This dataset holds all materials for the Inform E-learning GIS course

This is a full-day training, developed by UNEP CMB, to introduce participants to the basics of GIS, how to import points from Excel to a GIS, and how to make maps with QGIS, MapX and Tableau. It prioritizes the use of free and open software.

The Geospatial Fabric version 1.1 (GFv1.1 or v1_1) is a dataset of spatial modeling units covering the conterminous United States (CONUS) and most major river basins that flow in from Canada. The GFv1.1 is an update to the original Geospatial Fabric (GFv1, Viger and Bock, 2014) for the National Hydrologic Modeling (NHM). Analogous to the GFv1, the GFv1.1 described here includes the following vector feature classes: points of interest (POIs_v1_1), a stream network (nsegment_v1_1), and hydrologic response units (nhru_v1_1), with several additional ancillary tables. These data are contained within the Environmental Systems Research Institute (ESRI) geodatabase format (GFv1.1.gdb).

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Geospatial Analytics Market Size 2024-2028

The geospatial analytics market size is forecast to increase by USD 127.2 billion at a CAGR of 18.68% between 2023 and 2028.

The market is experiencing significant growth due to the increasing adoption of geospatial data analytics in sectors such as healthcare and insurance. This trend is driven by the abundance of data being generated through emerging methods like remote sensing, IoT, and drones. However, data privacy and security concerns remain a challenge, as geospatial data can reveal sensitive information.
Organizations must implement robust security measures to protect this valuable information. In the US and North America, the market is expected to grow steadily, driven by the region's advanced technological infrastructure and increasing focus on data-driven decision-making. Companies in this space should stay abreast of emerging trends and address concerns related to data security to remain competitive.

What will be the Size of the Geospatial Analytics Market During the Forecast Period?

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The market is experiencing significant growth due to the increasing demand for location intelligence in various industries, particularly Medium Scale Enterprises (MSEs). This market is driven by the integration of Artificial Intelligence (AI) and machine learning (ML), enabling advanced data analysis and prediction capabilities. The Internet of Things (IoT) is also fueling market growth, as real-time location data is collected and analyzed for various applications, including disaster risk reduction. Hexagon and Luciad are among the key players in this market, offering advanced geospatial analytics solutions. Big data analysis, digital globe imagery, and Pitney Bowes' location intelligence offerings are also contributing to market expansion.
The integration of AI, ML, and 5G technology is expected to further accelerate growth, with applications ranging from supply chain optimization to web-based GIS platforms built using JavaScript and HTML5.

How is this Geospatial Analytics Industry segmented and which is the largest segment?

The geospatial analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2017-2022 for the following segments.

Technology

  GPS
  GIS
  Remote sensing
  Others


End-user

  BFSI
  Government and utilities
  Telecom
  Manufacturing and automotive
  Others


Component

  Software
  Service


Type

  Surface & Field Analytics
  Network & Location Analytics
  Geovisualization
  Others


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK


  APAC

    China


  Middle East and Africa



  South America

By Technology Insights

The GPS segment is estimated to witness significant growth during the forecast period. The market is driven by various sectors including Defense & Internal Security, Retail & Logistics, Energy & Utilities, Agriculture, Healthcare & Life Sciences, Infrastructure, and GIS. Among these, GPS, a satellite-based radio navigation system, was the largest segment in 2023. Operated by the US Space Force, GPS enables geolocation and time information transmission to receivers, facilitating georeferencing, positioning, navigation, and time and frequency control. This technology is widely used in industries such as logistics, transportation, and surveying, making it a significant contributor to the market's growth. The Energy & Utilities sector also leverages geospatial analytics for infrastructure planning, asset management, and maintenance, further fueling market expansion.

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The GPS segment was valued at USD 29.90 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 36% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The North American region dominates the market due to the region's early adoption of advanced technologies and the maturity of industries, particularly in healthcare and the industrial sector. The healthcare industry's need for high-level analytics, driven by the COVID-19 pandemic, is a significant factor fueling market growth. In the industrial sector, the abundance of successful technology implementations leads to a faster rate of adoption. Geospatial analytics plays a crucial role in various applications. These applications provide valuable insights for businesses and governments, enabling informed decision-making and improving operational efficien

Discover a simple method and approach that guides everything you do with spatial analysis. Learn best practices, explore case studies, and get workflows to help you more successfully analyze your data.

This document shows just the questions we asked the applicants who applied to participate in this Georeferencing for Research Use workshop. We used a Google Form to deliver these questions and collect responses. It is both an application and serves as our pre-workshop survey.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. To produce the digital map, a combination of 1:12,000-scale ortho imagery acquired in 2003, 2004, and 2005 and all of the GPS-referenced ground data were used to interpret the complex patterns of vegetation and land-use. All imagery was acquired from the U.S. Department of Agriculture - Farm Service Agency’s Aerial Photography Field Office and the National Agriculture Imagery Program. In the end, 32 map units (13 vegetated and 19 land-use) were developed and directly cross-walked or matched to corresponding plant associations and land-use classes. All of the interpreted and remotely sensed data were converted to Geographic Information System (GIS) databases using ArcGIS© software. Draft maps were printed, field tested, reviewed, and revised. One hundred-twenty four accuracy assessment (AA) data points were collected in 2006 and used to determine the map’s accuracy. After final revisions, the accuracy assessment revealed an overall thematic accuracy of 89%. Project Size = 6,784 acres San Antonio Missions National Historical Park = 844 acres Map Classes = 32 13 Vegetated 19 Non-vegetated Minimum Mapping Unit = ½ hectare is the program standard but this was modified at SAAN to ¼ acre. Total Size = 1,122 Polygons Average Polygon Size = 6 acres Overall Thematic Accuracy = 89%

These data were produced by GA for the Computer Education Group of the ACT for the purposes of basic GIS training in ACT schools. Geological data consists mainly of polygons of rock units grouped according to rock type and geological age. Data have been derived from 1:250 000 and 1:100 000 scale geological maps produced by GA. The complete training dataset includes basic geology, Landsat TM images, and a portion of the 9 Second DEM of Australia.

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

The geospatial analytics market size is predicted to rise from $93.49 billion in 2024 to $362.45 billion by 2035, growing at a CAGR of 13.1% from 2024 to 2035.

Needing to answer the question of “where” sat at the forefront of everyone’s mind, and using a geographic information system (GIS) for real-time surveillance transformed possibly overwhelming data into location intelligence that provided agencies and civic leaders with valuable insights.This book highlights best practices, key GIS capabilities, and lessons learned during the COVID-19 response that can help communities prepare for the next crisis.GIS has empowered:Organizations to use human mobility data to estimate the adherence to social distancing guidelinesCommunities to monitor their health care systems’ capacity through spatially enabled surge toolsGovernments to use location-allocation methods to site new resources (i.e., testing sites and augmented care sites) in ways that account for at-risk and vulnerable populationsCommunities to use maps and spatial analysis to review case trends at local levels to support reopening of economiesOrganizations to think spatially as they consider “back-to-the-workplace” plans that account for physical distancing and employee safety needsLearning from COVID-19 also includes a “next steps” section that provides ideas, strategies, tools, and actions to help jump-start your own use of GIS, either as a citizen scientist or a health professional. A collection of online resources, including additional stories, videos, new ideas and concepts, and downloadable tools and content, complements this book.Now is the time to use science and data to make informed decisions for our future, and this book shows us how we can do it.Dr. Este GeraghtyDr. Este Geraghty is the chief medical officer and health solutions director at Esri where she leads business development for the Health and Human Services sector.Matt ArtzMatt Artz is a content strategist for Esri Press. He brings a wide breadth of experience in environmental science, technology, and marketing.

The Geopspatial Fabric provides a consistent, documented, and topologically connected set of spatial features that create an abstracted stream/basin network of features useful for hydrologic modeling.The GIS vector features contained in this Geospatial Fabric (GF) data set cover the lower 48 U.S. states, Hawaii, and Puerto Rico. Four GIS feature classes are provided for each Region: 1) the Region outline ("one"), 2) Points of Interest ("POIs"), 3) a routing network ("nsegment"), and 4) Hydrologic Response Units ("nhru"). A graphic showing the boundaries for all Regions is provided at http://dx.doi.org/doi:10.5066/F7542KMD. These Regions are identical to those used to organize the NHDPlus v.1 dataset (US EPA and US Geological Survey, 2005). Although the GF Feature data set has been derived from NHDPlus v.1, it is an entirely new data set that has been designed to generically support regional and national scale applications of hydrologic models. Definition of each type of feature c ...

Golf Courses, as of 2014

Military Installations, Ranges, and Training Areas - PolygonsThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the Department of Defense (DOD), displays military installations, ranges, and training areas (MIRTA) in the United States and its territories. This polygon data, per DOD, "integrates site information about DoD installations, training ranges, and land assets in a format which can be immediately put to work in commercial geospatial information systems. Homeland Security/Homeland Defense, law enforcement, and readiness planners will benefit from immediate access to DoD site location data during emergencies. Land use planning and renewable energy planning will also benefit from use of this data."Joint Base AndrewsData downloaded: April 30, 2024Data source: Military Installations, Ranges, and Training AreasNGDAID: 134 (Military Installations, Ranges, and Training Areas)OGC API Features Link: (MIRTA Polygon OGC Features) copy this link to embed it in OGC Compliant viewersFor more information, please visit:Your Official Guide to U.S. Military InstallationsGeospatial Information for U.S. Military Installations, Ranges, and Training AreasSupport documentation: Release NotesFor feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Real Property Theme Community. Per the Federal Geospatial Data Committee (FGDC), Real Property is defined as "the spatial representation (location) of real property entities, typically consisting of one or more of the following: unimproved land, a building, a structure, site improvements and the underlying land. Complex real property entities (that is "facilities") are used for a broad spectrum of functions or missions. This theme focuses on spatial representation of real property assets only and does not seek to describe special purpose functions of real property such as those found in the Cultural Resources, Transportation, or Utilities themes."For other NGDA Content: Esri Federal Datasets

A list of address locations of golf courses was compiled. Addresses were geocoded using 10.0 US Streets Geocode Service and checked for accuracy by staff at UT Chattanooga ARCS. Geospatial data creation was completed 6/4/2012.

NSW Named Water Course defines the centreline of a named water course as a single feature. It is an aggregation of Hydro Line parts.

United States geospatial analytics market size reached USD 25.2 Billion in 2024. Looking forward, IMARC Group expects the market to reach USD 60.1 Billion by 2033, exhibiting a growth rate (CAGR) of 10% during 2025-2033. The growing need for facilitating data-driven decisions, along with the rising focus of government bodies on improving situational awareness and monitoring of troops and enemy movements, is primarily propelling the market growth across the country.

IMARC Group provides an analysis of the key trends in each segment of the market, along with forecasts at the country level for 2025-2033. Our report has categorized the market based on component, type, technology, enterprise size, deployment mode, and vertical.

OVERVIEWThis site is dedicated to raising the level of spatial and data literacy used in public policy. We invite you to explore curated content, training, best practices, and datasets that can provide a baseline for your research, analysis, and policy recommendations. Learn about emerging policy questions and how GIS can be used to help come up with solutions to those questions.EXPLOREGo to your area of interest and explore hundreds of maps about various topics such as social equity, economic opportunity, public safety, and more. Browse and view the maps, or collect them and share via a simple URL. Sharing a collection of maps is an easy way to use maps as a tool for understanding. Help policymakers and stakeholders use data as a driving factor for policy decisions in your area.ISSUESBrowse different categories to find data layers, maps, and tools. Use this set of content as a driving force for your GIS workflows related to policy. RESOURCESTo maximize your experience with the Policy Maps, we’ve assembled education, training, best practices, and industry perspectives that help raise your data literacy, provide you with models, and connect you with the work of your peers.

The U.S. Geological Survey, in collaboration with the National Oceanic and Atmospheric Administration's (NOAA) National Marine Sanctuary Program, conducted seabed mapping and related research in the Stellwagen Bank National Marine Sanctuary region from 1993 to 2004. The mapped area is approximately 3,700 square km (1,100 square nm) in size and was subdivided into 18 quadrangles. Several series of sea floor maps of the region based on multibeam sonar surveys have been published. In addition, 2,628 seabed sediment samples were collected and analyzed and approximately 10,600 still photographs of the seabed were acquired during the project. These data provide the basis for scientists, policymakers, and managers for understanding the complex ecosystem of the sanctuary region and for monitoring and managing its economic and natural resources.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for Olympic National Park. The vegetation map is a geotiff raster, and at 67MB may be difficult to download. An ArcGIS file geodatabase contains plot data and lookup tables that relate map class units to mapping associations. The geodatabase includes a vegetation Feature dataset with the park boundary and project boundary used in the map. The map development process was organized around the random forests machine learning algorithm. The modeling used 2,519 plots representing 150 vegetation associations and 50 map classes. Imagery from the National Agriculture Imagery Program and the Sentinel-2 and Landsat 8 satellites, airborne lidar bare earth and canopy height data, elevation data from the U.S. Geological Survey 3D Elevation Program, and climate normals from the PRISM Climate Group were used to develop a variety of predictor metrics. The predictors and the map class calls at each plot were input to a process in which each map class was modeled against every other map class in a factorial random forests scheme. We used the plot-level modeling outcomes and species composition data to adjust the crosswalk between association and map class so that floristic consistency and model accuracy were jointly optimized across all classes. The map was produced by predicting the factorial models and selecting the overall best-performing class at each 3-meter pixel. The final vegetation map, including a buffer surrounding the park, contains 43 natural vegetated classes, seven mostly unvegetated natural classes, and four classes representing burned areas or anthropogenic disturbance.