ArcGIS Desktop 10.8.2. This is just a software. If you need a license, please send a request to Software Central (softwarecentral@ucla.edu).

This is a link to the QGIS website where you can download open-source GIS software for viewing, analyzing and manipulating geodata like our downloadable shapefiles.

King County GIS data is at: https://gis-kingcounty.opendata.arcgis.com/ (new KCGIS Open Data site) OR http://www5.kingcounty.gov/gisdataportal/ (legacy KCGIS data FTP download portal)

The Minnesota DNR Toolbox and Hydro Tools provide a number of convenience geoprocessing tools used regularly by MNDNR staff. Many of these may be useful to the wider public. However, some tools may rely on data that is not available outside of the DNR. All tools require at least ArcGIS 10+.

If you create a GDRS using GDRS Manager and include this toolbox resource and MNDNR Quick Layers, the DNR toolboxes will automatically be added to the ArcToolbox window whenever Quick Layers GDRS Location is set to the GDRS location that has the toolboxes.

Toolsets included in MNDNR Tools V10:
- Analysis Tools
- Conversion Tools
- Division Tools
- General Tools
- Hydrology Tools
- LiDAR and DEM Tools
- Raster Tools
- Sampling Tools

These toolboxes are provided free of charge and are not warrantied for any specific use. We do not provide support or assistance in downloading or using these tools. We do, however, strive to produce high-quality tools and appreciate comments you have about them.

Directions to download GIS and Real Estate Data from the JCC GIS Portal

Crowther_Nature_Files.zip This description pertains to the original download. Details on revised (newer) versions of the datasets are listed below. When more than one version of a file exists in Figshare, the original DOI will take users to the latest version, though each version technically has its own DOI. -- Two global maps (raster files) of tree density. These maps highlight how the number of trees varies across the world. One map was generated using biome-level models of tree density, and applied at the biome scale. The other map was generated using ecoregion-level models of tree density, and applied at the ecoregion scale. For this reason, transitions between biomes or between ecoregions may be unrealistically harsh, but large-scale estimates are robust (see Crowther et al 2015 and Glick et al 2016). At the outset, this study was intended to generate reliable estimates at broad spatial scales, which inherently comes at the cost of fine-scale precision. For this reason, country-scale (or larger) estimates are generally more robust than individual pixel-level estimates. Additionally, due to data limitations, estimates for Mangroves and Tropical coniferous forest (as identified by WWF and TNC) were generated using models constructed from Topical moist broadleaf forest data and Temperate coniferous forest data, respectively. Because we used ecological analogy, the estimates for these two biomes should be considered less reliable than those of other biomes . These two maps initially appeared in Crowther et al (2015), with the biome map being featured more prominently. Explicit publication of the data is associated with Glick et al (2016). As they are produced, updated versions of these datasets, as well as alternative formats, will be made available under Additional Versions (see below).

Methods: We collected over 420,000 ground-sources estimates of tree density from around the world. We then constructed linear regression models using vegetative, climatic, topographic, and anthropogenic variables to produce forest tree density estimates for all locations globally. All modeling was done in R. Mapping was done using R and ArcGIS 10.1.

Viewing Instructions: Load the files into an appropriate geographic information system (GIS). For the original download (ArcGIS geodatabase files), load the files into ArcGIS to view or export the data to other formats. Because these datasets are large and have a unique coordinate system that is not read by many GIS, we suggest loading them into an ArcGIS dataframe whose coordinate system matches that of the data (see File Format). For GeoTiff files (see Additional Versions), load them into any compatible GIS or image management program.

Comments: The original download provides a zipped folder that contains (1) an ArcGIS File Geodatabase (.gdb) containing one raster file for each of the two global models of tree density – one based on biomes and one based on ecoregions; (2) a layer file (.lyr) for each of the global models with the symbology used for each respective model in Crowther et al (2015); and an ArcGIS Map Document (.mxd) that contains the layers and symbology for each map in the paper. The data is delivered in the Goode homolosine interrupted projected coordinate system that was used to compute biome, ecoregion, and global estimates of the number and density of trees presented in Crowther et al (2015). To obtain maps like those presented in the official publication, raster files will need to be reprojected to the Eckert III projected coordinate system. Details on subsequent revisions and alternative file formats are list below under Additional Versions.----------

Additional Versions: Crowther_Nature_Files_Revision_01.zip contains tree density predictions for small islands that are not included in the data available in the original dataset. These predictions were not taken into consideration in production of maps and figures presented in Crowther et al (2015), with the exception of the values presented in Supplemental Table 2. The file structure follows that of the original data and includes both biome- and ecoregion-level models.

Crowther_Nature_Files_Revision_01_WGS84_GeoTiff.zip contains Revision_01 of the biome-level model, but stored in WGS84 and GeoTiff format. This file was produced by reprojecting the original Goode homolosine files to WGS84 using nearest neighbor resampling in ArcMap. All areal computations presented in the manuscript were computed using the Goode homolosine projection. This means that comparable computations made with projected versions of this WGS84 data are likely to differ (substantially at greater latitudes) as a product of the resampling. Included in this .zip file are the primary .tif and its visualization support files.

References:

Crowther, T. W., Glick, H. B., Covey, K. R., Bettigole, C., Maynard, D. S., Thomas, S. M., Smith, J. R., Hintler, G., Duguid, M. C., Amatulli, G., Tuanmu, M. N., Jetz, W., Salas, C., Stam, C., Piotto, D., Tavani, R., Green, S., Bruce, G., Williams, S. J., Wiser, S. K., Huber, M. O., Hengeveld, G. M., Nabuurs, G. J., Tikhonova, E., Borchardt, P., Li, C. F., Powrie, L. W., Fischer, M., Hemp, A., Homeier, J., Cho, P., Vibrans, A. C., Umunay, P. M., Piao, S. L., Rowe, C. W., Ashton, M. S., Crane, P. R., and Bradford, M. A. 2015. Mapping tree density at a global scale. Nature, 525(7568): 201-205. DOI: http://doi.org/10.1038/nature14967Glick, H. B., Bettigole, C. B., Maynard, D. S., Covey, K. R., Smith, J. R., and Crowther, T. W. 2016. Spatially explicit models of global tree density. Scientific Data, 3(160069), doi:10.1038/sdata.2016.69.

DNRGPS is an update to the popular DNRGarmin application. DNRGPS and its predecessor were built to transfer data between Garmin handheld GPS receivers and GIS software.

DNRGPS was released as Open Source software with the intention that the GPS user community will become stewards of the application, initiating future modifications and enhancements.

DNRGPS does not require installation. Simply run the application .exe

See the DNRGPS application documentation for more details.

Compatible with: Windows (XP, 7, 8, 10, and 11), ArcGIS shapefiles and file geodatabases, Google Earth, most hand-held Garmin GPSs, and other NMEA output GPSs

Limited Compatibility: Interactions with ArcMap layer files and ArcMap graphics are no longer supported. Instead use shapefile or geodatabase.

Prerequisite: .NET 4 Framework

DNR Data and Software License Agreement

Subscribe to the DNRGPS announcement list to be notified of upgrades or updates.

This web map references the live tiled map service from the OpenStreetMap project. OpenStreetMap (OSM) is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information such as free satellite imagery, and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap server: http://www.OpenStreetMap.org. See that website for additional information about OpenStreetMap. It is made available as a basemap for GIS work in Esri products under a Creative Commons Attribution-ShareAlike license.Tip: This service is one of the basemaps used in the ArcGIS.com map viewer and ArcGIS Explorer Online. Simply click one of those links to launch the interactive application of your choice, and then choose Open Street Map from the Basemap control to start using this service. You'll also find this service in the Basemap gallery in ArcGIS Explorer Desktop and ArcGIS Desktop 10.

The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making.

   BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions.


   Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself.


   For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise.


   Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. 


   This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery.


   See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt


   See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

This data release contains the analytical results and evaluated source data files of geospatial analyses for identifying areas in Alaska that may be prospective for different types of lode gold deposits, including orogenic, reduced-intrusion-related, epithermal, and gold-bearing porphyry. The spatial analysis is based on queries of statewide source datasets of aeromagnetic surveys, Alaska Geochemical Database (AGDB3), Alaska Resource Data File (ARDF), and Alaska Geologic Map (SIM3340) within areas defined by 12-digit HUCs (subwatersheds) from the National Watershed Boundary dataset. The packages of files available for download are: 1. LodeGold_Results_gdb.zip - The analytical results in geodatabase polygon feature classes which contain the scores for each source dataset layer query, the accumulative score, and a designation for high, medium, or low potential and high, medium, or low certainty for a deposit type within the HUC. The data is described by FGDC metadata. An mxd file, and cartographic feature classes are provided for display of the results in ArcMap. An included README file describes the complete contents of the zip file. 2. LodeGold_Results_shape.zip - Copies of the results from the geodatabase are also provided in shapefile and CSV formats. The included README file describes the complete contents of the zip file. 3. LodeGold_SourceData_gdb.zip - The source datasets in geodatabase and geotiff format. Data layers include aeromagnetic surveys, AGDB3, ARDF, lithology from SIM3340, and HUC subwatersheds. The data is described by FGDC metadata. An mxd file and cartographic feature classes are provided for display of the source data in ArcMap. Also included are the python scripts used to perform the analyses. Users may modify the scripts to design their own analyses. The included README files describe the complete contents of the zip file and explain the usage of the scripts. 4. LodeGold_SourceData_shape.zip - Copies of the geodatabase source dataset derivatives from ARDF and lithology from SIM3340 created for this analysis are also provided in shapefile and CSV formats. The included README file describes the complete contents of the zip file.

TRCA GIS Open data on ArcGIS online. This link will take you to an external site URL: https://trca-camaps.opendata.arcgis.com/

Developed by SOLARGIS and provided by the Global Solar Atlas (GSA), this data resource contains optimum tilt to maximize yearly yield in (°) covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m), PVOUT and TEMP 30 arcsec (nominally 1 km) and OPTA 2 arcmin (nominally 4 km). The data is hyperlinked under 'resources' with the following characteristics: OPTA LTAy_AvgDailyTotals (GeoTIFF) Data format: GEOTIFF File size : 2.08 MB There are two temporal representation of solar resource and PVOUT data available: • Longterm yearly/monthly average of daily totals (LTAym_AvgDailyTotals) • Longterm average of yearly/monthly totals (LTAym_YearlyMonthlyTotals) Both type of data are equivalent, you can select the summarization of your preference. The relation between datasets is described by simple equations: • LTAy_YearlyTotals = LTAy_DailyTotals * 365.25 • LTAy_MonthlyTotals = LTAy_DailyTotals * Number_of_Days_In_The_Month For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest) For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world. For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).

Countywide datasets are available as zipped Esri geodatabases. Sets of the 5-foot-interval contours at township-level extents are available as zipped shapefiles in addition to geodatabases. (None of the data are available in GeoJSON or KML format.) Note that the zipped files are exceptionally large.All files are compressed in the open-source 7-Zip format (external link to 7-zip.org). Other utilities which can extract zipped files will work in most cases, but some of these data files might extract with 7-Zip only.

This map layer depicts the Sierra Nevada Conservancy's, Watershed Improvement Program Administrative Boundaries, which are known as Watershed Assessment Areas (AA) including the Tahoe Basin, which is not located within the SNC's boundary.

Developed by SOLARGIS and provided by the Global Solar Atlas (GSA), this data resource contains air temperature at 2m above ground level in °C covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m), PVOUT and TEMP 30 arcsec (nominally 1 km) and OPTA 2 arcmin (nominally 4 km). The data is hyperlinked under 'resources' with the following characteristics: TEMP GISdata (GeoTIFF) Data format: GEOTIFF File size : 121.03 MB There are two temporal representation of solar resource and PVOUT data available: • Longterm yearly/monthly average of daily totals (LTAym_AvgDailyTotals) • Longterm average of yearly/monthly totals (LTAym_YearlyMonthlyTotals) Both type of data are equivalent, you can select the summarization of your preference. The relation between datasets is described by simple equations: • LTAy_YearlyTotals = LTAy_DailyTotals * 365.25 • LTAy_MonthlyTotals = LTAy_DailyTotals * Number_of_Days_In_The_Month For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest) For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world. For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).

This data set contains small-scale base GIS data layers compiled by the National Park Service Servicewide Inventory and Monitoring Program and Water Resources Division for use in a Baseline Water Quality Data Inventory and Analysis Report that was prepared for the park. The report presents the results of surface water quality data retrievals for the park from six of the United States Environmental Protection Agency's (EPA) national databases: (1) Storage and Retrieval (STORET) water quality database management system; (2) River Reach File (RF3) Hydrography; (3) Industrial Facilities Discharges; (4) Drinking Water Supplies; (5) Water Gages; and (6) Water Impoundments. The small-scale GIS data layers were used to prepare the maps included in the report that depict the locations of water quality monitoring stations, industrial discharges, drinking intakes, water gages, and water impoundments. The data layers included in the maps (and this dataset) vary depending on availability, but generally include roads, hydrography, political boundaries, USGS 7.5' minute quadrangle outlines, hydrologic units, trails, and others as appropriate. The scales of each layer vary depending on data source but are generally 1:100,000.

Populations of many cold-water species are likely to decline this century with global warming, but declines will vary spatially and some populations will persist even under extreme climate change scenarios. Especially cold habitats could provide important refugia from both future environmental change and invasions by non-native species that prefer warmer waters. The Climate Shield website hosts geospatial data and related information that describes specific locations of cold-water refuge streams for native Cutthroat Trout (Oncorhynchus clarkii) and Bull Trout (Salvelinus confluentus) across the American West. Forecasts about the locations of refugia could enable the protection of key watersheds, inform support among multiple stakeholders, and provide a foundation for planning climate-smart conservation networks that improve the odds of preserving native trout populations through the 21st century. The Northern Rockies Adaptation Partnership provided a valuable forum that accelerated this work. The Great Northern and North Pacific Landscape Conservation Cooperatives generously funded the NorWeST project, which serves as the foundation for Climate Shield. The Climate Shield Cutthroat Trout and Bull Trout models were developed from fish surveys conducted at more than 4,500 locations in over 500 streams, as described in the cited peer-reviewed studies and agency reports. Resources in this dataset:Resource Title: Digital Maps and ArcGIS Shapefiles. File Name: Web Page, url: https://www.fs.fed.us/rm/boise/AWAE/projects/ClimateShield/maps.html Information is available here to download as easy-to-use digital maps (.pdf files) and ArcGIS shapefiles for all streams within the historical ranges of native trout across the northwestern U.S. The geographic areas match the NorWeST production units because those stream temperature scenarios are integral to Climate Shield.

Developed by SOLARGIS (https://solargis.com) and provided by the Global Solar Atlas (GSA), this data resource contains global horizontal irradiation (GHI) in kWh/m² covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m), PVOUT and TEMP 30 arcsec (nominally 1 km) and OPTA 2 arcmin (nominally 4 km). The data is hyperlinked under 'resources' with the following characteristics: GHI - LTAy_AvgDailyTotals (GeoTIFF) Data format: GEOTIFF File size : 268.11 MB There are two temporal representation of solar resource and PVOUT data available: • Longterm yearly/monthly average of daily totals (LTAym_AvgDailyTotals) • Longterm average of yearly/monthly totals (LTAym_YearlyMonthlyTotals) Both type of data are equivalent, you can select the summarization of your preference. The relation between datasets is described by simple equations: • LTAy_YearlyTotals = LTAy_DailyTotals * 365.25 • LTAy_MonthlyTotals = LTAy_DailyTotals * Number_of_Days_In_The_Month For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest) For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world. For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).

Publicly accessible data services, apps, maps, downloads and KMLs for all of the Alaska Department of Natural Resources datasets. This is the community's public platform for exploring and downloading open data, discovering and building apps, and engaging to solve important local issues. Analyze and combine Open Datasets using maps, as well as develop new web and mobile applications. Let's make our great community even better, together!DO NOT DELETE OR MODIFY THIS ITEM. This item is managed by the Open Data application. To make changes to this site, please visit https://opendata.arcgis.com/admin/

Developed by SOLARGIS and provided by the Global Solar Atlas (GSA), this data resource contains solar resource data for: direct normal irradiation (DNI), global horizontal irradiation (GHI), diffuse horizontal irradiation data (DIF), and global irradiation for optimally tilted surfaces (GTI), all in kWh/m² covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m). Due to the large amount of data, the coverage has been divided into eight segments. Four segments for the North hemisphere: WWN (West-west-north), WN (West-north), EN (East-north), EEN (East-east-north). Analogically four segments for the South hemisphere: WWS, WS, ES, EES. The data is hyperlinked under 'resources' with the following characteristics: DNI LTAy_AvgDailyTotals (GeoTIFF) Data format: raster (gridded), GEOTIFF File size : 343.99 MB For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest) For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world. For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).