This political map of United States of America shows state and national boundaries, state names and other features.

These data are part of a larger USGS project to develop an updated geospatial database of mines, mineral deposits and mineral regions in the United States. Mine and prospect-related symbols, such as those used to represent prospect pits, mines, adits, dumps, tailings, etc., hereafter referred to as “mine” symbols or features, are currently being digitized on a state-by-state basis from the 7.5-minute (1:24, 000-scale) and the 15-minute (1:48, 000 and 1:62,500-scale) archive of the USGS Historical Topographic Maps Collection, or acquired from available databases (California and Nevada, 1:24,000-scale only). Compilation of these features is the first phase in capturing accurate locations and general information about features related to mineral resource exploration and extraction across the U.S. To date, the compilation of 400,000-plus point and polygon mine symbols from approximately 51,000 maps of 17 western states (AZ, CA, CO, ID, KS, MT, ND, NE, NM, NV, OK, OR, SD, UT, WA, WY and western TX) has been completed. The process renders not only a more complete picture of exploration and mining in the western U.S., but an approximate time line of when these activities occurred. The data may be used for land use planning, assessing abandoned mine lands and mine-related environmental impacts, assessing the value of mineral resources from Federal, State and private lands, and mapping mineralized areas and systems for input into the land management process. The data are presented as three groups of layers based on the scale of the source maps. No reconciliation between the data groups was done.

This dataset consists of cartographic data in digital line graph (DLG) form for the northeastern states (Connecticut, Maine, Massachusetts, New Hampshire, New York, Rhode Island and Vermont). Information is presented on two planimetric base categories, political boundaries and administrative boundaries, each available in two formats: the topologically structured format and a simpler format optimized for graphic display. These DGL data can be used to plot base maps and for various kinds of spatial analysis. They may also be combined with other geographically referenced data to facilitate analysis, for example the Geographic Names Information System.

This data release contains the GIS data supporting U.S. Geological Survey Open-File Report (OFR) 2005-1252, "The Geologic Map of Seattle—A Progress Report," published in 2005 by Kathy Goetz Troost, Derek B. Booth, Aaron P. Wisher, and Scott A. Shimel (https://doi.org/10.3133/ofr20051252). The OFR was prepared for the 2005 Washington Hydrogeology Symposium and describes the status of geologic mapping for Seattle, Washington, at the time. The map is the result of field mapping and compilation of subsurface geologic data during the years 1999–2004 and was funded by the City of Seattle and the U.S. Geological Survey. Data from more than 36,000 exploration points, geotechnical borings, monitoring wells, excavations, and outcrops were used in making the map. The northern part of the 2005 OFR and the supporting GIS data were subsequently published as two geologic maps: Booth, D.B., Troost, K.G., and Shimel, S.A., 2005, Geologic map of northwestern Seattle (part of the Seattle North 7.5’ ...

The Geographic Information Retrieval and Analysis System (GIRAS) was developed in the mid 70s to put into digital form a number of data layers which were of interest to the USGS. One of these data layers was the Hydrologic Units. The map is based on the Hydrologic Unit Maps published by the U.S. Geological Survey Office of Water Data Coordination, together with the list descriptions and name of region, subregion, accounting units, and cataloging unit. The hydrologic units are encoded with an eight- digit number that indicates the hydrologic region (first two digits), hydrologic subregion (second two digits), accounting unit (third two digits), and cataloging unit (fourth two digits). The data produced by GIRAS was originally collected at a scale of 1:250K. Some areas, notably major cities in the west, were recompiled at a scale of 1:100K. In order to join the data together and use the data in a geographic information system (GIS) the data were processed in the ARC/INFO GUS software package. Within the GIS, the data were edgematched and the neatline boundaries between maps were removed to create a single data set for the conterminous United States. NOTE: A version of this data theme that is more throughly checked (though based on smaller-scale maps) is available here: https://water.usgs.gov/lookup/getspatial?huc2m HUC, GIRAS, Hydrologic Units, 1:250 For the most current data and information relating to hydrologic unit codes (HUCs) please see http://water.usgs.gov/GIS/huc.html. The Watershed Boundary Dataset (WBD) is the most current data available for watershed delineation. See http://www.nrcs.usda.gov/wps/portal/nrcs/main/national/water/watersheds/dataset

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Geographic Information System Analytics Market Size 2024-2028

The geographic information system analytics market size is forecast to increase by USD 12 billion at a CAGR of 12.41% between 2023 and 2028.

The GIS Analytics Market analysis is experiencing significant growth, driven by the increasing need for efficient land management and emerging methods in data collection and generation. The defense industry's reliance on geospatial technology for situational awareness and real-time location monitoring is a major factor fueling market expansion. Additionally, the oil and gas industry's adoption of GIS for resource exploration and management is a key trend. Building Information Modeling (BIM) and smart city initiatives are also contributing to market growth, as they require multiple layered maps for effective planning and implementation. The Internet of Things (IoT) and Software as a Service (SaaS) are transforming GIS analytics by enabling real-time data processing and analysis.
Augmented reality is another emerging trend, as it enhances the user experience and provides valuable insights through visual overlays. Overall, heavy investments are required for setting up GIS stations and accessing data sources, making this a promising market for technology innovators and investors alike.

What will be the Size of the GIS Analytics Market during the forecast period?

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The geographic information system analytics market encompasses various industries, including government sectors, agriculture, and infrastructure development. Smart city projects, building information modeling, and infrastructure development are key areas driving market growth. Spatial data plays a crucial role in sectors such as transportation, mining, and oil and gas. Cloud technology is transforming GIS analytics by enabling real-time data access and analysis. Startups are disrupting traditional GIS markets with innovative location-based services and smart city planning solutions. Infrastructure development in sectors like construction and green buildings relies on modern GIS solutions for efficient planning and management. Smart utilities and telematics navigation are also leveraging GIS analytics for improved operational efficiency.
GIS technology is essential for zoning and land use management, enabling data-driven decision-making. Smart public works and urban planning projects utilize mapping and geospatial technology for effective implementation. Surveying is another sector that benefits from advanced GIS solutions. Overall, the GIS analytics market is evolving, with a focus on providing actionable insights to businesses and organizations.

How is this Geographic Information System Analytics Industry segmented?

The geographic information system 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 2018-2022 for the following segments.

End-user

  Retail and Real Estate
  Government
  Utilities
  Telecom
  Manufacturing and Automotive
  Agriculture
  Construction
  Mining
  Transportation
  Healthcare
  Defense and Intelligence
  Energy
  Education and Research
  BFSI


Components

  Software
  Services


Deployment Modes

  On-Premises
  Cloud-Based


Applications

  Urban and Regional Planning
  Disaster Management
  Environmental Monitoring Asset Management
  Surveying and Mapping
  Location-Based Services
  Geospatial Business Intelligence
  Natural Resource Management


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    South Korea


  Middle East and Africa

    UAE


  South America

    Brazil


  Rest of World

By End-user Insights

The retail and real estate segment is estimated to witness significant growth during the forecast period.

The GIS analytics market analysis is witnessing significant growth due to the increasing demand for advanced technologies in various industries. In the retail sector, for instance, retailers are utilizing GIS analytics to gain a competitive edge by analyzing customer demographics and buying patterns through real-time location monitoring and multiple layered maps. The retail industry's success relies heavily on these insights for effective marketing strategies. Moreover, the defense industries are integrating GIS analytics into their operations for infrastructure development, permitting, and public safety. Building Information Modeling (BIM) and 4D GIS software are increasingly being adopted for construction project workflows, while urban planning and designing require geospatial data for smart city planning and site selection.

The oil and gas industry is leveraging satellite imaging and IoT devices for land acquisition and mining operations. In the public sector,

An automated inventory of the names and locations of physical and cultural geographic features located throughout the United States. To promote geographic feature name standardization and to serve as the Federal Government's repository of information regarding feature name spellings and applications for features in U.S. The names listed in the inventory can be published on Federal maps, charts, and in other documents. The feature locative information has been used in emergency preparedness, marketing, site-selection and analysis, genealogical and historical research, and transportation routing applications.The full Kansas geospatial catalog is administered by the Kansas Data Access & Support Center (DASC) and can be found at the following URL: https://hub.kansasgis.org/

This data set depicts land use and land cover from the 1970s and 1980s and has been previously published by the U.S. Geological Survey (USGS) in other file formats. This version has been reformatted to other file formats and includes minor edits applied by the U.S. Environmental Protection Agency (USEPA) and USGS scientists. This data set was developed to meet the needs of the USGS National Water-Quality Assessment (NAWQA) Program.

The Geographic Names Information System (GNIS) is the Federal standard for geographic nomenclature. The U.S. Geological Survey developed the GNIS for the U.S. Board on Geographic Names, a Federal inter-agency body chartered by public law to maintain uniform feature name usage throughout the Government and to promulgate standard names to the public. The GNIS is the official repository of domestic geographic names data; the official vehicle for geographic names use by all departments of the Federal Government; and the source for applying geographic names to Federal electronic and printed products of all types. See https://www.usgs.gov/core-science-systems/ngp/board-on-geographic-names for additional information.

The USGS Earthquake Hazards Program of the U.S. Geological Survey (USGS) is part of the National Earthquake Hazards Reduction Program (NEHRP) led by the National Institute of Standards and Technology (NIST).

The USGS role in NEHRP is to provide Earth sciences information and products for earthquake loss reduction. The goals of the USGS' Earthquake Hazards Program are: * Improve earthquake hazard identification and risk assessment methods and their use; * Maintain and improve comprehensive earthquake monitoring in the United States with focus on "real-time" systems in urban areas; * Improve the understanding of earthquakes occurrence and their effects and consequences.

This dataset can be used to provide and apply relevant earthquake science information and knowledge for reducing deaths, injuries, and property damage from earthquakes through understanding of their characteristics and effects and by providing the information and knowledge needed to mitigate these losses.

The Geographic Names Information System (GNIS) is the Federal standard for geographic nomenclature in support of the U.S. Board on Geographic Names. The Administrative Features layer (ADMIN_FEATURE) in the GNIS Web Map Service contains all features in the database with Feature Class of Civil, Forest, Park, Reserve. See http://geonames.usgs.gov/domestic/feature_class.htm for feature class values and definitions. The ADMIN_FEATURE250 layer contains large features designated by the Geographic Names Office as ones that should be labeled on maps or displays with a scale of 1:250,000. See http://geonames.usgs.gov for additional information.

GNIS contains point data derived from the federal Geographic Names Information System, depicting the locations of all named places in New Hampshire. Place name locations from the federal GNIS have been corrected and updated, based upon a variety of sources, including current and historic US Geological Survey topographic maps, aerial photography, New Hampshire state agency records, and current web sites.

Students will explore U.S. census data to see the spatial differences in the United States’ population. The activity uses a web-based map and is tied to the AP Human Geography benchmarks. Learning outcomes:· Unit 2, A1: Geographical analysis of population (density, distribute and scale)· Unit 2, A3: Geographical analysis of population (composition: age, sex, income, education and ethnicity)· Unit 2, A4: Geographical analysis of population (patterns of fertility, mortality and health)Find more advanced human geography geoinquiries and explore all geoinquiries at http://www.esri.com/geoinquiries

The Digital Raster Graphic (DRG) is a raster image of a scanned USGS topographic map including the collar information, georeferenced to the UTM grid. This version of the Digital Raster Graphic (DRG) has been clipped to remove the collar (white border of the map) and has been reprojected to geographic coordinates.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..This table provides geographical mobility for persons relative to their residence at the time they were surveyed. The characteristics crossed by geographical mobility reflect the current survey year..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2019-2023 American Community Survey 5-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Geospatial data about United States Fault Lines. Export to CAD, GIS, PDF, CSV and access via API.

The GNIS contains information about physical and cultural geographic features in the United States and associated areas, both current and historical (not including roads and highways). The database holds the Federally recognized name of each feature and defines the location of the feature by state, county, USGS topographic map, and geographic coordinates.

Author: Joseph Kerski, post_secondary_educator, Esri and University of DenverGrade/Audience: high school, ap human geography, post secondary, professional developmentResource type: lessonSubject topic(s): population, maps, citiesRegion: africa, asia, australia oceania, europe, north america, south america, united states, worldStandards: All APHG population tenets. Geography for Life cultural and population geography standards. Objectives: 1. Understand how population change and demographic characteristics are evident at a variety of scales in a variety of places around the world. 2. Understand the whys of where through analysis of change over space and time. 3. Develop skills using spatial data and interactive maps. 4. Understand how population data is communicated using 2D and 3D maps, visualizations, and symbology. Summary: Teaching and learning about demographics and population change in an effective, engaging manner is enriched and enlivened through the use of web mapping tools and spatial data. These tools, enabled by the advent of cloud-based geographic information systems (GIS) technology, bring problem solving, critical thinking, and spatial analysis to every classroom instructor and student (Kerski 2003; Jo, Hong, and Verma 2016).

Two datasets provide geographic, land use and population data for US Counties within the contiguous US. Land area, water area, cropland area, farmland area, pastureland area and idle cropland area are given along with latitude and longitude of the county centroid and the county population. Variables in this dataset come from the US Dept. of Agriculture (USDA) Natural Resources Conservation Service (NRCS) and the US Census Bureau.

EOS-WEBSTER provides seven datasets which provide county-level data on agricultural management, crop production, livestock, soil properties, geography and population. These datasets were assembled during the mid-1990's to provide driving variables for an assessment of greenhouse gas production from US agriculture using the DNDC agro-ecosystem model [see, for example, Li et al. (1992), J. Geophys. Res., 97:9759-9776; Li et al. (1996) Global Biogeochem. Cycles, 10:297-306]. The data (except nitrogen fertilizer use) were all derived from publicly available, national databases. Each dataset has a separate DIF.

The US County data has been divided into seven datasets.

US County Data Datasets:

1) Agricultural Management 2) Crop Data (NASS Crop data) 3) Crop Summary (NASS Crop data) 4) Geography and Population 5) Land Use 6) Livestock Populations 7) Soil Properties

Data presented here include a shapefile that combines fault data for the United States and Canada (Chorlton, 2007; Reed and others, 2005; Styron and Pagani, 2020) and a shapefile of faults for Australia (Chorlton, 2007; Raymond and others, 2012; Styron and Pagani, 2020). These two shapefiles were used as an evidential layer to evaluate the mineral prospectivity for sediment-hosted Pb-Zn deposits (Lawley and others, 2022). References Chorlton, L.B., 2007, Generalized geology of the world: Bedrock domains and major faults in GIS format: a small-scale world geology map with an extended geological attribute database: Geological Survey of Canada Open File 5529, https://doi.org/10.4095/223767. Lawley, C.J.M., McCafferty, A.E., Graham, G.E., Huston, D.L., Kelley, K.D., Czarnota, K., Paradis, S., Peter, J.M., Hayward, N., Barlow, M., Emsbo, P., Coyan, J., San Juan, C.A., and Gadd, M.G., 2022, Data-driven prospectivity modelling of sediment-hosted Zn-Pb mineral systems and their critical r ...