technical potential and capacity potential of wind and solar power in this study</p> <p>File 7: Aggregation of the estimated 10-year hourly CF into 288 month-hour profile</p> <p>File 8: The estimated 10-year hourly CF of all available technologies and CF tranches in each zone</p>
Geographical Information System (GIS) Mapping to Assess the Need for IEP Provision in Tayside
ClinicalTrials.gov study NCT04099719. IPD Sharing: Not stated. Countries: 1. Publications: 0.
GeoHealth: Geographic Information System for Health Management and Clinical, Epidemiological and Translational Research
ClinicalTrials.gov study NCT05637411. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Modeling spatial patterns of soil respiration in maize fields from vegetation and soil property factors with the use of remote sensing and geographical information system
Open the record for dataset details and reuse information.
FIG. 6 in Development of a Geographical Information System for the marine resources of Rodrigues
FIG. 6. Database form from the Access database to show the Species Record form listing sites where found and relative abundance (six-point SACFOR scale) and a hyperlink displays a photograph of the species.
FIG. 7 in Development of a Geographical Information System for the marine resources of Rodrigues
FIG. 7. Database form from the Access database illustrating how the Sites and Species Records form is linked to a site picture, biotope description, species photograph and scanned image of some species within the GIS.
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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.