57 datasets found
  1. Data from: Geographic Names Information System: National Geographic Names...

    • icpsr.umich.edu
    • search.datacite.org
    ascii
    Updated Jan 18, 2006
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    United States Department of the Interior. United States Geological Survey (2006). Geographic Names Information System: National Geographic Names Data Base, Michigan Geographic Names [Dataset]. http://doi.org/10.3886/ICPSR08374.v1
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    asciiAvailable download formats
    Dataset updated
    Jan 18, 2006
    Dataset provided by
    Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
    Authors
    United States Department of the Interior. United States Geological Survey
    License

    https://www.icpsr.umich.edu/web/ICPSR/studies/8374/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/8374/terms

    Area covered
    United States, Michigan
    Description

    The Geographic Names Information System (GNIS) was developed by the United States Geological Survey (USGS) to meet major national needs regarding geographic names and their standardization and dissemination. This dataset consists of standard report files written from the National Geographic Names Data Base, one of five data bases maintained in the GNIS. A standard format data file containing Michigan place names and geographic features such as towns, schools, reservoirs, parks, streams, valleys, springs and ridges is accompanied by a file that provides a Cross-Reference to USGS 7.5 x 7.5 minute quadrangle maps for each feature. The records in the data files are organized alphabetically by place or feature name. The other variables available in the dataset include: Federal Information Processing Standard (FIPS) state/county codes, Geographic Coordinates -- latitude and longitude to degrees, minutes, and seconds followed by a single digit alpha directional character, and a GNIS Map Code that can be used with the Cross-Reference file to provide the name of the 7.5 x 7.5 minute quadrangle map that contains that geographic feature.

  2. Data from: National assessment of Tree City USA participation according to...

    • catalog.data.gov
    • cloud.csiss.gmu.edu
    • +3more
    Updated Nov 12, 2020
    + more versions
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    U.S. EPA Office of Research and Development (ORD) (2020). National assessment of Tree City USA participation according to geography and socioeconomic characteristics [Dataset]. https://catalog.data.gov/dataset/national-assessment-of-tree-city-usa-participation-according-to-geography-and-socioeconomi
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    Dataset updated
    Nov 12, 2020
    Dataset provided by
    United States Environmental Protection Agencyhttp://www.epa.gov/
    Description

    Tree City USA is a national program that recognizes municipal commitment to community forestry. In return for meeting program requirements, Tree City USA participants expect social, economic, and/or environmental benefits. Understanding the geographic distribution and socioeconomic characteristics of Tree City USA communities at the national scale can offer insights into the motivations or barriers to program participation, and provide context for community forestry research at finer scales. In this study, researchers assessed patterns in Tree City USA participation for all U.S. communities with more than 2,500 people according to geography, community population size, and socioeconomic characteristics, such as income, education, and race. Nationally, 23.5% of communities studied were Tree City USA participants, and this accounted for 53.9% of the total population in these communities. Tree City USA participation rates varied substantially by U.S. region, but in each region participation rates were higher in larger communities, and long-term participants tended to be larger communities than more recent enrollees. In logistic regression models, owner occupancy rates were significant negative predictors of Tree City USA participation, education and percent white population were positive predictors in many U.S. regions, and inconsistent patterns were observed for income and population age. The findings indicate that communities with smaller populations, lower education levels, and higher minority populations are underserved regionally by Tree City USA, and future efforts should identify and overcome barriers to participation in these types of communities. This dataset is associated with the following publication: Berland , A., D. Herrmann , and M. Hopton. National Assessment of Tree City USA Participation According to Geography andSocioeconomic Characteristics. Arboriculture & Urban Forestry. International Society of Arboriculture, Champaign, IL, USA, 42(2): 120-130, (2016).

  3. Basic Geographic and Historic Data for Interfacing ICPSR Data Sets,...

    • icpsr.umich.edu
    ascii, delimited, sas +2
    Updated Nov 6, 2012
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    Sechrist, Robert P. (2012). Basic Geographic and Historic Data for Interfacing ICPSR Data Sets, 1620-1983 [United States] [Dataset]. http://doi.org/10.3886/ICPSR08159.v2
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    sas, ascii, stata, delimited, spssAvailable download formats
    Dataset updated
    Nov 6, 2012
    Dataset provided by
    Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
    Authors
    Sechrist, Robert P.
    License

    https://www.icpsr.umich.edu/web/ICPSR/studies/8159/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/8159/terms

    Time period covered
    1620 - 1983
    Area covered
    Wyoming, Massachusetts, Delaware, Maryland, Alabama, Kentucky, Arizona, Kansas, Vermont, Pennsylvania
    Description

    This data collection contains the basic information about all counties in the coterminous United States needed for mapping county-based data. It provides an interface between ICPSR datasets and the mapping programs SAS/GRAPH, SURFACE II, and SYMAP. Cloropleth and isopleth maps can be produced by match-merging this dataset with any other dataset (special facilities exist for ICPSR datasets) and running the merged data against a cartographic program. Isopleth mapping programs, using the latitude and longitude coordinates provided for each county seat, can produce maps of ICPSR data. Cloropleth mapping of county-level data can be accomplished after merging by running the merged dataset through SAS/GRAPH. The variables provide state Federal Information Processing (FIPS) codes, county FIPS codes, county names/county seat names, the month, day, and year in which each county was created, the latitude and longitude of county seats, as well as the ICPSR state and county codes.

  4. Geographic Information System Analytics Market Analysis, Size, and Forecast...

    • technavio.com
    Updated Jul 15, 2024
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    Technavio (2024). Geographic Information System Analytics Market Analysis, Size, and Forecast 2024-2028: North America (US and Canada), Europe (France, Germany, UK), APAC (China, India, South Korea), Middle East and Africa , and South America [Dataset]. https://www.technavio.com/report/geographic-information-system-analytics-market-industry-analysis
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    Dataset updated
    Jul 15, 2024
    Dataset provided by
    TechNavio
    Authors
    Technavio
    Time period covered
    2021 - 2025
    Area covered
    France, United States, Canada, Germany, United Kingdom, Global
    Description

    Snapshot img

    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?

    Request Free Sample

    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,

  5. p

    Institute Of Geography And Statistics in Nebraska, United States - 1...

    • poidata.io
    csv, excel, json
    Updated Jul 1, 2025
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    Poidata.io (2025). Institute Of Geography And Statistics in Nebraska, United States - 1 Verified Listings Database [Dataset]. https://www.poidata.io/report/institute-of-geography-and-statistics/united-states/nebraska
    Explore at:
    json, excel, csvAvailable download formats
    Dataset updated
    Jul 1, 2025
    Dataset provided by
    Poidata.io
    Area covered
    Nebraska, United States
    Description

    Comprehensive dataset of 1 Institute of Geography and Statistics in Nebraska, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

  6. g

    Data from: United States Geological Survey Digital Cartographic Data...

    • datasearch.gesis.org
    • icpsr.umich.edu
    v1
    Updated Aug 5, 2015
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    United States Department of the Interior. United States Geological Survey (2015). United States Geological Survey Digital Cartographic Data Standards: Digital Line Graphs from 1:2,000,000-Scale Maps [Dataset]. http://doi.org/10.3886/ICPSR08379.v1
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    v1Available download formats
    Dataset updated
    Aug 5, 2015
    Dataset provided by
    da|ra (Registration agency for social science and economic data)
    Authors
    United States Department of the Interior. United States Geological Survey
    Description

    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.

  7. USA 2020 Census Population Characteristics - Place Geographies

    • hub.arcgis.com
    • data-isdh.opendata.arcgis.com
    • +1more
    Updated Jun 1, 2023
    + more versions
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    Esri (2023). USA 2020 Census Population Characteristics - Place Geographies [Dataset]. https://hub.arcgis.com/maps/9c84c24c55a04c3b8317f37e536e6a8a
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    Dataset updated
    Jun 1, 2023
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    Description

    This layer shows total population counts by sex, age, and race groups data from the 2020 Census Demographic and Housing Characteristics. This is shown by Nation, Consolidated City, Census Designated Place, Incorporated Place boundaries. Each geography layer contains a common set of Census counts based on available attributes from the U.S. Census Bureau. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.   To see the full list of attributes available in this service, go to the "Data" tab above, and then choose "Fields" at the top right. Each attribute contains definitions, additional details, and the formula for calculated fields in the field description.Vintage of boundaries and attributes: 2020 Demographic and Housing Characteristics Table(s): P1, H1, H3, P2, P3, P5, P12, P13, P17, PCT12 (Not all lines of these DHC tables are available in this feature layer.)Data downloaded from: U.S. Census Bureau’s data.census.gov siteDate the Data was Downloaded: May 25, 2023Geography Levels included: Nation, Consolidated City, Census Designated Place, Incorporated PlaceNational Figures: included in Nation layer The United States Census Bureau Demographic and Housing Characteristics: 2020 Census Results 2020 Census Data Quality Geography & 2020 Census Technical Documentation Data Table Guide: includes the final list of tables, lowest level of geography by table and table shells for the Demographic Profile and Demographic and Housing Characteristics.News & Updates This layer is ready to be used in ArcGIS Pro, ArcGIS Online and its configurable apps, Story Maps, dashboards, Notebooks, Python, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the U.S. Census Bureau when using this data. Data Processing Notes: These 2020 Census boundaries come from the US Census TIGER geodatabases. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For Census tracts and block groups, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract and block group boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are unchanged and available as attributes within the data table (units are square meters).  The layer contains all US states, Washington D.C., and Puerto Rico. Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Block groups that fall within the same criteria (Block Group denoted as 0 with no area land) have also been removed.Percentages and derived counts, are calculated values (that can be identified by the "_calc_" stub in the field name). Field alias names were created based on the Table Shells file available from the Data Table Guide for the Demographic Profile and Demographic and Housing Characteristics. Not all lines of all tables listed above are included in this layer. Duplicative counts were dropped. For example, P0030001 was dropped, as it is duplicative of P0010001.To protect the privacy and confidentiality of respondents, their data has been protected using differential privacy techniques by the U.S. Census Bureau.

  8. W

    State Boundaries with Shorelines (National)

    • cloud.csiss.gmu.edu
    Updated Mar 7, 2021
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    United States (2021). State Boundaries with Shorelines (National) [Dataset]. https://cloud.csiss.gmu.edu/uddi/dataset/state-boundaries-with-shorelines-national
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    Dataset updated
    Mar 7, 2021
    Dataset provided by
    United States
    Description

    The United States State Boundaries database is a geographic database of state political boundaries. The database includes boundaries for all 50 states plus Puerto Rico, Washington D.C., American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the U.S. Virgin Islands. In order for others to use the information in the Census MAF/TIGER database in a geographic information system (GIS) or for other geographic applications, the Census Bureau releases to the public extracts of the database in the form of TIGER/Line Shapefiles. State boundaries with shorelines cut in. The State Boundary with Detailed Shorelines database was created using TIGER/LINE 2011 shapefile data gathered from ESRI's Geography Network. The individual county shapefiles were processed into Arc/Info coverages and then appended together to create complete state coverages. OST-R/BTS Hydrographic data was integrated to create detailed shorelines. The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. States and equivalent entities are the primary governmental divisions of the United States. In addition to the fifty States, the Census Bureau treats the District of Columbia, Puerto Rico, and each of the Island Areas (American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the U.S. Virgin Islands) as the statistical equivalents of States for the purpose of data presentation.

  9. n

    Geography, Land Use and Population data for Counties in the Contiguous...

    • cmr.earthdata.nasa.gov
    Updated Apr 21, 2017
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    (2017). Geography, Land Use and Population data for Counties in the Contiguous United States [Dataset]. https://cmr.earthdata.nasa.gov/search/concepts/C1214610539-SCIOPS.html
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    Dataset updated
    Apr 21, 2017
    Time period covered
    Jan 1, 1990 - Dec 31, 1990
    Area covered
    Description

    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

  10. 10 powerful tools and maps with which to teach about population and...

    • library.ncge.org
    Updated Jul 27, 2021
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    NCGE (2021). 10 powerful tools and maps with which to teach about population and demographics [Dataset]. https://library.ncge.org/documents/bae1d5f1cba243ea88d09b043b8444ee
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    Dataset updated
    Jul 27, 2021
    Dataset provided by
    National Council for Geographic Educationhttp://www.ncge.org/
    Authors
    NCGE
    License

    Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
    License information was derived automatically

    Description

    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).

  11. USA 2020 Census Race and Ethnicity Characteristics - Place Geographies

    • data-isdh.opendata.arcgis.com
    Updated Jun 29, 2023
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    Esri (2023). USA 2020 Census Race and Ethnicity Characteristics - Place Geographies [Dataset]. https://data-isdh.opendata.arcgis.com/maps/e5c585386d304366b32ce9ffb0b0bd11
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    Dataset updated
    Jun 29, 2023
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    Description

    This layer shows race and ethnicity data from the 2020 Census Demographic and Housing Characteristics. This is shown by Nation, Consolidated City, Census Designated Place, Incorporated Place boundaries. Each geography layer contains a common set of Census counts based on available attributes from the U.S. Census Bureau. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.   To see the full list of attributes available in this service, go to the "Data" tab above, and then choose "Fields" at the top right. Each attribute contains definitions, additional details, and the formula for calculated fields in the field description.Vintage of boundaries and attributes: 2020 Demographic and Housing Characteristics Table(s): P1, H1, H3, P5, P9 Data downloaded from: U.S. Census Bureau’s data.census.gov siteDate the Data was Downloaded: May 25, 2023Geography Levels included: Nation, Consolidated City, Census Designated Place, Incorporated PlaceNational Figures: included in Nation layer The United States Census Bureau Demographic and Housing Characteristics: 2020 Census Results 2020 Census Data Quality Geography & 2020 Census Technical Documentation Data Table Guide: includes the final list of tables, lowest level of geography by table and table shells for the Demographic Profile and Demographic and Housing Characteristics.News & Updates This layer is ready to be used in ArcGIS Pro, ArcGIS Online and its configurable apps, Story Maps, dashboards, Notebooks, Python, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the U.S. Census Bureau when using this data. Data Processing Notes: These 2020 Census boundaries come from the US Census TIGER geodatabases. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For Census tracts and block groups, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract and block group boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are unchanged and available as attributes within the data table (units are square meters).  The layer contains all US states, Washington D.C., and Puerto Rico. Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Block groups that fall within the same criteria (Block Group denoted as 0 with no area land) have also been removed.Percentages and derived counts, are calculated values (that can be identified by the "_calc_" stub in the field name). Field alias names were created based on the Table Shells file available from the Data Table Guide for the Demographic Profile and Demographic and Housing Characteristics. Not all lines of all tables listed above are included in this layer. Duplicative counts were dropped. For example, P0030001 was dropped, as it is duplicative of P0010001.To protect the privacy and confidentiality of respondents, their data has been protected using differential privacy techniques by the U.S. Census Bureau.

  12. p

    Faculty Of Geography And Histories in New Mexico, United States - 3 Verified...

    • poidata.io
    csv, excel, json
    Updated Jul 15, 2025
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    Poidata.io (2025). Faculty Of Geography And Histories in New Mexico, United States - 3 Verified Listings Database [Dataset]. https://www.poidata.io/report/faculty-of-geography-and-history/united-states/new-mexico
    Explore at:
    csv, json, excelAvailable download formats
    Dataset updated
    Jul 15, 2025
    Dataset provided by
    Poidata.io
    Area covered
    New Mexico, United States
    Description

    Comprehensive dataset of 3 Faculty of geography and histories in New Mexico, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

  13. Regional Crime Analysis Geographic Information System (RCAGIS)

    • icpsr.umich.edu
    Updated May 29, 2002
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    United States Department of Justice. Criminal Division Geographic Information Systems Staff. Baltimore County Police Department (2002). Regional Crime Analysis Geographic Information System (RCAGIS) [Dataset]. http://doi.org/10.3886/ICPSR03372.v1
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    Dataset updated
    May 29, 2002
    Dataset provided by
    Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
    Authors
    United States Department of Justice. Criminal Division Geographic Information Systems Staff. Baltimore County Police Department
    License

    https://www.icpsr.umich.edu/web/ICPSR/studies/3372/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/3372/terms

    Description

    The Regional Crime Analysis GIS (RCAGIS) is an Environmental Systems Research Institute (ESRI) MapObjects-based system that was developed by the United States Department of Justice Criminal Division Geographic Information Systems (GIS) Staff, in conjunction with the Baltimore County Police Department and the Regional Crime Analysis System (RCAS) group, to facilitate the analysis of crime on a regional basis. The RCAGIS system was designed specifically to assist in the analysis of crime incident data across jurisdictional boundaries. Features of the system include: (1) three modes, each designed for a specific level of analysis (simple queries, crime analysis, or reports), (2) wizard-driven (guided) incident database queries, (3) graphical tools for the creation, saving, and printing of map layout files, (4) an interface with CrimeStat spatial statistics software developed by Ned Levine and Associates for advanced analysis tools such as hot spot surfaces and ellipses, (5) tools for graphically viewing and analyzing historical crime trends in specific areas, and (6) linkage tools for drawing connections between vehicle theft and recovery locations, incident locations and suspects' homes, and between attributes in any two loaded shapefiles. RCAGIS also supports digital imagery, such as orthophotos and other raster data sources, and geographic source data in multiple projections. RCAGIS can be configured to support multiple incident database backends and varying database schemas using a field mapping utility.

  14. 2023 American Community Survey: B07404G | Geographical Mobility in the Past...

    • data.census.gov
    + more versions
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    ACS, 2023 American Community Survey: B07404G | Geographical Mobility in the Past Year (Two or More Races) for Residence 1 Year Ago in the United States (ACS 1-Year Estimates Detailed Tables) [Dataset]. https://data.census.gov/table/ACSDT1Y2023.B07404G?t=Race+and+Ethnicity:Two+or+More+Races
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    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Authors
    ACS
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Time period covered
    2023
    Area covered
    United States
    Description

    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 previous place of residence. The characteristics crossed by geographical mobility reflect the current survey year. The estimates do not include people who moved to Puerto Rico, other U.S. Island Areas, or Foreign Countries..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, 2023 American Community Survey 1-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..Tables for Geographical Mobility by Residence 1 Year Ago in the United States are only available for States; Counties; Places; County Subdivisions in selected states (CT, ME, MA, MI, MN, NH, NJ, NY, PA, RI, VT, WI); Combined Statistical Areas; Metropolitan and Micropolitan Statistical Areas, and their associated Metropolitan Divisions and Principal Cities..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.

  15. d

    Market Research Data | Global Map data | Geographic data | Address and Zip...

    • datarade.ai
    .csv
    Updated Oct 19, 2024
    + more versions
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    GeoPostcodes (2024). Market Research Data | Global Map data | Geographic data | Address and Zip Code Database | Geocoded [Dataset]. https://datarade.ai/data-products/geopostcodes-market-research-data-map-data-geographic-dat-geopostcodes
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    .csvAvailable download formats
    Dataset updated
    Oct 19, 2024
    Dataset authored and provided by
    GeoPostcodes
    Area covered
    Saint Barthélemy, Poland, Christmas Island, Papua New Guinea, South Sudan, Monaco, Tokelau, Korea (Democratic People's Republic of), Slovenia, Sierra Leone
    Description

    A global self-hosted Market Research dataset containing all administrative divisions, cities, addresses, and zip codes for 247 countries. All geospatial data is updated weekly to maintain the highest data quality, including challenging countries such as China, Brazil, Russia, and the United Kingdom.

    Use cases for the Global Zip Code Database (Market Research data)

    • Address capture and validation

    • Map and visualization

    • Reporting and Business Intelligence (BI)

    • Master Data Mangement

    • Logistics and Supply Chain Management

    • Sales and Marketing

    Data export methodology

    Our map data packages are offered in variable formats, including .csv. All geographic data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

    Product Features

    • Fully and accurately geocoded

    • Administrative areas with a level range of 0-4

    • Multi-language support including address names in local and foreign languages

    • Comprehensive city definitions across countries

    For additional insights, you can combine the map data with:

    • UNLOCODE and IATA codes

    • Time zones and Daylight Saving Times

    Why do companies choose our Market Research databases

    • Enterprise-grade service

    • Reduce integration time and cost by 30%

    • Weekly updates for the highest quality

    Note: Custom geographic data packages are available. Please submit a request via the above contact button for more details.

  16. w

    Data from: The United States of America country update

    • data.wu.ac.at
    Updated Apr 9, 2018
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    (2018). The United States of America country update [Dataset]. https://data.wu.ac.at/odso/geothermaldata_org/NWMzYzk5MzgtY2UwZC00MDczLWJhMTUtZGU0OGI2OGI5YzMw
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    Dataset updated
    Apr 9, 2018
    Area covered
    United States
    Description

    No Publication Abstract is Available

  17. p

    Faculty Of Geography And Histories in Colorado, United States - 2 Verified...

    • poidata.io
    csv, excel, json
    Updated Jun 26, 2025
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    Poidata.io (2025). Faculty Of Geography And Histories in Colorado, United States - 2 Verified Listings Database [Dataset]. https://www.poidata.io/report/faculty-of-geography-and-history/united-states/colorado
    Explore at:
    json, csv, excelAvailable download formats
    Dataset updated
    Jun 26, 2025
    Dataset provided by
    Poidata.io
    Area covered
    Colorado, United States
    Description

    Comprehensive dataset of 2 Faculty of geography and histories in Colorado, United States as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

  18. Uniform Crime Reports (UCR) and Federal Information Processing Standards...

    • icpsr.umich.edu
    • catalog.data.gov
    ascii, sas, spss +1
    Updated Nov 4, 2005
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    Inter-university Consortium for Political and Social Research (2005). Uniform Crime Reports (UCR) and Federal Information Processing Standards (FIPS) State and County Geographic Codes, 1990: United States [Dataset]. http://doi.org/10.3886/ICPSR02565.v1
    Explore at:
    sas, ascii, stata, spssAvailable download formats
    Dataset updated
    Nov 4, 2005
    Dataset authored and provided by
    Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
    License

    https://www.icpsr.umich.edu/web/ICPSR/studies/2565/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/2565/terms

    Time period covered
    1990 - 1996
    Area covered
    United States
    Description

    This dataset was created to facilitate the conversion of Uniform Crime Reporting (UCR) Program state and county codes to Federal Information Processing Standards (FIPS) state and county codes. The four UCR agency-level data files archived at ICPSR in Uniform Crime Reporting Program Data: United States contain UCR state and county codes as geographic identifiers. Researchers who wish to use these data with other sources, such as Census data, may want to convert these UCR codes to FIPS codes in order to link the different data sources. This file was created to facilitate this linkage. It contains state abbreviations, UCR state and county codes, FIPS state and county codes, and county names for all counties present in the UCR data files since 1990. These same FIPS codes were used to create the UCR County-Level Detailed Arrest and Offense files from 1990-1996.

  19. Geographic Management Information System

    • s.cnmilf.com
    • datasets.ai
    • +1more
    Updated Jun 25, 2024
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    data.usaid.gov (2024). Geographic Management Information System [Dataset]. https://s.cnmilf.com/user74170196/https/catalog.data.gov/dataset/geographic-management-information-system
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    Dataset updated
    Jun 25, 2024
    Dataset provided by
    United States Agency for International Developmenthttp://usaid.gov/
    Description

    The Geographic Management Information System (GeoMIS) is a FISMA Moderate minor application built using ArcGIS Server and portal, Microsoft SQL, and a web-facing front-end. The system can be accessed over the internet via https://www.usaidgiswbg.com using a web browser. GeoMIS is based on a commercial off-the-shelf product developed by Esri. Esri is creates geographic information system (GIS) software, web GIS and geodatabase management applications and is based in California. GeoMISIt is maintained by an Israeli company, Systematics (see Attachment 3) which is EsriI's agent in Israel. The mission has an annual maintenance contract with Systematics for GeoMIS. GeoMIS has 100 users from USAID staff (USA Direct Hire and Foreign Service Nationals) and 200 users from USAID contractors and grantees. The system is installed at USAID WBG office in Tel Aviv/Israel inside the computer room in the DMZ. It has no interconnections with any other system.

  20. USA Topo Maps

    • maps.openlaredo.com
    • data.openlaredo.com
    • +14more
    Updated Feb 10, 2012
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    Esri (2012). USA Topo Maps [Dataset]. https://maps.openlaredo.com/maps/esri::usa-topo-maps-1/about
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    Dataset updated
    Feb 10, 2012
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    Description

    Important Note: This item is in mature support as of June 2021 and is no longer updated. This map presents land cover and detailed topographic maps for the United States. It uses the USA Topographic Map service. The map includes the National Park Service (NPS) Natural Earth physical map at 1.24km per pixel for the world at small scales, i-cubed eTOPO 1:250,000-scale maps for the contiguous United States at medium scales, and National Geographic TOPO! 1:100,000 and 1:24,000-scale maps (1:250,000 and 1:63,000 in Alaska) for the United States at large scales. The TOPO! maps are seamless, scanned images of United States Geological Survey (USGS) paper topographic maps.The maps provide a very useful basemap for a variety of applications, particularly in rural areas where the topographic maps provide unique detail and features from other basemaps.To add this map service into a desktop application directly, go to the entry for the USA Topo Maps map service. Tip: Here are some famous locations as they appear in this web map, accessed by including their location in the URL that launches the map:Grand Canyon, ArizonaGolden Gate, CaliforniaThe Statue of Liberty, New YorkWashington DCCanyon De Chelly, ArizonaYellowstone National Park, WyomingArea 51, Nevada

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United States Department of the Interior. United States Geological Survey (2006). Geographic Names Information System: National Geographic Names Data Base, Michigan Geographic Names [Dataset]. http://doi.org/10.3886/ICPSR08374.v1
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Data from: Geographic Names Information System: National Geographic Names Data Base, Michigan Geographic Names

Related Article
Explore at:
asciiAvailable download formats
Dataset updated
Jan 18, 2006
Dataset provided by
Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
Authors
United States Department of the Interior. United States Geological Survey
License

https://www.icpsr.umich.edu/web/ICPSR/studies/8374/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/8374/terms

Area covered
United States, Michigan
Description

The Geographic Names Information System (GNIS) was developed by the United States Geological Survey (USGS) to meet major national needs regarding geographic names and their standardization and dissemination. This dataset consists of standard report files written from the National Geographic Names Data Base, one of five data bases maintained in the GNIS. A standard format data file containing Michigan place names and geographic features such as towns, schools, reservoirs, parks, streams, valleys, springs and ridges is accompanied by a file that provides a Cross-Reference to USGS 7.5 x 7.5 minute quadrangle maps for each feature. The records in the data files are organized alphabetically by place or feature name. The other variables available in the dataset include: Federal Information Processing Standard (FIPS) state/county codes, Geographic Coordinates -- latitude and longitude to degrees, minutes, and seconds followed by a single digit alpha directional character, and a GNIS Map Code that can be used with the Cross-Reference file to provide the name of the 7.5 x 7.5 minute quadrangle map that contains that geographic feature.

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