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.

Open source GIS software available for download

The Digital Geomorphic-GIS Map of Gulf Islands National Seashore (5-meter accuracy and 1-foot resolution 2006-2007 mapping), Mississippi and Florida is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (guis_geomorphology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (guis_geomorphology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (guis_geomorphology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (guis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (guis_geomorphology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (guis_geomorphology_metadata_faq.pdf). Please read the guis_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (guis_geomorphology_metadata.txt or guis_geomorphology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:26,000 and United States National Map Accuracy Standards features are within (horizontally) 13.2 meters or 43.3 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

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.

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The GIS market share in EMEA is expected to increase to USD 2.01 billion from 2021 to 2026, and the market’s growth momentum will accelerate at a CAGR of 8.23%.

This EMEA GIS market research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches. Furthermore, this report extensively covers GIS market in EMEA segmentation by:

Component - Software, data, and services
End-user - Government, utilities, military, telecommunication, and others

What will the GIS Market Size in EMEA be During the Forecast Period?

Download the Free Report Sample to Unlock the GIS Market Size in EMEA for the Forecast Period and Other Important Statistics

The EMEA GIS market report also offers information on several market vendors, including arxiT SA, Autodesk Inc., Bentley Systems Inc., Cimtex International, CNIM SA, Computer Aided Development Corp. Ltd., Environmental Systems Research Institute Inc., Fugro NV, General Electric Co., HERE Global BV, Hexagon AB, Hi-Target, Mapbox Inc., Maxar Technologies Inc., Pitney Bowes Inc., PSI Services LLC, Rolta India Ltd., SNC Lavalin Group Inc., SuperMap Software Co. Ltd., Takor Group Ltd., and Trimble Inc. among others.

GIS Market in EMEA: Key Drivers, Trends, and Challenges

The integration of BIM and GIS is notably driving the GIS market growth in EMEA, although factors such as data viability and risk of intrusion may impede market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the GIS industry in EMEA. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.

Key GIS Market Driver in EMEA

One of the key factors driving the geographic information system (GIS) market growth in EMEA is the integration of BIM and GIS. A GIS adds value to BIM by visualizing and analyzing the data with regard to the buildings and surrounding features, such as environmental and demographic information. BIM data and workflows include information regarding sensors and the placement of devices in IoT-connected networks. For instance, Dubai's Civil Defense Department has integrated GIS data with its automatic fire surveillance system. This information is provided in a matter of seconds on the building monitoring systems of the Civil Defense Department. Furthermore, location-based services offered by GIS providers help generate huge volumes of data from stationary and moving devices and enable users to perform real-time spatial analytics and derive useful geographic insights from it. Owing to the advantages associated with the integration of BIM with GIS solutions, the demand for GIS solutions is expected to increase during the forecast period.

Key GIS Market Challenge in EMEA

One of the key challenges to the is the GIS market growth in EMEA is the data viability and risk of intrusion. Hackers can hack into these systems with malicious intentions and manipulate the data, which could have destructive or negative repercussions. Such hacking of data could cause nationwide chaos. For instance, if a hacker manipulated the traffic management database, massive traffic jams and accidents could result. If a hacker obtained access to the database of a national disaster management organization and manipulated the data to create a false disaster situation, it could lead to a panic situation. Therefore, the security infrastructure accompanying the implementation of GIS software solutions must be robust. Such security threats may impede market growth in the coming years.

Key GIS Market Trend in EMEA

Integration of augmented reality (AR) and GIS is one of the key geographic information system market trends in EMEA that is expected to impact the industry positively in the forecast period. AR apps could provide GIS content to professional end-users and aid them in making decisions on-site, using advanced and reliable information available on their mobile devices and smartphones. For instance, when the user simply points the camera of the phone at the ground, the application will be able to show the user the location and orientation of water pipes and electric cables that are concealed underground. Organizations such as the Open Geospatial Consortium (OGC) and the World Wide Web Consortium (W3C) are seeking investments and are open to sponsors for an upcoming AR pilot project, which seeks to advance the standards of AR technology at both respective organizations. Such factors will further support the market growth in the coming years.

This GIS market in EMEA analysis report also provides detailed information on other upcoming trends and challenges that will have a far-reaching effect on the market growth. The actionable insights on the trends and challenges will help companies evaluate and develop growth st

The Minnesota DNR Toolbox provides 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.

Toolsets included in MNDNR Tools:
- Analysis Tools
- Conversion Tools
- General Tools
- LiDAR and DEM Tools
- Sampling Tools

The application download includes a comprehensive help document, which you can also access separately here: ArcGISPro_MNDNR_Toolbox_Pro_User_Guide.pdf

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.

[Metadata] Description: C&C of Honolulu Land Use Zoning Designations as of September 2023.

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.

Contents: This is an ArcGIS Pro zip file that you can download and use for creating map books based on United States National Grid (USNG). It contains a geodatabase, layouts, and tasks designed to teach you how to create a basic map book.Version 1.0.0 Uploaded on May 24th and created with ArcGIS Pro 2.1.3 - Please see the README below before getting started!Updated to 1.1.0 on August 20thUpdated to 1.2.0 on September 7thUpdated to 2.0.0 on October 12thUpdate to 2.1.0 on December 29thBack to 1.2.0 due to breaking changes in the templateBack to 1.0.0 due to breaking changes in the template as of June 11th 2019Updated to 2.1.1 on October 8th 2019Audience: GIS Professionals and new users of ArcGIS Pro who support Public Safety agencies with map books. If you are looking for apps that can be used by any public safety professional, see the USNG Lookup Viewer.Purpose: To teach you how to make a map book with critical infrastructure and a basemap, based on USNG. You NEED to follow the steps in the task and not try to take shortcuts the first time you use this task in order to receive the full benefits. Background: This ArcGIS Pro template is meant to be a starting point for your map book projects and is based on best practices by the USNG National Implementation Center (TUNIC) at Delta State University and is hosted by the NAPSG Foundation. This does not replace previous templates created in ArcMap, but is a new experimental approach to making map books. We will continue to refine this template and work with other organizations to make improvements over time. So please send us your feedback admin@publicsafetygis.org and comments below. Instructions: Download the zip file by clicking on the thumbnail or the Download button.Unzip the file to an appropriate location on your computer (C:\Users\YourUsername\Documents\ArcGIS\Projects is a common location for ArcGIS Pro Projects).Open the USNG Map book Project File (APRX).If the Task is not already open by default, navigate to Catalog > Tasks > and open 'Create a US National Grid Map Book' Follow the instructions! This task will have some automated processes and models that run in the background but you should pay close attention to the instructions so you also learn all of the steps. This will allow you to innovate and customize the template for your own use.FAQsWhat is US National Grid? The US National Grid (USNG) is a point and area reference system that provides for actionable location information in a uniform format. Its use helps achieve consistent situational awareness across all levels of government, disciplines, and threats & hazards – regardless of your role in an incident.One of the key resources NAPSG makes available to support emergency responders is a basic USNG situational awareness application. See the NAPSG Foundation and USNG Center websites for more information.What is an ArcGIS Pro Task? A task is a set of preconfigured steps that guide you and others through a workflow or business process. A task can be used to implement a best-practice workflow, improve the efficiency of a workflow, or create a series of interactive tutorial steps. See "What is a Task?" for more information.Do I need to be proficient in ArcGIS Pro to use this template? We feel that this is a good starting point if you have already taken the ArcGIS Pro QuickStart Tutorials. While the task will automate many steps, you will want to get comfortable with the map layouts and other new features in ArcGIS Pro.Is this template free? This resources is provided at no-cost, but also with no guarantees of quality assurance or support at this time. Can't I just use ArcMap? Ok - here you go. USNG 1:24K Map Template for ArcMapKnown Limitations and BugsZoom To: It appears there may be a bug or limitation with automatically zooming the map to the proper extent, so get comfortable with navigation or zoom to feature via the attribute table.FGDC Compliance: We are seeking feedback from experts in the field to make sure that this meets minimum requirements. At this point in time we do not claim to have any official endorsement of standardization. File Size: Highly detailed basemaps can really add up and contribute to your overall file size, especially over a large area / many pages. Consider making a simple "Basemap" of street centerlines and building footprints.We will do the best we can to address limitations and are very open to feedback!

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GIS In Utility Industry Market Size 2025-2029

The gis in utility industry market size is forecast to increase by USD 3.55 billion, at a CAGR of 19.8% between 2024 and 2029.

The utility industry's growing adoption of Geographic Information Systems (GIS) is driven by the increasing need for efficient and effective infrastructure management. GIS solutions enable utility companies to visualize, analyze, and manage their assets and networks more effectively, leading to improved operational efficiency and customer service. A notable trend in this market is the expanding application of GIS for water management, as utilities seek to optimize water distribution and reduce non-revenue water losses. However, the utility GIS market faces challenges from open-source GIS software, which can offer cost-effective alternatives to proprietary solutions. These open-source options may limit the functionality and support available to users, necessitating careful consideration when choosing a GIS solution. To capitalize on market opportunities and navigate these challenges, utility companies must assess their specific needs and evaluate the trade-offs between cost, functionality, and support when selecting a GIS provider. Effective strategic planning and operational execution will be crucial for success in this dynamic market.

What will be the Size of the GIS In Utility Industry Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe Global Utilities Industry Market for Geographic Information Systems (GIS) continues to evolve, driven by the increasing demand for advanced data management and analysis solutions. GIS services play a crucial role in utility infrastructure management, enabling asset management, data integration, project management, demand forecasting, data modeling, data analytics, grid modernization, data security, field data capture, outage management, and spatial analysis. These applications are not static but rather continuously unfolding, with new patterns emerging in areas such as energy efficiency, smart grid technologies, renewable energy integration, network optimization, and transmission lines. Spatial statistics, data privacy, geospatial databases, and remote sensing are integral components of this evolving landscape, ensuring the effective management of utility infrastructure. Moreover, the adoption of mobile GIS, infrastructure planning, customer service, asset lifecycle management, metering systems, regulatory compliance, GIS data management, route planning, environmental impact assessment, mapping software, GIS consulting, GIS training, smart metering, workforce management, location intelligence, aerial imagery, construction management, data visualization, operations and maintenance, GIS implementation, and IoT sensors is transforming the industry. The integration of these technologies and services facilitates efficient utility infrastructure management, enhancing network performance, improving customer service, and ensuring regulatory compliance. The ongoing evolution of the utilities industry market for GIS reflects the dynamic nature of the sector, with continuous innovation and adaptation to meet the changing needs of utility providers and consumers.

How is this GIS In Utility Industry Industry segmented?

The gis in utility industry industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ProductSoftwareDataServicesDeploymentOn-premisesCloudGeographyNorth AmericaUSCanadaEuropeFranceGermanyRussiaMiddle East and AfricaUAEAPACChinaIndiaJapanSouth AmericaBrazilRest of World (ROW).

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.In the utility industry, Geographic Information Systems (GIS) play a pivotal role in optimizing operations and managing infrastructure. Utilities, including electricity, gas, water, and telecommunications providers, utilize GIS software for asset management, infrastructure planning, network performance monitoring, and informed decision-making. The GIS software segment in the utility industry encompasses various solutions, starting with fundamental GIS software that manages and analyzes geographical data. Additionally, utility companies leverage specialized software for field data collection, energy efficiency, smart grid technologies, distribution grid design, renewable energy integration, network optimization, transmission lines, spatial statistics, data privacy, geospatial databases, GIS services, project management, demand forecasting, data modeling, data analytics, grid modernization, data security, field data capture, outage ma

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.

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.

A vector GIS dataset of candidate areas for terrestrial ecological restoration based on landscape context. The dataset was created using NLCD 2011 (www.mrlc.gov) and morphological spatial pattern analysis (MSPA) (http://forest.jrc.ec.europa.eu/download/software/guidos/mspa/). There are 13 attributes for the polygons in the dataset, including presence and length of roads, candidate area size, size of surround contiguous natural areas, soil productivity, presence and length of road, areas suitable for wetland restoration, and others. This dataset is associated with the following publication: Wickham, J., K. Riiters, P. Vogt, J. Costanza, and A. Neale. An inventory of continental U.S. terrestrial candidate ecological restoration areas based on landscape context. RESTORATION ECOLOGY. Blackwell Publishing, Malden, MA, USA, 25(6): 894-902, (2017).

NLEAP GIS 5.0 can help users identify hot spots across the landscape and identify management practices that can increase nitrogen use efficiency. A Nitrogen Trading Tool (NTT) analysis can be conducted to determine the potential benefits of implementing best management practices and the quantity of nitrogen savings that could potentially be traded in future air or water quality markets. Resources in this dataset:Resource Title: NLEAP GIS 5.0. File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=428&modecode=30-12-30-15 download page

Developed by SOLARGIS (https://solargis.com) 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).

This map shows the free and open data status of county public geospatial (GIS) data across Minnesota. The accompanying data set can be used to make similar maps using GIS software.

Counties shown in this dataset as having free and open public geospatial data (with or without a policy) are: Aitkin, Anoka, Becker, Beltrami, Benton, Big Stone, Carlton, Carver, Cass, Chippewa, Chisago, Clay, Clearwater, Cook, Crow Wing, Dakota, Douglas, Grant, Hennepin, Hubbard, Isanti, Itasca, Kittson, Koochiching, Lac qui Parle, Lake, Lyon, Marshall, McLeod, Meeker, Mille Lacs, Morrison, Mower, Norman, Olmsted, Otter Tail, Pipestone, Polk, Pope, Ramsey, Renville, Rice, Scott, Sherburne, Stearns, Steele, Stevens, St. Louis, Traverse, Waseca, Washington, Wilkin, Winona, Wright and Yellow Medicine.

To see if a county's data is distributed via the Minnesota Geospatial Commons, check the Commons organizations page: https://gisdata.mn.gov/organization

To see if a county distributes data via its website, check the link(s) on the Minnesota County GIS Contacts webpage: https://www.mngeo.state.mn.us/county_contacts.html

This resource was created by Esri Canada Education and Research. To browse our full collection of higher-education learning resources, please visit https://hed.esri.ca/resourcefinder/.This tutorial introduces you to using Python code in a Jupyter Notebook, an open source web application that enables you to create and share documents that contain rich text, equations and multimedia, alongside executable code and visualization of analysis outputs. The tutorial begins by stepping through the basics of setting up and being productive with Python notebooks. You will be introduced to ArcGIS Notebooks, which are Python Notebooks that are well-integrated within the ArcGIS platform. Finally, you will be guided through a series of ArcGIS Notebooks that illustrate how to create compelling notebooks for data science that integrate your own Python scripts using the ArcGIS API for Python and ArcPy in combination with thousands of open source Python libraries to enhance your analysis and visualization.To download the dataset Labs, click the Open button to the top right. This will automatically download a ZIP file containing all files and data required.You can also clone the tutorial documents and datasets for this GitHub repo: https://github.com/highered-esricanada/arcgis-notebooks-tutorial.git.Software & Solutions Used: Required: This tutorial was last tested on August 27th, 2024, using ArcGIS Pro 3.3. If you're using a different version of ArcGIS Pro, you may encounter different functionality and results.Recommended: ArcGIS Online subscription account with permissions to use advanced Notebooks and GeoEnrichmentOptional: Notebook Server for ArcGIS Enterprise 11.3+Time to Complete: 2 h (excludes processing time)File Size: 196 MBDate Created: January 2022Last Updated: August 27, 2024

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).

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

This dataset was sourced from the Queensland Department of Natural Resources and Mines in 2012. Information provided by the Department describes the dataset as follows:

This data was originally provided on DVD and contains the converted shapefiles, layer files, raster images and project .mxd files used on the Queensland geology and structural framework map. The maps were done in ArcGIS 9.3.1 and the data stored in file geodatabases, topology created and validated. This provides greater data quality by performing topological validation on the feature's spatial relationships. For the purposes of the DVD, shapefiles were created from the file geodatabases and for MapInfo users MapInfo .tab and .wor files. The shapefiles on the DVD are a revision of the 1975 Queensland geology data, and are both are available for display, query and download on the department's online GIS application.

The Queensland geology map is a digital representation of the distribution or extent of geological units within Queensland. In the GIS, polygons have a range of attributes including unit name, type of unit, age, lithological description, dominant rock type, and an abbreviated symbol for use in labelling the polygons. The lines in this dataset are a digital representation of the position of the boundaries of geological units and other linear features such as faults and folds. The lines are attributed with a description of the type of line represented. Approximately 2000 rock units were grouped into the 250 map units in this data set. The digital data was generalised and simplified from the Department's detailed geological data and was captured at 1:500 000 scale for output at 1:2 000 000 scale.

In the ESRI version, a layer file is provided which presents the units in the colours and patterns used on the printed hard copy map. For Map Info users, a simplified colour palette is provided without patterns. However a georeferenced image of the hard copy map is included and can be displayed as a background in both Arc Map and Map Info.

The geological framework of Queensland is classified by structural or tectonic unit (provinces and basins) in which the rocks formed. These are referred to as basins (or in some cases troughs and depressions) where the original form and structure are still apparent. Provinces (and subprovinces) are generally older basins that have been strongly tectonised and/or metamorphosed so that the original basin extent and form are no longer preserved. Note that intrusive and some related volcanic rocks that overlap these provinces and basins have not been included in this classification. The map was compiled using boundaries modified and generalised from the 1:2 000 000 Queensland Geology map (2012). Outlines of subsurface basins are also shown and these are based on data and published interpretations from petroleum exploration and geophysical surveys (seismic, gravity and magnetics).

For the structural framework dataset, two versions are provided. In QLD_STRUCTURAL_FRAMEWORK, polygons are tagged with the name of the surface structural unit, and names of underlying units are imbedded in a text string in the HIERARCHY field. In QLD_STRUCTURAL_FRAMEWORK_MULTI_POLYS, the data is structured into a series of overlapping, multi-part polygons, one for each structural unit. Two layer files are provided with the ESRI data, one where units are symbolised by name. Because the dataset has been designed for units display in the order of superposition, this layer file assigns colours to the units that occur at the surface with concealed units being left uncoloured. Another layer file symbolises them by the orogen of which they are part. A similar set of palettes has been provided for Map Info.

Dataset History

Details on the source data can be found in the xml file associated with data layer.

Data in this release

*ESRI.shp and MapInfo .tab files of rock unit polygons and lines with associated layer attributes of Queensland geology

*ESRI.shp and MapInfo .tab files of structural unit polygons and lines with associated layer attributes of structural framework

*ArcMap .mxd and .lyr files and MapInfo .wor files containing symbology

*Georeferenced Queensland geology map, gravity and magnetic images

*Queensland geology map, structural framework and schematic diagram PDF files

*Data supplied in geographical coordinates (latitude/longitude) based on Geocentric Datum of Australia - GDA94

Accessing the data

Programs exist for the viewing and manipulation of the digital spatial data contained on this DVD. Accessing the digital datasets will require GIS software. The following GIS viewers can be downloaded from the internet. ESRI ArcExplorer can be found by a search of www.esriaustralia.com.au and MapInfo ProViewer by a search on www.pbinsight.com.au collectively ("the websites").

Metadata

Metadata is contained in .htm files placed in the root folder of each vector data folder. For ArcMap users metadata for viewing in ArcCatalog is held in an .xml file with each shapefile within the ESRI Shapefile folders.

Disclaimer

The State of Queensland is not responsible for the privacy practices or the content of the websites and makes no statements, representations, or warranties about the content or accuracy or completeness of, any information or products contained on the websites.

Despite our best efforts, the State of Queensland makes no warranties that the information or products available on the websites are free from infection by computer viruses or other contamination.

The State of Queensland disclaims all responsibility and all liability (including without limitation, liability in negligence) for all expenses, losses, damages and costs you might incur as a result of accessing the websites or using the products available on the websites in any way, and for any reason.

The State of Queensland has included the websites in this document as an information source only. The State of Queensland does not promote or endorse the websites or the programs contained on them in any way.

WARNING: The Queensland Government and the Department of Natural Resources and Mines accept no liability for and give no undertakings, guarantees or warranties concerning the accuracy, completeness or fitness for the purposes of the information provided. The consumer must take all responsible steps to protect the data from unauthorised use, reproduction, distribution or publication by other parties.

Please view the 'readme.html' and 'licence.html' file for further, more complete information

Dataset Citation

Geological Survey of Queensland (2012) Queensland geology and structural framework - GIS data July 2012. Bioregional Assessment Source Dataset. Viewed 07 December 2018, http://data.bioregionalassessments.gov.au/dataset/69da6301-04c1-4993-93c1-4673f3e22762.

[Metadata] Mental Health Professional Shortage Areas as of April 2024. Source - Hawaii State Department of Health. Description: Designation of Health Professional Shortage Areas for Mental Health. See also Health Professional Shortage Areas for Dental Health and Primary Care. A Health Professional Shortage Area (HPSA) means any of the following which has a shortage of health professionals: (a) an urban or rural area which is a rational service area for the delivery of health services, (b) a population group, or (c) a public or nonprofit private medical facility. HPSAs are divided into three major categories according to the type of health professional shortage: primary care, dental or mental health HPSAs. For more information about HPSA’s, visit the Hawaii State Department of Health HPSA website at https://health.hawaii.gov/opcrh/home/health-professional-shortage-area-hpsa/. Hawaii Statewide GIS Program staff downloaded data from https://data.hrsa.gov/data/download?hmpgtitle=hmpg-hrsa-data April 2024. Projected to UTM Zone 4 NAD 83 HARN, and clipped to coastline. For additional information, please refer to summary metadata at https://files.hawaii.gov/dbedt/op/gis/data/hpsa.pdf or contact Hawaii Statewide GIS Program, Office of Planning and Sustainable Development, State of Hawaii; PO Box 2359, Honolulu, Hi. 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.