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138 results for “Geospatial”
Results from the OnStove Nepal model "Achieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis"
<p>This repository includes all result datasets and figures from the <a href="https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal">OnStove Nepal</a> model presented in the paper "<strong>Achieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis</strong>" DOI: <a href="https://doi.org/10.1016/S2542-5196(24)00209-2">https://doi.org/10.1016/S2542-5196(24)00209-2</a>.</p> <p>The code and automated workflow to run the model can be found in the Github repository <a href="https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal">https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal</a>. All model input data can be downloaded from the permanent repository at<em> </em><a href="https://doi.org/10.5281/zenodo.10641858">10.5281/zenodo.10641858</a>.</p> <h2>Folder structure</h2> <p>The folder structure consists of a <strong>Procedded GIS Data </strong>folder containing all GIS processed data. These are the outputs from the <strong>DataProcessor.ipynb </strong>script and the raw GIS input data files found in the input data repository.</p> <p>A folder for <strong>each scenario</strong> results. Within each scenario folder, there are:</p> <ul> <li>A <strong>model.pkl </strong>and a <strong>results.pkl </strong>files. These are a calibrated OnStove model with the scenario inputs and a complete results model file of the scenario respectively. Both of these files can be read and explored using the OnStove tool. </li> <li>A <strong>summary.csv </strong>file with the summary results of the scenario for each technology.</li> <li>A <strong>Subsidies_scenario_name.csv </strong>file showing the required total subsidies per technology of the scenario.</li> <li>Image files in pdf format for: <ul> <li>The baseline technologies used in the country (<strong>current_shares.pdf</strong>),</li> <li>The spatial mix of technologies providing the maximum net-benefits throughout the country (<strong>max_benefit_tech.pdf</strong>), </li> <li>The total costs and benefits of the transition per technology (<strong>costs_benefits.pdf</strong>),</li> <li>The bar plot of max benefit technology shares (<strong>tech_split.pdf</strong>),</li> <li>The max benefit technologies distribution over relative wealth in the country (<strong>tech_histogram.pdf</strong>),</li> </ul> </li> <li>A <strong>Rasters </strong>folder with raster files of different result maps in .tif format.</li> </ul> <p>Inside the <strong>MCA </strong>folder, all results from the prioritization analysis are found, including:</p> <ul> <li>The prioritized spatial technology mix to achieve the goals of the country (<strong>Prioritized_hh.pdf</strong>),</li> <li>The biogas cookstoves relative wealth distribution index (<strong>Biogas_index.pdf</strong>),</li> <li>The biomass ICS T3 cookstoves relative wealth distribution index (<strong>Biomass_ICS_T3_index.pdf</strong>),</li> <li>The electrical cookstoves relative wealth distribution index (<strong>Electricity_index.pdf</strong>),</li> <li>The biogas cookstoves priority map (<strong>Biogas_priority_areas.pdf</strong>),</li> <li>The biomass ICS T3 cookstoves priority map (<strong>Biomass_ICS_T3_priority_areas.pdf</strong>),</li> <li>The electrical cookstoves priority map (<strong>Electricity_priority_areas.pdf</strong>),</li> <li>The total costs and benefits of the transition per technology (<strong>costs_benefits.pdf</strong>),</li> <li>The prioritized technology shares distribution over relative wealth in the country (<strong>tech_histogram_prioritized.pdf</strong>),</li> <li>A <strong>Subsidies_prioritized.csv </strong>file showing the required total subsidies per technology,</li> <li>A <strong>mca.pkl </strong>file with the MCA model that can be manipulated using the OnStove tool,</li> <li>A <strong>access_results.txt </strong>file with the current and after prioritization clean cooking access shares in the country.</li> </ul> <p>A <strong>main_plot.pdf </strong>and a <strong>prioritized_plot.pdf </strong>files showing the compiled results for all scenarios and prioritized scenario respectively.</p> <h2>License</h2> <p>All datasets are released under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a> (CC BY 4.0).</p>
A geospatial dataset of lichen key attributes in the Earth's three poles
<p>To develop the geospatial dataset, we initially defined two lichen attributes: color type and growth form. These attributes were chosen due to their significant correlation with lichen physiological and biochemical characteristics, as well as their association with reflection spectra. Each record of this geospatial dataset consists of information such as scientific name, longitude, latitude, ecoregion name, biome name, color type, growth form, and the occurranceID belongs to the GBIF original dataset.</p> <p>This dataset serves as a foundational resource for extensive investigations into the intricate interplay between lichen physiology and the environment, addressing a significant knowledge gap in the field. Furthermore, our dataset holds the potential to address challenges associated with remote sensing monitoring of lichens, a longstanding issue in vegetation remote sensing. Precise in situ observation records, as provided by our dataset, can facilitate the development of remote sensing techniques tailored for lichen monitoring.</p> <p>"Program" is the code used in the process of establishing the geospatial dataset of lichen key attributes in the Earth’s three poles.</p> <p> </p>
Geospatial data on indicators for parks in the city of Berlin, Germany
<p>The data contains features and indicators for 224 parks (at least 2 ha in size) in the city of Berlin and overall scores (indices) for natural elements, built elements (infrastructure) and spatial context (e.g. distance to public transport). All data is supplement to linked online web map.</p> <p><strong>List of data and content</strong></p> <ul> <li>Park_Berlin_Indicators: vector files (*.shp, *.geojson)</li> <li>Park_Berlin_Indicators: excel files (*.xlsx)</li> </ul> <p><strong>Spatial reference</strong><br> All data is projected in ETRS 1989 UTM Zone 33N (<a href="https://spatialreference.org/ref/epsg/25833/">EPSG:25833</a>)</p> <p><strong>Web-GIS</strong><br> View data and explore interactively using the <a href="https://arcg.is/5a9me">online application.</a></p> <p><strong>Data sources and processing</strong><br> For details on underlying data sources (e.g. availabilty, spatial resolution, time reference) and on data processing please refer to the linked publication, incl. Appendix 1</p> <p><strong>Acknowledgments</strong><br> We thank the City of Berlin for providing data. We greatly acknowledge OpenStreetMap (OSM) and contributers for providing important parts of the used data. This work was supported by the research project “Environmental‐Health Interactions in Cities (GreenEquityHEALTH) ‐ Challenges for Human Well‐Being under Global Changes” (project duration 2017–2022), funded by the German Federal Ministry of Education and Research (BMBF; no.01LN1705A).</p> <p><strong>Based on related original publication</strong><br> Kraemer, R., & Kabisch, N. (2021). Parks in context: Advancing citywide spatial quality assessments of urban green spaces using fine-scaled indicators. Ecology and Society, 26(2). <a href="https://doi.org/10.5751/ES-12485-260245">https://doi.org/10.5751/ES-12485-260245 </a></p>
Nationwide geospatial dataset of environmental covariates at 1km resolution in Mexico
<p><strong>Package of 39 covariates, a combination of topographic, climatic, and vegetation derived variables with pixel sizes of 1000 m for the period of 2009 to 2014 from google earth engine (GEE) to assemble a nationwide geospatial dataset of Mexico.</strong><br> <strong>Datasets included WorldClim V1; a set of bioclimatic variables derived from the monthly temperature and rainfall <a href="https://www.zotero.org/google-docs/?tmZL4M">(Hijmans, 2005)</a>; time-series analysis of Landsat images from the Hansen Global Forest Change v1.8 (2000-2020) dataset <a href="https://www.zotero.org/google-docs/?jGj5Rn">(Hansen et al., 2013)</a>; 4-day composite dataset from Moderate Resolution Imaging Spectro-radiometer (MODIS) sensors with fraction of photosynthetic active radiation and leaf area index at 500-m resolution <a href="https://www.zotero.org/google-docs/?t45qy3">(Myneni, Ranga et al., 2015)</a> and the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Emissivity Database (2000-2008) <a href="https://www.zotero.org/google-docs/?8bVVc9">(Hulley et al., 2009, 2012, 2015; Hulley & Hook, 2008, 2009, 2011; NASA JPL, 2014)</a>. All covariates were resampled to 1000 m. The resampling was done with conventional bilinear interpolation as implemented in GEE.</strong></p>
Synthetic geospatial data for performance analysis of geospatial database systems
<p>This dataset contains a set of synthetic data that can be used to evaluate the efficiency of geosaptial datasbases. </p> <p>The datasets is composed of four json file, characterized by different size. They can be used to analyze the scalability of geospatial datasets with respect to the database size.</p> <p>Each json file contains a set of "points", each one characterized by a set of random attributes (description, url of a picture linked to the point, creation date, delete date, update date, identifier, partition identifier).</p> <p>The synthetically generated points are uniformly distributed among the world.</p>
ArcGIS Map Packages and GIS Data for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al. (2019)
<p><strong>ArcGIS Map Packages and GIS Data for Gillreath-Brown, Nagaoka, and Wolverton (2019)</strong></p> <p>**When using the GIS data included in these map packages, please cite all of the following:</p> <blockquote> <p>Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, 2019. PLoSONE 14(8):e0220457. <a href="http://doi.org/10.1371/journal.pone.0220457">http://doi.org/10.1371/journal.pone.0220457</a></p> <p>Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. ArcGIS Map Packages for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al., 2019. Version 1. Zenodo. <a href="https://doi.org/10.5281/zenodo.2572018">https://doi.org/10.5281/zenodo.2572018</a></p> </blockquote> <p><strong>OVERVIEW OF CONTENTS</strong></p> <p>This repository contains map packages for Gillreath-Brown, Nagaoka, and Wolverton (2019), as well as the raw digital elevation model (DEM) and soils data, of which the analyses was based on. The map packages contain all GIS data associated with the analyses described and presented in the publication. The map packages were created in ArcGIS 10.2.2; however, the packages will work in recent versions of ArcGIS. (Note: I was able to open the packages in ArcGIS 10.6.1, when tested on February 17, 2019). The primary files contained in this repository are:</p> <ul> <li>Raw DEM and Soils data <ul> <li>Digital Elevation Model Data (Map services and data available from U.S. Geological Survey, National Geospatial Program, and can be downloaded from the <a href="https://viewer.nationalmap.gov/basic/">National Elevation Dataset</a>) <ul> <li><strong>DEM_Individual_Tiles</strong>: Individual DEM tiles prior to being merged (1/3 arc second) from USGS National Elevation Dataset.</li> <li><strong>DEMs_Merged</strong>: DEMs were combined into one layer. Individual watersheds (i.e., Goodman, Coffey, and Crow Canyon) were clipped from this combined DEM. </li> </ul> </li> <li> Soils Data (Map services and data available from <a href="https://data.nal.usda.gov/dataset/natural-resources-conservation-service-web-soil-survey">Natural Resources Conservation Service Web Soil Survey</a>, U.S. Department of Agriculture) <ul> <li><strong>Animas-Dolores_Area_Soils</strong>: Small portion of the soil mapunits cover the northeastern corner of the Coffey Watershed (CW).</li> <li><strong>Cortez_Area_Soils</strong>: Soils for Montezuma County, encompasses all of Goodman (GW) and Crow Canyon (CCW) watersheds, and a large portion of the Coffey watershed (CW).</li> </ul> </li> </ul> </li> <li>ArcGIS Map Packages <ul> <li><strong>Goodman_Watershed_Full_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the full Goodman Watershed (GW).</li> <li><strong>Goodman_Watershed_Mesa-Only_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the mesa-only Goodman Watershed.</li> <li><strong>Crow_Canyon_Watershed_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the Crow Canyon Watershed (CCW).</li> <li><strong>Coffey_Watershed_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the Coffey Watershed (CW).</li> </ul> </li> </ul> <p>For additional information on contents of the map packages, please see see "Map Packages Descriptions" or open a map package in ArcGIS and go to "properties" or "map document properties."</p> <p><strong>LICENSES</strong></p> <p>Code: <a href="http://opensource.org/licenses/MIT">MIT</a> year: 2019 <br> Copyright holders: Andrew Gillreath-Brown, Lisa Nagaoka, and Steve Wolverton</p> <p><strong>CONTACT</strong></p> <p><strong>Andrew Gillreath-Brown, PhD Candidate, RPA</strong><br> <a href="https://anthro.wsu.edu/">Department of Anthropology</a>, Washington State University<br> <a href="mailto:andrew.brown1234@gmail.com">andrew.brown1234@gmail.com</a> – Email<br> <a href="https://andrewgillreathbrown.wordpress.com/">andrewgillreathbrown.wordpress.com</a> – Web</p>
The first geospatial dataset of irrigated fields (2020-2024) in Vojvodina (Serbia)
<p><span>Irrigation is a cornerstone of global food security, enabling sustainable agricultural production and helping to ensure that food is available for people around the world, now and in the future. Mapping irrigated fields provides valuable information for sustainable water management, agricultural development, and environmental conservation efforts. However, the collection of high-quality training data, which is necessary for accurate irrigation mapping remains costly and labour-intensive. To address this, we created a georeferenced regional dataset consisting of location, crop type, and occurrence of the irrigation equipment which are essential information for mapping irrigated fields. Four main irrigated crops were considered: maize, soybean, sugar beet, and wheat. The dataset<span>,</span> consisting of a total of 1256 parcels<span>,</span> is created for Vojvodina, the main agricultural area in Serbia, spanning the period of five years (2020 - 2024). This study’s goal is to give accessibility to our dataset which further can be explored and used for building or fine-tuning machine learning and deep learning models for the automatic detection of irrigated fields using satellite imagery.</span></p>
GRIDCERF: Geospatial Raster Input Datasets for Capacity Expansion Regional Feasibility
<p>Geospatial Raster Input Datasets for Capacity Expansion Regional Feasibility (GRIDCERF) is a data package containing all the necessary input layers for the <a href="https://github.com/IMMM-SFA/cerf">Capacity Expansion Regional Feasibility (CERF) model</a>. The CERF model uses these layers to find feasible power plant siting locations at a 1 kilometer scale across the conterminous United States for renewable and non-renewable electricity production technologies. This package encompasses a wide variety of geospatial layers pertaining to restrictions, regulations, and challenges that come with siting new electricity production facilities.</p>
Geospatial Analysis of Economic Development in kenya by Province
<p>This dataset presents both vector and raster data combinations for pm2.5, elevation, nightlight data, population density, area, and population that can be used to estimate the economic development of Kenya using distribution of banks as a proxy.</p>
Geospatial analysis of mining areas reclamation potential through Technosols in Brazil
<p>This repository contains two datasets:</p> <p>1. An update of metadata analysis with data published before 2021 resulting from the search equation "TS = (Technosol* AND (Organic carbon OR Organic matter)" in the Web of Science (WOS) database. Update from Allory 2022: https://doi.org/10.24396/ORDAR-60.</p> <p>2. A database containing geospatial datasets (inputs and outputs), R scripts, and other FOSS software files used for the geospatial analysis of land reclamation potential through Technosols in Brazil.</p>
Geospatial, biophysical and socioeconomic data for the Athens municipality on a zipcode resolution
<p>A collated dataset from various remote sensing, local and national sources. </p>
LAGOS-NE – Lake nutrient chemistry and geospatial data to measure spatial structure of ecosystem properties in a 17-state region of the U.S.
This dataset includes data for the lake water quality and geospatial variables that describe climate, hydrology, land use land cover, and lake characteristics that were used to study spatial structure in lake properties at the sub-continental scales (Lapierre et al. Quantifying spatial structure to improve understanding of the relationships between climate, landscape, and lake ecosystem properties, to be submitted to Ecology). All observations came from LAGOS-NELIMNO v. 1.054.1 and LAGOS-NEGEO v. 1.03 (LAke multi-scaled GeOSpatial and temporal database), an integrated database of lake ecosystems (Soranno et al. 2015). LAGOS-NE contains a complete census of lakes great than or equal to 4 ha with corresponding geospatial information for a 17-state region of the U.S., and a subset of the lakes has observational data on morphometry and chemistry. Approximately 54 different sources of data were compiled for the LAGOS-NELIMNO v. 1.054.1 dataset and were mostly generated by government agencies (state, federal, tribal) and universities. In this analysis, we compiled lake water quality data from the summer stratified season (June 15-September 15) in the most recent 10 years of data included in LAGOS-NELIMNO v. 1.054.1 (2002-2011). We report the median total nitrogen, total phosphorus, secchi depth, and chlorophyll values for each lake, which was calculated as the grand median of each yearly median value. We also include data for lake and landscape characteristics including variables related to lake morphometry, climate, hydrology, atmospheric deposition, land use and land cover.
LAGOS-NE-GIS v1.0: A module for LAGOS-NE, a multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of U.S. Lakes: 2013-1925
This data package, LAGOS-NE-GIS v1.0, is 1 of 5 data packages associated with the LAGOS-NE database-- the LAke multi-scaled GeOSpatial and temporal database. Three of the data packages each contain different types of data for 51,101 lakes and reservoirs larger than 4 ha in 17 lake-rich U.S. states to support research on thousands of lakes. These three package are: (1) LAGOS-NE-LOCUS v1.01: lake location and physical characteristics for all lakes. (2) LAGOS-NE-GEO v1.05: ecological context (i.e., the land use, geologic, climatic, and hydrologic setting of lakes) for all lakes. These geospatial data were created by processing national-scale and publicly-accessible datasets to quantify numerous metrics at multiple spatial resolutions. And, (3) LAGOS-NE-LIMNO v1.087.1: in-situ measurements of lake water quality from the past three decades for approximately 2,600-12,000 lakes, depending on the variable. This module was created by harmonizing 87 water quality datasets from federal, state, tribal, and non-profit agencies, university researchers, and citizen scientists. The other two data packages contain supporting data for the LAGOS-NE database: (4) LAGOS-NE-GIS v1.0: the GIS data layers for lakes, wetlands, and streams, as well as the spatial resolutions that were used to create the LAGOS-NE-GEO module. (5) LAGOS-NE-RAWDATA: the original 87 datasets of lake water quality prior to processing, the R code that converts the original data formats into LAGOS-NE data format, and the log file from this procedure to create LAGOS-NE. This latter data package supports the reproducibility of LAGOS-NE-LIMNO. The LAGOS-NE GIS v1.0 module includes GIS datasets for: lake polygons and their hydrologic classification; wetland polygons and their classification; streams as a line coverage and their classification by stream order; the zones used for this study (state and county; hydrologic units [at the 4, 8 and 12 scales]); and, lake watersheds (IWS). We also include boundaries of U.S. stat
Gap Fire Perimeter (Santa Barbara County, CA), July 9, 2008 - From Geospatial Multi-Agency Coordination Group (GeoMAC)
The Gap Fire burned from 2008-07-01 to 2008-07-28, Lizard's Mouth area of Los Padres National Forest, Santa Barbara County. Approximately 9544 acres were burned (information per http://cdfdata.fire.ca.gov). This dataset contains a KML polygon showing the extent of the fire on 2008-07-09, and was acquired by request from the Geospatial Multi-Agency Coordination Group (GeoMAC, http://www.geomac.gov). These data are based upon input from incident intelligence sources, Global Positioning System (GPS) data, and infrared (IR) imagery. See methods for more information.
Demographic, economic, geospatial data for municipalities of the Central Federal District in Russia (excluding the city of Moscow and the Moscow oblast) in 2010-2016
<p>The database contains demographic, economic, geospatial data for 452 municipalities of the 16 administrative units of the Central Federal District in Russia (excluding the city of Moscow and the Moscow oblast) for 2010-2016.</p> <p>The sources of data are the municipal-level statistics of Rosstat, Google Maps data and calculated indicators. The statistical data were arranged by the year, the data on municipalities for which there were administrative and territorial transformations for the period under study were excluded (in some cases, the data were provided in accordance with the administrative-territorial demarcation as of 2016).</p> <p>Municipalities' websites were used to fill the lack of population information in individual municipalities for some years.</p> <p>Calculated variables were made to estimate a number of indicators per capita, to introduce additional demographic indicators (e.g. migration inflow rate), to bring price economic indicators to base year prices (2010). For example, indicators of income of the local budget, volumes of investments in fixed assets (excluding budgetary funds), level of wages are modified to a comparable form (to 2010 prices).</p> <p>The distances on roads in different units of measurement from the geographical center of municipalities to the center of the capital of the region are calculated using the Google Maps database.</p> <p>Data mapping was performed using ArcGIS software.</p> <p>The data set consists of</p> <p>1) Municipalities_CFD_Russia_2010_2016_ENG.xlsx - The database of demographic, economic, geospatial data for 452 municipalities of the 16 administrative units of the Central Federal District in Russia (excluding the city of Moscow and the Moscow oblast) for 2010-2016,</p> <p>2) MUNICIPALITIES_CFD_RUSSIA_SHAPE.rar - The shape-files for maps construction,</p> <p>3) Fig.1. Municipalities ENG.jpg - The map of studied administrative units and municipalities of the Central Federal District in Russia .</p>
India Flood Inventory-Impacts (IFI-Impacts) [1967-2023]: A multi-source national geospatial database to facilitate comprehensive flood research
<p>This repository hosts the India Flood Inventory with Impacts (IFI-Impacts) database. It contains flood event data sourced from the Indian Meteorological Department from 1967-2023. It has undergone extensive manual digitization, cleaning, and includes new information to make it suitable for computational research in hydroclimate.</p> <p>v4.0: Development of District Flood Severity Index (DFSI)</p> <p>v3.0: India Flood Inventory (IFI) 1967-2023. Updated with local government codes (LGD) for state and district. </p> <p>v1.0: India Flood Inventory (IFI) 1967-2016.</p> <p>v2.0: India Flood Inventory (IFI) 1967-2023. With impacts and district flooded area.</p> <p><strong>REFERENCES</strong></p> <p>Saharia, M., Jain, A., Baishya, R.R., Haobam, S., Sreejith, O.P., Pai, D.S., Rafieeinasab, A., 2021. India flood inventory: creation of a multi-source national geospatial database to facilitate comprehensive flood research. Nat Hazards. <a href="https://doi.org/10.1007/s11069-021-04698-6">https://doi.org/10.1007/s11069-021-04698-6</a></p> <div> <div>Saharia, M., Jain, S.K., Prakash, V., Malik, H., Sreejith, O.P., Joshi, D., 2025. A district-level flood severity index for flood management in India. Nat Hazards. <a href="https://doi.org/10.1007/s11069-025-07493-9">https://doi.org/10.1007/s11069-025-07493-9</a></div> </div>
GRIDCERF: Geospatial Raster Input Data for Capacity Expansion Regional Feasibility
<p><strong>Abstract:</strong></p> <p>Climate change, energy system transitions, and socioeconomic change are compounding influences affecting the growth of electricity demand. While energy efficiency initiatives and distributed resources can address a significant amount of this demand, the United States will likely still need new utility-scale generation resources. The energy sector uses capacity expansion planning models to determine the aggregate need for new generation, but these models are typically at the state or regional scale and are not equipped to address the wide range of location- and technology-specific issues that are increasingly a factor in power plant siting. To help address these challenges, we have developed the Geospatial Raster Input Data for Capacity Expansion Regional Feasibility (GRIDCERF) data package, a high-resolution product to evaluate siting suitability for renewable and non-renewable power plants in the conterminous United States. GRIDCERF offers 265 suitability layers for use with 56 power plant technology configurations in a harmonized format that can be easily ingested by geospatially-enabled modeling software. It also provides pre-compiled technology-specific suitability layers and allows for user customization to robustly address science objectives when evaluating varying future conditions.</p> <p><strong>Accompanying GitHub repository:</strong></p> <p>The following GitHub repository contains the code used to generate the data in this archive: https://github.com/IMMM-SFA/vernon-etal_2023_scidata</p> <p><strong>Contents:</strong></p> <p><strong>Note:</strong></p> <p>GRIDCERF does not provide the source data directly due to some license restrictions related for direct redistribution of the unaltered source data. However, the included file "gridcerf_source_data_description.csv" details the provenance associated with each source dataset and notes their individual licenses/disclaimers.</p> <p><strong>Common Rasters:</strong></p> <p><strong>Suitability Layer Type and Source</strong></p> <p><strong>GRIDCERF Raster Name</strong></p> <p><strong>Bureau of Land Management (BLM) Surface Management Agency Areas</strong><strong><sup>33</sup></strong></p> <p>gridcerf_blm_surface_management_agency_areas.tif</p> <p><strong>BLM National Landscape Conservation System (NLCS) - National Monuments</strong><strong><sup>34</sup></strong></p> <p>gridcerf_blm_nlcs_national_monument_conus.tif</p> <p><strong>BLM NLCS - Outstanding Natural Areas</strong><strong><sup>35</sup></strong></p> <p>gridcerf_blm_nlcs_outstanding_natural_areas_conus.tif</p> <p><strong>BLM NLCS - Wilderness</strong><strong><sup>36</sup></strong></p> <p>gridcerf_blm_nlcs_wilderness_conus.tif</p> <p><strong>BLM NLCS - Wilderness Study Areas</strong><strong><sup>37</sup></strong></p> <p>gridcerf_blm_nlcs_wilderness_study_areas_conus.tif</p> <p><strong>National Park Service (NPS) Class 1 airsheds</strong><strong><sup>38</sup></strong></p> <p>gridcerf_class1_airsheds_conus.tif</p> <p><strong>NPS Administrative Boundaries</strong><strong><sup>39</sup></strong></p> <p>gridcerf_nps_administrative_boundaries_conus.tif</p> <p><strong>NPS Historic Trails</strong><strong><sup>40</sup></strong></p> <p>gridcerf_nps_historic_trails_conus.tif</p> <p><strong>NPS Scenic Trails</strong><strong><sup>41</sup></strong></p> <p>gridcerf_nps_scenic_trails_conus.tif</p> <p><strong>U.S. Fish and Wildlife Service (USFWS) - Critical Habitat</strong><strong><sup>42</sup></strong></p> <p>gridcerf_usfws_critical_habitat_conus.tif</p> <p><strong>USFWS - Special Designation</strong><strong><sup>43</sup></strong></p> <p>gridcerf_usfws_special_designation_conus.tif</p> <p><strong>USFWS - Wild and Scenic River System</strong><strong><sup>44</sup></strong></p> <p>gridcerf_usfws_national_wild_scenic_river_system_conus.tif</p> <p><strong>USFWS - National Realty Tracts</strong><strong><sup>45</sup></strong></p> <p>gridcerf_usfws_national_realty_tracts_conus.tif</p> <p><strong>National Land Cover Dataset (NLCD) Wetlands</strong><strong><sup>46</sup></strong></p> <p>gridcerf_nlcd_wetlands_conus.tif</p> <p><strong>U.S. Forest Service (USFS) Administrative Boundaries</strong><strong><sup>47</sup></strong></p> <p>gridcerf_usfs_administrative_boundaries_conus.tif</p> <p><strong>USFS Wilderness Areas</strong><strong><sup>48</sup></strong></p> <p>gridcerf_usfs_wilderness_areas_conus.tif</p> <p><strong>U.S. Geological Survey (USGS) National Wilderness Lands</strong><strong><sup>49</sup></strong></p> <p>gridcerf_usgs_wilderness_areas_conus.tif</p> <p><strong>USGS Protected Areas of the U.S - Class 1&2</strong><strong><sup>50</sup></strong></p> <p>gridcerf_usgs_padus_class_1_to_2_conus.tif</p> <p><strong>U.S. State Protected Lands</strong><strong><sup>51</sup></strong></p> <p>gridcerf_wdpa_state_protected_lands_conus.tif</p> <p><strong>Nature Conservancy lands</strong><strong><sup>52</sup></strong></p> <p>gridcerf_wdpa_tnc_managed_lands_conus.tif</p> <p> </p> <p><strong>Technology-specific Rasters:</strong></p> <p><strong>Suitability Layer Type and Source</strong></p> <p><strong>GRIDCERF Raster Name</strong></p> <p><strong>Bureau of Indian Affairs (BIA) Land Area Representations Dataset</strong><strong><sup>53</sup></strong></p> <p>gridcerf_bia_land_area_representations_conus.tif</p> <p><strong>Slope 5% or less suitable</strong><strong><sup>20</sup></strong></p> <p>gridcerf_srtm_slope_5pct_or_less.tif</p> <p><strong>Slope 10% or less suitable</strong><strong><sup>20</sup></strong></p> <p>gridcerf_srtm_slope_10pct_or_less.tif</p> <p><strong>Slope 12% or less suitable</strong><strong><sup>20</sup></strong></p> <p>gridcerf_srtm_slope_12pct_or_less.tif</p> <p><strong>Slope 20% or less suitable</strong><strong><sup>20</sup></strong></p> <p>gridcerf_srtm_slope_20pct_or_less.tif</p> <p><strong>Airports (10-mile buffer)</strong><strong><sup>54</sup></strong></p> <p>gridcerf_airports_10mi_buffer_conus.tif</p> <p><strong>Airports (3-mile buffer)</strong><strong><sup>54</sup></strong></p> <p>gridcerf_airports_3mi_buffer_conus.tif</p> <p><strong>Proximity to Railroad and Navigable Waters (< 5 km)</strong> <strong><sup>55,56</sup></strong></p> <p>gridcerf_usdot_railnodes_navwaters_within5km.tif</p> <p><strong>Coal Supply</strong><strong><sup>55–57</sup></strong></p> <p>gridcerf_coalmines20km_railnodes5km_navwaters5km_conus.tif</p> <p><strong>United States Environmental Protection Agency (EPA) CO Non-attainment Areas</strong><strong><sup>58</sup></strong></p> <p>gridcerf_epa_nonattainment_co_conus.tif</p> <p><strong>EPA NOx Non-attainment Areas</strong><strong><sup>58</sup></strong></p> <p>gridcerf_epa_nonattainment_no2_conus.tif</p> <p><strong>EPA Ozone Non-attainment Areas</strong><strong><sup>58</sup></strong></p> <p>gridcerf_epa_nonattainment_ozone_conus.tif</p> <p><strong>EPA Lead Non-attainment Areas</strong><strong><sup>58</sup></strong></p> <p>gridcerf_epa_nonattainment_lead_conus.tif</p> <p><strong>EPA PM10 Non-attainment Areas</strong><strong><sup>58</sup></strong></p> <p>gridcerf_epa_nonattainment_pm10_conus.tif</p> <p><strong>EPA PM2.5 Non-attainment Areas</strong><strong><sup>58</sup></strong></p> <p>gridcerf_epa_nonattainment_pm2p5_conus.tif</p> <p><strong>EPA SOx Non-attainment Areas</strong><strong><sup>58</sup></strong></p> <p>gridcerf_epa_nonattainment_so2_conus.tif</p> <p><strong>Earthquake Potential</strong><strong><sup>59</sup></strong></p> <p>gridcerf_usgs_earthquake_pga_0.3_at_2pct_in_50yrs_conus.tif</p> <p><strong>Densely population areas</strong><strong><sup>11</sup></strong></p> <p>gridcerf_densely_populated_ssp[2,3,5]_[year].tif</p> <p><strong>Densely population areas buffered by 25 miles</strong><strong><sup>11</sup></strong></p> <p>gridcerf_densely_populated_ssp[2,3,5]_[year]_buff25mi.tif</p> <p><strong>Densely population areas – nuclear</strong><strong><sup>11</sup></strong></p> <p>gridcerf_densely_populated_ssp[2,3,5]_[year]_nuclear.tif</p> <p><strong>National Hydrography Dataset (version 2; NHDv2)</strong><strong><sup>32</sup></strong></p> <p>gridcerf_nhd2plus_surfaceflow_greaterthan[bin]mgd_buffer20km.tif</p> <p><strong>National Renewable Energy Laboratory (NREL) concentrating solar direct normal potential</strong><strong><sup>26</sup></strong></p> <p>gridcerf_nrel_solar_csp_centralized_potential.tif</p> <p><strong>NREL photovoltaic potential</strong><strong><sup>26</sup></strong></p> <p>gridcerf_nrel_solar_pv_centralized_potential.tif</p> <p><strong>NREL Wind Integration National Dataset (WIND) toolkit</strong><strong><sup>22</sup></strong></p> <p>gridcerf_nrel_wind_development_potential_hubheight[080,110,140]_cf35.tif</p> <p> </p> <p><strong>Compiled Technology Rasters:</strong></p> <p>The list of layers that make up each compiled technology raster can be found in the "reference/compiled_layer_configuration.txt" file in this data archive.</p> <p>The following technology raster file names are self-descriptive in the format "gridcerf_<technology>_<subtype>_<carbon_capture>_<cooling_type>.tif". Some technologies do not have a carbon capture or cooling type designation and will simply have technology specific considerations listed.</p> <pre>gridcerf_biomass_conventional_ccs_dry.tif gridcerf_biomass_conventional_ccs_oncethrough.tif gridcerf_biomass_conventional_ccs_recirculating.tif gridcerf_biomass_conventional_no-ccs_dry.tif gridcerf_biomass_conventional_no-ccs_oncethrough.tif gridcerf_biomass_conventional_no-ccs_pond.tif gridcerf_biomass_conventional_no-ccs_recirculating.tif gridcerf_biomass_igcc_no-ccs_dry.tif gridcerf_biomass_igcc_no-ccs_oncethrough.tif gridcerf_biomass_igcc_no-ccs_recirculating.tif gridcerf_biomass_igcc_with-ccs_dry.tif gridcerf_biomass_igcc_with-ccs_oncethrough.tif gridcerf_biomass_igcc_with-ccs_recirculating.tif gridcerf_coal_conventional_ccs_dry.tif gridcerf_coal_conventional_ccs_oncethrough.tif gridcerf_coal_conventional_ccs_recirculating.tif gridcerf_coal_conventional_no-ccs_dry.tif gridcerf_coal_conventional_no-ccs_oncethrough.tif gridcerf_coal_conventional_no-ccs_pond.tif gridcerf_coal_conventional_no-ccs_recirculating.tif gridcerf_coal_igcc_no-ccs_dry.tif gridcerf_coal_igcc_no-ccs_oncethrough.tif gridcerf_coal_igcc_no-ccs_recirculating.tif gridcerf_coal_igcc_with-ccs_dry.tif gridcerf_coal_igcc_with-ccs_oncethrough.tif gridcerf_coal_igcc_with-ccs_recirculating.tif gridcerf_gas_cc_ccs_dry.tif gridcerf_gas_cc_ccs_oncethrough.tif gridcerf_gas_cc_ccs_recirculating.tif gridcerf_gas_cc_no-ccs_dry.tif gridcerf_gas_cc_no-ccs_oncethrough.tif gridcerf_gas_cc_no-ccs_pond.tif gridcerf_gas_cc_no-ccs_recirculating.tif gridcerf_gas_turbine_dry.tif gridcerf_gas_turbine_oncethrough.tif gridcerf_gas_turbine_pond.tif gridcerf_gas_turbine_recirculating.tif gridcerf_nuclear_gen3_oncethrough.tif gridcerf_nuclear_gen3_pond.tif gridcerf_nuclear_gen3_recirculating.tif gridcerf_refinedliquids_cc_ccs_dry.tif gridcerf_refinedliquids_cc_ccs_oncethrough.tif gridcerf_refinedliquids_cc_ccs_recirculating.tif gridcerf_refinedliquids_cc_no-ccs_dry.tif gridcerf_refinedliquids_cc_no-ccs_oncethrough.tif gridcerf_refinedliquids_cc_no-ccs_recirculating.tif gridcerf_refinedliquids_ct_dry.tif gridcerf_refinedliquids_ct_oncethrough.tif gridcerf_refinedliquids_ct_pond.tif gridcerf_refinedliquids_ct_recirculating.tif gridcerf_solar_csp_centralized_dry-hybrid.tif gridcerf_solar_csp_centralized_recirculating.tif gridcerf_solar_pv_centralized.tif gridcerf_wind_onshore_hubheight080m.tif gridcerf_wind_onshore_hubheight110m.tif gridcerf_wind_onshore_hubheight140m.tif </pre> <p><strong>Reference Data: </strong>Contains land mask and other useful boundary data. Also contains additional literature review resource and the layers used to build the compiled suitability.</p> <p><strong>References:</strong></p> <p>1. Bureau of Land Management. BLM National Surface Management Agency Area Polygons - National Geospatial Data Asset (NGDA). <em>Landscape Approach Data Portal</em> https://gbp-blm-egis.hub.arcgis.com/datasets/blm-national-sma-surface-management-agency-area-polygons/about (2023).</p> <p>2. Bureau of Land Management. BLM National NLCS National Monuments, National Conservation Areas and Similar Designations Polygons. <em>U.S.Department of Interior Bureau of Land Management Geospatial Business Plaform</em> https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-natl-nlcs-national-monuments-national-conservation-areas-polygons/about (2023).</p> <p>3. Hartger, P. NLCS Outstanding Natural Areas. <em>ArcGIS Hub</em> https://hub.arcgis.com/datasets/Wilderness::nlcs-outstanding-natural-areas/about (2017).</p> <p>4. Bureau of Land Management. BLM National NLCS Wilderness Areas Polygons. <em>U.S.Department of Interior Bureau of Land Management Geospatial Business Plaform</em> https://arcg.is/a01uC (2023).</p> <p>5. Bureau of Land Management. BLM National NLCS Wilderness Study Areas Polygons. <em>U.S.Department of Interior</em> https://arcg.is/14XPiC (2023).</p> <p>6. United States Environmental Protection Agency. Mandatory Class 1 Federal Areas Web Service. <em>Mandatory Class 1 Federal Areas Web Service</em> https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7BF234E37B-E7CE-4A47-89D6-68399D540576%7D (2015).</p> <p>7. National Park Service. National Park Service Boundary. <em>National Park Service</em> https://public-nps.opendata.arcgis.com/datasets/nps::nps-boundary-4/about (2023).</p> <p>8. National Park Service. National Historic Trails. <em>Geospatial Energy Mapper</em> https://gem.anl.gov/tool (2019).</p> <p>9. National Park Service. National Scenic Trails. <em>Geospatial Energy Mapper</em> https://gem.anl.gov/tool (2019).</p> <p>10. U.S. Fish and Wildlife Service. USFWS Threatened & Endangered Species Active Critical Habitat Report. <em>ECOS Environmental Conservation Online System</em> https://ecos.fws.gov/ecp/report/table/critical-habitat.html (2023).</p> <p>11. U.S. Fish and Wildlife. U.S. Fish and Wildlife Special Designation. <em>U.S. Fish and Wildlife</em> https://gis-fws.opendata.arcgis.com/datasets/fws::fws-special-designation/about (2023).</p> <p>12. U.S. Fish and Wildlife Service. National Wild and Scenic Rivers System. <em>Geospatial Energy Mapper</em> https://data.fs.usda.gov/geodata/edw/edw_resources/shp/S_USA.WildScenicRiver_LN.zip (2014).</p> <p>13. U.S. Fish and WIldlife Service. USFWS National Realty Tracts. <em>ServCat Fish and Wildlife Service Catalog</em> https://ecos.fws.gov/ServCat/Reference/Profile/154057 (2023).</p> <p>14. Dewitz, J. & U.S. Geological Survey. National Land Cover Database (NLCD) 2019 Products (ver. 2.0, June 2021). <em>Multi-Resoltuion Land Characteristics Consortium</em> https://www.mrlc.gov/data/nlcd-2019-land-cover-conus (2021).</p> <p>15. USDA Forest Service. Administrative Forest Boundaries. <em>Download National Datasets</em> https://data.fs.usda.gov/geodata/edw/datasets.php?dsetCategory=boundaries (2015).</p> <p>16. U.S. Forest Service. National Wilderness Areas. <em>Download National Datasets</em> https://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=Wilderness+areas (2023).</p> <p>17. U.S. Geological Survey. Wilderness Areas in the United States. <em>ScienceBase-Catalog</em> https://www.sciencebase.gov/catalog/item/4fc8f0e4e4b0bffa8ab259e7 (2000).</p> <p>18. U.S. Geological Survey Gap Analysis Project. Protected Areas Database of the United States (PAD-US) 3.0 (ver. 2.0, March 2023). <em>ScienceBase-Catalog</em> https://doi.org/10.5066/P9Q9LQ4B (2023).</p> <p>19. Protected Planet. Protected Areas (WDPA). <em>Protected Areas (WDPA)</em> https://www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA (2023).</p> <p>20. The Nature Conservancy. The Nature Conservancy lands. <em>The Nature Conservancy (TNC) Lands</em> https://geospatial.tnc.org/datasets/TNC::tnc-lands-north-america/about?layer=0 (2022).</p> <p>21. Bureau of Indian Affairs. American Indian and Alaskan Native Land Area Representations (LAR). <em>U.S. Department of the Interior Indian Affairs</em> https://biamaps.doi.gov/bogs/datadownload.html (2019).</p> <p>22. Jarvis, A., Reuter, H. I., Nelson, A. & Guevara, E. Hole-filled SRTM for the globe Version 4. <em>CGIAR Consortium for Spatial Information</em> https://research.utwente.nl/en/publications/hole-filled-srtm-for-the-globe-version-4-data-grid (2008).</p> <p>23. Bureau of Transportation Statistics. Aviation Facilities. <em>Transportation.gov U.S. Department of Transportation</em> https://geodata.bts.gov/maps/usdot::aviation-facilities (2023).</p> <p>24. U.S. Department of Transportation. North American Rail Network Nodes. <em>U.S. Department of Transportation</em> https://data-usdot.opendata.arcgis.com/datasets/usdot::north-american-rail-network-nodes/explore?location=34.085743%2C-108.310932%2C3.96 (2023).</p> <p>25. U.S. Department of Transportation. Navigable Waterway Network Lines. <em>Bureau of Transportation Statistics</em> https://geodata.bts.gov/datasets/usdot::navigable-waterway-network-lines/about (2023).</p> <p>26. Energy Information Association (EIA). Energy Information Association U.S. Coal Mining Locations. <em>ScienceBase-Catalog</em> https://www.sciencebase.gov/catalog/item/5748a4cbe4b07e28b664dd78 (2017).</p> <p>27. Environmental Protection Agency. Green Book GIS Download. https://www.epa.gov/green-book/green-book-gis-download (2023).</p> <p>28. Shumway, A. <em>Data Release for the 2014 National Seismic Hazard Model for the Conterminous U.S</em>. https://doi.org/10.5066/P9P77LGZ (2019).</p> <p>29. Zoraghein, H. & O’Neill, B. C. U.S. State-level Projections of the Spatial Distribution of Population Consistent with Shared Socioeconomic Pathways. <em>Sustainability</em> <strong>12</strong>, (2020).</p> <p>30. Moore, R. B. <em>et al.</em> <em>User’s guide for the national hydrography dataset plus (NHDPlus) high resolution: U.S. Geological Survey Open-File Report 2019–1096</em>. https://pubs.er.usgs.gov/publication/ofr20191096 (2019).</p> <p>31. Perez, R. <em>et al.</em> A new operational model for satellite-derived irradiances: description and validation. <em>Solar Energy</em> <strong>73</strong>, 307–317 (2002).</p> <p>32. Draxl, C., Clifton, A., Hodge, B.-M. & McCaa, J. The Wind Integration National Dataset (WIND) Toolkit. <em>Applied Energy</em> <strong>151</strong>, 355–366 (2015).</p> <p> </p> <p> </p>
Input data for the OnStove Nepal model "AAchieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis"
<p>This repository includes input data to run the OnStove Nepal model presented in the paper "<strong>Achieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis</strong>" DOI: <a href="https://doi.org/10.1016/S2542-5196(24)00209-2">https://doi.org/10.1016/S2542-5196(24)00209-2</a>.</p> <p>The code and automated workflow to run the model can be found in the Github repository <a href="https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal">https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal</a>. All result files and figures can be downloaded from the permanent repository <a href="https://doi.org/10.5281/zenodo.10643983">https://doi.org/10.5281/zenodo.10643983</a>.</p> <p>The "<strong>GIS_input_data/</strong>" directory includes all the geospatial datasets needed to run the model. Each dataset folder contains a Source.md file describing the dataset, source, attribution, and license. To run the model extract the data inside your "<strong>1. Data</strong>"<strong> </strong>folder in your project. </p> <p>The "<strong>Scenario_inputs/</strong>" directory includes the CSV files with the input socio- and techno-economic data for the different scenarios. Sources for the socio- and techno-economic data can be found in the <strong>supplementary material</strong> of the related publication in the link <a href="https://doi.org/10.1016/S2542-5196(24)00209-2">https://doi.org/10.1016/S2542-5196(24)00209-2</a>. To run the model extract the scenario data inside your "<strong>2. Scenario inputs</strong>"<strong> </strong>folder in your project. </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>
Spatiotemporal dataset of dengue influencing factors in Brazil based on geospatial big data cloud computing
<p>We produced a spatiotemporal dataset of dengue influencing factors in Brazil based on geospatial big data cloud computing from 2001-2024.</p> <p>GDP and building surface area are yearly data.</p> <p>PDSI is monthly data.</p>
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