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20 results for “GIS analysis”
GIS Protocol for Multy-Scale Emerging Hot Spot Analysis
<p>This GIS protocol is primarily intended as supplementary material to the article (Štular et al., 2022). The article contains important contextual information about its intended use. In short, this GIS protocol was developed for the purposes of archaeological regional analysis of spatial data. The data are provided elsewhere in spreadsheet format (Štular et al., 2021). Data in GIS format are included in this repository. The GIS protocol can be used with any relevant data for any purpose as long as the data format matches the format of the included data.</p> <p>Includes GIS protocol (textual description) and GIS data in *.shp format.</p>
GRASS GIS database for CASAS-PBDM (www.casasglobal.org) geospatial mapping and analysis
<p>GRASS GIS database for geospatial mapping and analysis of physiologically based demographic modeling (PBDM) implemented by the Center for the Analysis of Sustainable Agricultural Systems (CASAS, <a href="https://www.casasglobal.org/" target="_blank" rel="noopener">www.casasglobal.org</a>).</p> <p>The <code>casas_gis_grass8data.zip</code> archive includes data updated for use with GRASS GIS version 8.</p>
Supplementary GIS data - Potential and implications of automated pre-processing of LiDAR-based digital elevation models for large-scale archaeological landscape analysis
<p>A supplementary dataset related to the paper discussing preparation of a digital elevation model derived from DMR 5G (LiDAR-based DEM of the Czech Republic) cleaned of modern artificial features. It includes data used as a clipping mask and data produced during the testing phase.</p> <p>Contents:</p> <ul> <li>..\clipping_buffers.gdb\ - Clipping buffers based on ZABAGED dataset used for masking the original data stored as ESRI geodatabase.</li> <li>..\drainages\ - Drainages with Strahler order higher than four (potential watercourses) for the original and filtered DEMs. <ul> <li>drainages_filtered - Drainges identified in the filtered DEM stored as GeoTIFF.</li> <li>drainages_original - Drainges identified in the original DEM stored as GeoTIFF. </li> </ul> </li> <li>..\LSC\ - Locations with significant land surface curvature for the original and filtered DEMs. <ul> <li>LSC_filtered - Significant LSC identified in the filtered DEM stored as GeoTIFF. </li> <li>LSC_original - Significant LSC identified in the original DEM stored as GeoTIFF. </li> </ul> </li> <li>..\visibility\ - Viewsheds computed over the original and filtered DEMs. <ul> <li>Libice\ - Sample viewsheds computed for the early medieval hillfort of Libice. <ul> <li>Libice_visibility_filtered - Viewshed based on the filtered DEM stored as GeoTIFF. </li> <li>Libice_visibility_original - Viewshed based on the original DEM stored as GeoTIFF. </li> <li>observer_points - Observer points used for calculating the viewsheds.</li> </ul> </li> <li>regular_grid\ - Cumulative viewsheds calculated for regularly spaced points in a 10 x 10 km grid with a visibility radius of 5 km and an observer height of 2 m; a total of 574 viewsheds. <ul> <li>visibility_filtered - Cumulative viewshed for the filtered DEM stored as GeoTIFF.</li> <li>visibility_original - Cumulative viewshed for the original DEM stored as GeoTIFF. </li> <li>visibility_test_buffers - Buffers used for the viewshed calculations stored as ESRI shapefile.</li> <li>visibility_test_observers - Observer points used for the viewshed calculations stored as ESRI shapefile.</li> </ul> </li> </ul> </li> </ul> <p> </p> <p>Preprint version of the related paper:</p> <p>Novák, David and Pružinec, Filip, Potential and Implications of Automated Pre-Processing of Lidar-Based Digital Elevation Models for Large-Scale Archaeological Landscape Analysis. Available at SSRN: <a href="https://ssrn.com/abstract=4063514">https://ssrn.com/abstract=4063514</a></p>
2021 UN Open GIS Challenge 1 - Training on Satellite Data Analysis and Machine Learning with QGIS (Satellite_QGIS)
<p>This dataset is part of the <a href="https://www.osgeo.org/foundation-news/2021-osgeo-un-committee-educational-challenge/?fbclid=IwAR0UvwkPO2pay7C0tJawb63eewjBGfeL9TIQpYUFccza9OIo6HAolmHXLWE">2021 UN Open GIS Challenge 1 - Training on Satellite Data Analysis and Machine Learning with QGIS (Satellite_QGIS)</a>,</p> <p>Exercise 1: Supervised Change Detection: Monitoring deglaciation in Huascaran, Peru.</p>
Capacity Building for GIS-based SDG Indicator Analysis with Global High-resolution Land Cover Datasets - training datasets
<p>Sample datasets for the <strong>Case Studies</strong> section of the <em> Capacity Building for GIS-based SDG Indicator Analysis with Global High-resolution Land Cover Datasets </em>web book (<a href="https://isprs-gis-sdg.readthedocs.io">https://isprs-gis-sdg.readthedocs.io</a>)</p>
Climate data and geographic data from Madagascar for learning multi-criteria analysis in GIS courses
<p>Climate data and geographic data from Madagascar for learning multi-criteria analysis in GIS courses. </p> <ul> <li><strong>MultiCriteriaAnalysis_ENG_v2023_v4.pdf</strong> - summary of Multi-Criteria Analysis methods for suitability of mango tree fruitculture.</li> <li><strong>MultiCriteriaAnalysis_simple_ENG_v2023_v4.xlsx</strong> - sheets with support material fromPDF and videos in lecture channel</li> <li><strong>Country borders</strong>: https://www.naturalearthdata.com/downloads/50m-cultural-vectors/50m-admin-0-countries-2/</li> <li><strong>Rivers</strong>: https://www.naturalearthdata.com/downloads/10m-physical-vectors/</li> <li><strong>Roads</strong>: https://www.naturalearthdata.com/downloads/50m-cultural-vectors/ </li> <li><strong>Global land cover classes - GLOBCOVER</strong>: http://due.esrin.esa.int/page_globcover.php</li> <li><strong>Temperature and precipiatation </strong>- climate variables: clipped from WorldClim database rasters (see http://www.worldclim.org/) </li> <li><strong>Digital Elevation Model - DEM </strong>from EarthExplorer web portal (see https://www.cirgeo.unipd.it/didattica/GIS/01x_access_geodata.html#USGS:_EarthExplorer<br> </li> </ul>
Supplemental data to Incense Road' from Petra to Gaza: an analysis using GIS and cost functions
<p>Base data and results in GIS format (shapefiles) of the research 'Incense Road’ from Petra to Gaza: an analysis using GIS and cost functions' </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>
GIS output and layers for the physiologically based demographic modeling analysis of interacting olive and olive fly in Andalusia
<p>GIS output and layers for the physiologically based demographic modeling analysis of interacting olive and olive fly in Andalusia performed under the MED-GOLD project within the Horizon 2020 framework program of the European Union. This version includes the file eurocordex_HadGEM2_ES_RCA4_2071-2100_rcp8.5.zip that was inadvertently not included in previous versions.</p>
The application of unmanned aerial vehicle (UAV) surveys and GIS to the analysis and monitoring of recreational trail conditions - dataset
<p>This dataset contains data used to test the protocol for high-resolution mapping and monitoring of recreational impacts in protected natural areas (PNAs) using unmanned aerial vehicle (UAV) surveys, Structure-from-Motion (SfM) data processing and geographic information systems (GIS) analysis to derive spatially coherent information about trail conditions (Tomczyk et al., 2023). Dataset includes the following folders:</p> <ol> <li>Cocora_raster_data (~3GB) and Vinicunca_raster_data (~32GB) - a very high-resolution (cm-scale) dataset derived from UAV-generated images. Data covers selected recreational trails in Colombia (Valle de Cocora) and Peru (Vinicunca). UAV-captured images were processed using the structure-from-motion approach in Agisoft Metashape software. Data are available as GeoTIFF files in the UTM projected coordinate system (UTM 18N for Colombia, UTM 19S for Peru). Individual files are named as follows [location]_[year]_[product]_[raster cell size].tif, where: <ul> <li>[location] is the place of data collection (e.g., Cocora, Vinicucna)</li> <li>[year] is the year of data collection (e.g., 2023)</li> <li>[product] is the tape of files: DEM = digital elevation model; ortho = orthomosaic; hs = hillshade</li> <li>[raster cell size] is the dimension of individual raster cell in mm (e.g., 15mm)</li> </ul> </li> <li> <p>Cocora_vector_data. and Vinicunca_vector_data – mapping of trail tread and conditions in GIS environment (ArcPro). Data are available as shp files. Data are in the UTM projected coordinate system (UTM 18N for Colombia, UTM 19S for Peru).</p> </li> </ol> <p>Structure-from-motio<span> </span>n processing was performed in Agisoft Metashape (<a href="https://www.agisoft.com/">https://www.agisoft.com/</a>, Agisoft, 2023). Mapping was performed in ArcGIS Pro (<a href="https://www.esri.com/en-us/arcgis/about-arcgis/overview">https://www.esri.com/en-us/arcgis/about-arcgis/overview</a>, Esri, 2022). Data can be used in any GIS software, including commercial (e.g. ArcGIS) or open source (e.g. QGIS).</p> <p>Tomczyk, A. M., Ewertowski, M. W., Creany, N., Monz, C. A., & Ancin-Murguzur, F. J. (2023). The application of unmanned aerial vehicle (UAV) surveys and GIS to the analysis and monitoring of recreational trail conditions. <em>International Journal of Applied Earth Observations and Geoinformation</em>, 103474. doi:<a href="https://doi.org/10.1016/j.jag.2023.103474"> https://doi.org/10.1016/j.jag.2023.103474</a></p>
Distribution pattern of rocky desertification in southwest China and analysis of its main driving factors based on GIS and Geodetector
<p>Rocky desertification, a pressing environmental concern in Southwest China, significantly impacts local living conditions and regional sustainability. Employing remote sensing on a macro scale, this study focuses on identifying and analyzing the spatial distribution and driving factors of rocky desertification. Conducted in Southwest China, using Landsat data from Google Earth Engine for 2020, the research quantitatively extracts information on rocky desertification patches through traditional methods. Excluding unlikely areas using land use data, spatial distribution features and driving factors are examined via GIS spatial analysis and a geodetector model. The main conclusions are as follows. Rocky desertification covers 217,530.4 km<sup>2</sup> (accounting for 15.6% of Southwest China), with areas of slight, moderate, and severe rocky desertification at 81.3%, 7.1%, and 11.6%, respectively. Spatially, rocky desertification primarily occurs in areas where lithology is carbonate rock between clastic rocks and continuous limestone, slope exceeds 15°, elevation ranges is 1000–2000 m, land use types are grassland and woodland, precipitation is 80–120 mm, and population density is below 50 people/km<sup>2</sup>. Human activities have minimal influence. Geodetector analysis identifies lithology, land use type, and slope as primary driving factors, with interactive effects of lithology and land use type and slope and land use type jointly influencing rocky desertification formation in Southwest China.</p>
Data analysis of article research tittle "Online GIS and Remote Sensing-Based Mapping of Flood Vulnerability in Samarinda Seberang Subdistrict"
<p>This dataset contains an explanation of data analysis for creating a flood vulnerability map of Samarinda Seberang District. The dataset contains sub-criteria for each flood parameter and its score value. In addition, this dataset contains the weight value of each parameter, flood vulnerability level and its coloring, and the results of calculating the area of each vulnerability level.</p>
Data from: Regional paleoclimates and local consequences: Integrating GIS analysis of diachronic settlement patterns and process-based agroecosystem modeling of potential agricultural productivity in Provence (France)
Open the record for dataset details and reuse information.
Distribution pattern of rocky desertification in southwest China and analysis of its main driving factors based on GIS and Geodetector
Open the record for dataset details and reuse information.
GIS Shapefile - Analysis of potential stewardship in support of BES research, residential.
Potential stewardship for Residential parcels in Baltimore City summarized by block group. Residential was defined as those parcels with a land use code of residential, residential commercial, residential condominium, or apartments based on the 2003 Maryland Property View A&T database. PRIZM 5, 15, and 62 classes are also present. PRIZM is the Potential Rating Index by Zip code Markets produced by the Claritas Corporation - (http://www.clusterbigip1.claritas.com/claritas/Default.jsp). Total Potential stewardship is that land within a parcels not occupied by buildings, that is land that could potentially support vegetation, regardless of whether or not any vegetation is present. Realized Potential Stewardship is land that is currently occupied by vegetation. Not Realized Potential Stewardship is the land not occupied by buildings or existing vegetation, and is thus the land that is potentially available for "greening" initiatives. Normalization for realized and not realized potential stewardship is carried out by dividing by the total potential stewardship. The potential stewardship was calculated using parcel data, building footprints, and GDT census block groups. Building footprints were erased from the parcel area, resulting in a layer indicating the potential stewardship for each parcel. The potential stewardship layer was then unioned MD DNR's 2001 SUFA vegetation layer. All polygons corresponding to water features were deleted since water features cannot undergo "greening." All polygons that did fall in the potential stewardship area were deleted. This resulted in a layer in which the polygons represented the potential stewardship land along with the potential stewardship land occupied by either grass or trees. This layer was then intersected with the census block group layer resulting in a layer that had the potential stewardship land, potential stewardship vegetation, and block group IDs. All attributes were then summarized at the block group level. A cursory
GIS Shapefile - Analysis of potential stewardship in support of BES research, parcel level
Parcel-level potential stewardship for Baltimore City. Potential stewardship is that land within a parcel not occupied by buildings, that is land that could potentially undergo "greening." This dataset contians polygons that represent potential stewardship land along with the vegetation that falls within the potential stewardship land. Potential stewardship should be estimated using the polygons with a land use (LU) code equal to 0. Parcel land use codes and census block group information is also present. A cursory analysis of the parcel data indicated that parcel data was outdated for the following block groups: 245102503031, 245102503032, and 245102503033. Note: transportation networks are not part of the parcel data, and thus were appropriately not part of this analysis. In addition a single BLOCKLOT may consist of two or more parcels in certain instances.
GIS Shapefile - Analysis of potential stewardship in support of BES research, block group
Potential stewardship for Baltimore City summarized by block group. PRIZM 5, 15, and 62 classes are also present. PRIZM is the Potential Rating Index by Zip code Markets produced by the Claritas corportation - (http://www.clusterbigip1.claritas.com/claritas/Default.jsp). Potential stewardship is that land within a parcels not occupied by buildings, that is land that could potentially undergo "greening." Not Realized Potential Stewardship is the land not occupied by buildings or existing vegetation, and is thus the land that is potentially available for "greening" initiatives. This dataset provides several summarizations at the block group level: 1) total potential stewardship area, 2) not realized potential stewardship, 3) normalized potential stewardship (potential stewardship area / block group area), 4) normalized not realized potential stewardship (not realized potential stewardship area / block group area), and 5) tree potential stewardship area. The potential stewardship was calculated using parcel data, building footprints, and GDT census block groups. Building footprints were erased from the parcel area, resulting in a layer indicating the potential stewardship for each parcel. The potential stewardship layer was then unioned MD DNR's SUFA vegetation layer. All polygons corresponding to water features were deleted since water features cannot undergo "greening." All polygons that did fall in the potential stewardship area were deleted. This resulted in a layer in which the polygons represented the potential stewardship land along with the potential stewardship land occupied by either grass or trees. This layer was then intersected with the census block group layer resulting in a layer that had the potential stewardship land, potential stewardship vegetation, and block group IDs. All attributes were then summarized at the block group level. Total Potential Stewardship (Tot_PotStew) is the area of parcel land in a block group that is not occupied by buildings o
GIS Shapefile - Analysis of potential stewardship in support of BES research, Baltimore City, block group
Potential stewardship for Residential parcels in Baltimore City summarized by block group. Residential parcels was defined as only those parcels with a land use code of residential (LU_CODE = "R") based on the 2003 Maryland Property View A&T database. PRIZM 5, 15, and 62 classes are also present. PRIZM is the Potential Rating Index by Zip code Markets produced by the Claritas Corporation - > http://www.clusterbigip1.claritas.com/claritas/Default.jsp>. Total Potential stewardship is that land within a parcels not occupied by buildings, that is land that could potentially support vegetation, regardless of whether or not any vegetation is present. Realized Potential Stewardship is land that is currently occupied by vegetation. Not Realized Potential Stewardship is the land not occupied by buildings or existing vegetation, and is thus the land that is potentially available for "greening" initiatives. Normalization for realized and not realized potential stewardship is carried out by dividing by the total potential stewardship. The potential stewardship was calculated using parcel data, building footprints, and GDT census block groups. Building footprints were erased from the parcel area, resulting in a layer indicating the potential stewardship for each parcel. The potential stewardship layer was then unioned MD DNR's 2001 SUFA vegetation layer. All polygons corresponding to water features were deleted since water features cannot undergo "greening." All polygons that did fall in the potential stewardship area were deleted. This resulted in a layer in which the polygons represented the potential stewardship land along with the potential stewardship land occupied by either grass or trees. This layer was then intersected with the census block group layer resulting in a layer that had the potential stewardship land, potential stewardship vegetation, and block group IDs. All attributes were then summarized at the block group level. A cursory analysis of the parcel data indic
GIS Data and Analysis for Cooling Demand and Environmental Impact in The Hague
<p>This dataset contains raw GIS data sourced from the BAG (<i>Basisregistratie Adressen en Gebouwen</i>; Registry of Addresses and Buildings). It provides comprehensive information on buildings, including advanced height data and administrative details. It also contains geographic divisions within The Hague. Additionally, the dataset incorporates energy label data, offering insights into the energy efficiency and performance of these buildings. This combined dataset serves as the backbone of a Master's thesis in Industrial Ecology, analysing residential and office cooling and its environmental impacts in The Hague, Netherlands. The codebase of this analysis can be found in this Github repository: <a href="https://github.com/simonvanlierde/msc-thesis-ie"><strong>https://github.com/simonvanlierde/msc-thesis-ie</strong></a></p><p>The dataset includes a background research spreadsheet containing supporting calculations. It also presents geopackages with results from the cooling demand model (CDM) for various scenarios: Status quo (SQ), 2030, and 2050 scenarios (Low, Medium, and High)</p><h3>Background research data</h3><p>The <i>background_research_data.xlsx</i><strong> </strong>spreadsheet contains comprehensive background research calculations supporting the shaping of input parameters used in the model. It contains several sheets:</p><ul><li><strong>Cooling Technologies</strong>: Details the various cooling technologies examined in the study, summarizing their characteristics and the market penetration mixes used in the analysis.</li><li><strong>LCA Results of Ventilation Systems</strong>: Provides an overview of the ecoinvent processes serving as proxies for the life-cycle impacts of cooling equipment, along with calculations of the weight of cooling systems and contribution tables from the LCA-based assessment.</li><li><strong>Material Scarcity</strong>: A detailed examination of the critical raw material content in the material footprint of ecoinvent processes, representing cooling equipment.</li><li><strong>Heat Plans per Neighbourhood</strong>: Forecasts of future heating solutions for each neighbourhood in The Hague.</li><li><strong>Building Stock</strong>: Analysis of the projected growth trends in residential and office building stocks in The Hague. AC Market: Market analysis covering air conditioner sales in the Netherlands from 2002 to 2022.</li><li><strong>Climate Change</strong>: Computations of climate-related parameters based on KNMI climate scenarios.</li><li><strong>Electricity Mix Analysis</strong>: Analysis of future projections for the Dutch electricity grid and calculations of life-cycle carbon intensities of the grid.</li></ul><h3>Input data</h3><p><strong>Geographic divisions</strong></p><ul><li>The outline of The Hague municipality through the Municipal boundaries (<i>Gemeenten</i>) layer, sourced from the <a href="http://www.pdok.nl/geo-services/-/article/bestuurlijke-gebieden">Administrative boundaries (<i>Bestuurlijke Gemeenten</i>) dataset</a> on the PDOK WFS service.</li><li>District (<i>Wijken</i>) and Neighbourhood (<i>Buurten</i>) layers were downloaded from the PDOK WFS service (from the <a href="https://www.pdok.nl/geo-services/-/article/cbs-wijken-en-buurten#df0df8fa1c3bab1a71a2f09d990abd7e"><i>CBS Wijken en Buurten 2022</i></a><i> </i>data package) and clipped to the outline of The Hague.</li><li>The 4-digit postcodes layer was downloaded from PDOK WFS service (<a href="http://www.pdok.nl/ogc-webservices/-/article/cbs-postcode4"><i>CBS Postcode4 statistieken 2020</i></a>) and clipped to The Hague's outline. The postcodes within The Hague were subsequently stored in a csv file.</li><li>The census block layer was downloaded from the PDOK WFS service (from the <a href="http://www.pdok.nl/introductie/-/article/cbs-vierkantstatistieken-100m"><i>CBS Vierkantstatistieken 100m 2021</i></a> data package) and also clipped to the outline of The Hague.</li><li>These layers have been combined in the <i>GeographicDivisions_TheHague</i> GeoPackage.</li></ul><p><strong>BAG data</strong></p><ul><li>BAG data was acquired through the download of a BAG GeoPackage from the BAG <a href="https://www.pdok.nl/downloads/-/article/basisregistratie-adressen-en-gebouwen-ba-1">ATOM download page</a>.</li><li>In the resulting GeoPackage, the Residences (<i>Verblijfsobject</i>) and Building (<i>Pand</i>) layers were clipped to match The Hague's outline.</li><li>The resulting residence data can be found in the <i>BAG_buildings_TheHague</i> GeoPackage.</li></ul><p><strong>3D BAG </strong></p><ul><li>Due to limitations imposed by the PDOK WFS service, which restricts the number of downloadable buildings to 10,000, it was necessary to acquire 145 individual GeoPackages for tiles covering The Hague from the <a href="http://3dbag.nl/nl/download">3D BAG website</a>.</li><li>These GeoPackages were merged using the <i>ogr2ogr</i> <i>append</i> function from the <a href="http://gdal.org/index.html">GDAL library </a>in bash.</li><li>Roof elevation data was extracted from the <i>LoD 1.2 2D</i> layer from the resulting GeoPackage.</li><li>Ground elevation data was obtained from the <i>Pand</i> layer.</li><li>Both of these layers were clipped to match The Hague's outline.</li><li>Roof and ground elevation data from the <i>LoD 1.2 2D</i> and <i>Pand</i> layers were joined to the <i>Pand</i> layer in the BAG dataset using the <i>BAG ID</i> of each building.</li><li>The resulting data can be found in the <i>BAG_buildings_TheHague</i> GeoPackage.</li></ul><p><strong>Energy labels</strong></p><ul><li>Energy labels were downloaded from the <a href="http://www.ep-online.nl/PublicData">Energy label registry</a> (<i>EP-online</i>) and stored in <i>energy_labels_TheNetherlands</i>.<i>csv</i>.</li></ul><p><strong>UHI effect data</strong></p><ul><li>A bitmap with the UHI effect intensity in The Hague was retrieved from the from the <a href="https://www.atlasnatuurlijkkapitaal.nl/kaarten?config=58bf95bc-67bf-402d-a355-af211ad33949&gm-x=121187.11870218973&gm-y=467370.5793842884&gm-z=3.1666666666666665&gm-b=1544180834512,true,1;1554714019959,true,0.8;&activateOnStart=layermanager&deactivateOnStart=layercollection">Dutch Natural Capital Atlas</a> (<i>Atlas Natuurlijk Kapitaal</i>) and stored in <i>UHI_effect_TheHague.tiff</i>.</li></ul><h3>Output data</h3><ul><li>The residence-level data joined to the building layer is contained in the <i>BAG_buildings_with_residence_data_full</i> GeoPackage.</li><li>The results for each building, according to different scenarios, are compiled in the <strong>buildings_with_CDM_results_[scenario]_full</strong> GeoPackages. The scenarios are abbreviated as follows:<ul><li><strong>SQ</strong>: Status Quo, covering the 2018-2022 reference period.</li><li><strong>2030</strong>: An average scenario projected for the year 2030.</li><li><strong>2050_L</strong>: A low-impact, best-case scenario for 2050.</li><li><strong>2050_M</strong>: A medium-impact, moderate scenario for 2050.</li><li><strong>2050_H</strong>: A high-impact, worst-case scenario for 2050.</li></ul></li></ul><p> </p>
GIS integration for spatial analysis of urban expansion in Tisaleo canton (Ecuador).
<p><span>Political or economic interests have led to an empirical zonal delimitation, and the deficient use of Geographic Information Systems does not allow the specific characterisation of the territories. This article aims to determine the spatial analysis of the urban expansion areas of the canton of Tisaleo by integrating Geographic Information Systems, using a quantitative research method. The main components analysed are: the situational diagnosis of the canton through the characterisation of its biophysical component, its human settlements and the public system of facilities and social services that support it. Based on this information, an interrelation of the data collected is carried out to complement a spatial analysis with GIS software. The results show that in the canton of Tisaleo there are areas suitable for urban growth, but their limits are overdimensioned, i.e. they include areas that do not have the aptitude to be urbanised, as well as rural areas that have the characteristics to be considered urban.</span></p>
ScienceDex guides
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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.