This map shows the relationship between Federal payments toward conservation and wetlands and payments toward producers not including conservation/wetlands. The data is produced by the USDA National Agricultural Statistics Service (USDA).Areas in yellow show where there are high amounts of Federal payments toward Conservation in comparison to other types, whereas areas in light blue have a higher amount of Federal payments toward all other agriculture in comparison to Conservation. Areas in black have an overall high amount of both types of payments. The map uses size to emphasize which counties received the overall largest receipts in US dollars.In 2017, the average farm received an average of $13,906, and conservation/wetland programs received and average of $6,980. These are the central colors of the map in order to anchor the map around the national figure. Areas with a pattern above or below the national average are highlighted by the colors along the edges of the legend (mentioned in the previous paragraph). For more information about Federal payments in 2017, visit this summary table from the USDA.For more information about the relationship mapping style used in this map, visit this blog. About the data and source:The Census of Agriculture, produced by the USDA National Agricultural Statistics Service (USDA), provides a complete count of America's farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2017, and provides a in-depth look at the agricultural industry.This layer summarizes payments made to producers by the Federal government from the 2017 Census of Agriculture at the county level.This layer was produced from data downloaded using the USDA's QuickStats Application. The data was transformed using the Pivot Table tool in ArcGIS Pro and joined to the county boundary file provided by the USDA. The layer was published as feature layer in ArcGIS Online.Dataset SummaryPhenomenon Mapped: Payments made to producers by the Federal governmentCoordinate System: Web Mercator Auxiliary SphereExtent: United States including Hawaii and AlaskaVisible Scale: All ScalesSource: USDA National Agricultural Statistics Service QuickStats ApplicationPublication Date: 2017AttributesThis layer provides values for the following attributes. Note that some values are not disclosed (coded as -1 in the layer) to protect the privacy of producers in areas with limited production.Federal Payments - Operations with ReceiptsFederal Payments - Receipts in US DollarsFederal Payments - Receipts in US Dollars per OperationFederal Payments not Including Conservation and Wetland Programs - Operations with ReceiptsFederal Payments not Including Conservation and Wetland Programs - Receipts in US DollarsFederal Payments not Including Conservation and Wetland Programs - Receipts in US Dollars per OperationFederal Payments for Conservation and Wetland Programs - Operations with ReceiptsFederal Payments for Conservation and Wetland Programs - Receipts in US DollarsFederal Payments for Conservation and Wetland Programs - Receipts in US Dollars per OperationConservation and wetland programs include:Conservation Reserve Program (CRP)Wetlands Reserve Program (WRP)Farmable Wetlands Program (FWP)Conservation Reserve Enhancement Program (CREP)Other programs with payments to producers include:2014 Agricultural Act (Farm Bill)Agriculture Risk Coverage (ARC)Price Loss Coverage (PLC)Commodity Credit Corporation (CCC)Loan Deficiency PaymentsDisaster Assistance ProgramsState and local government agricultural program payments and Federal crop insurance payments are not included.Additionally, attributes of State Name, State Code, County Name and County Code are included to facilitate cartography and use with other layers.

What is NAIP?The National Agriculture Imagery Program (NAIP) acquires aerial imagery during the agricultural growing seasons in the contiguous U.S. A primary goal of the NAIP program is to make digital ortho photography available to governmental agencies and the public within a year of acquisition.NAIP is administered by the USDA's Farm Production and Conservation Business Center through the Aerial Photography Field Office in Salt Lake City. The APFO as of August 16, 2020 has transitioned to the USDA FPAC-BC's Geospatial Enterprise Operations Branch (GEO). This "leaf-on" imagery is used as a base layer for GIS programs in FSA's County Service Centers, and is used to maintain the Common Land Unit (CLU) boundaries.How can I Access NAIP?On the web GEO (APFO) public image services can be accessed through the REST endpoint here. Compressed County Mosaics (CCMs) are available to the general public through the USDA Geospatial Data Gateway. All years of available imagery may be downloaded as 1/2, 1, or 2 meter CCMs depending on the original spatial resolution. CCMs with a file size larger than 8 GB are not able to be downloaded from the Gateway. Full resolution 4 band quarter quads (DOQQs) are available for purchase from FPAC GEO. Contact the GEO Customer Service Section for information on pricing for DOQQs and how to obtain CCMs larger than 8 GB. A NAIP image service is also available on ArcGIS Online through an organizational subscription.How can NAIP be used?NAIP is used by many non-FSA public and private sector customers for a wide variety of projects. A detailed study is available in the Qualitative and Quantitative Synopsis on NAIP Usage from 2004 -2008: Click here for a list of NAIP Information and Distribution Nodes.When is NAIP acquired?NAIP projects are contracted each year based upon available funding and the FSA imagery acquisition cycle. Beginning in 2003, NAIP was acquired on a 5-year cycle. 2008 was a transition year, a three-year cycle began in 2009, NAIP was on a two-year cycle until 2016, currently NAIP is on a 3 year refresh cycle. Click here >> for an interactive PDF status map of NAIP acquisitions from 2002 - 2018. 2021 acquisition status dashboard is available here.What are NAIP Specifications?NAIP imagery is currently acquired at 60cm ground sample distance (GSD) with a horizontal accuracy that matches within four meters of photo-identifiable ground control points.The default spectral resolution beginning in 2010 is four bands: Red, Green, Blue and Near Infrared.Contractually, every attempt will be made to comply with the specification of no more than 10% cloud cover per quarter quad tile, weather conditions permitting.All imagery is inspected for horizontal accuracy and tonal quality. Make Comments/Observations about current NAIP imagery.If you use NAIP imagery and have comments or find a problem with the imagery please use the NAIP Imagery Feedback Map to let us know what you find or how you are using NAIP imagery. Click here to access the map.**The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updates**Title: National Agriculture Imagery Program (NAIP) History 2002-2021Item Type: Web Mapping Application URL Summary: Story map depicting the highlights and changes throughout the National Agriculture Imagery Program (NAIP) from 2002-2021.Notes: Prepared by: Uploaded by EMcRae_NMCDCSource: URL referencing this original map product: https://nmcdc.maps.arcgis.com/home/item.html?id=445e3dfd16c4401f95f78ad5905a4cceFeature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=8eb6c5e7adc54ec889dd6fc9cc2c14c4UID: 26Data Requested: Ag CensusMethod of Acquisition: Living AtlasDate Acquired: May 2022Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 8Tags: PENDING

The Digital Surficial Geologic-GIS Map of Weir Farm National Historical Park and Vicinity, Connecticut is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (wefa_surficial_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (wefa_surficial_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (wefa_surficial_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (wefa_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (wefa_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (wefa_surficial_geology_metadata_faq.pdf). Please read the wefa_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (wefa_surficial_geology_metadata.txt or wefa_surficial_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

The Digital Geologic-GIS Map of the Bell Farm Quadrangle and parts of the Barthell SW Quadrangle, Kentucky is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (befa_geology.gdb), and a 2.) Open Geospatial Consortium (OGC) geopackage. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (befa_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (befa_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (biso_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (biso_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (befa_geology_metadata_faq.pdf). Please read the biso_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. QGIS software is available for free at: https://www.qgis.org/en/site/. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Kentucky Geological Survey and U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (befa_geology_metadata.txt or befa_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

Snapshot img

GIS Market Size 2025-2029

The GIS market size is forecast to increase by USD 24.07 billion at a CAGR of 20.3% between 2024 and 2029.

The Global Geographic Information System (GIS) market is experiencing significant growth due to the integration of Building Information Modeling (BIM) software and GIS, enabling more accurate and efficient construction projects. The increasing adoption of GIS solutions in precision farming for soil and water management is another key trend, with farmers utilizing sensors, GPS, and satellite data to optimize fertilizer usage and crop yields. However, challenges persist, such as the lack of proper planning leading to implementation failures of GIS solutions. In the realm of smart cities, GIS plays a crucial role in managing data from various sources, including LIDAR, computer-aided design, and digital twin technologies. Additionally, public safety and insurance industries are leveraging GIS for server-based data analysis, while smartphones and antennas facilitate real-time data collection. Amidst this digital transformation, ensuring data security and privacy becomes paramount, making it a critical consideration for market participants.

What will be the Size of the GIS Market During the Forecast Period?

Request Free Sample

The Global Geographic Information System (GIS) market encompasses a range of software solutions and hardware components used to capture, manage, analyze, and visualize geospatial data. Key industries driving market growth include transportation, smart city planning, green buildings, architecture and construction, utilities, oil and gas, agriculture, and urbanization. GIS technology plays a pivotal role in various applications such as 4D GIS software for infrastructure project management, augmented reality platforms for enhanced visualization, and LIDAR and GNSS/GPS antenna for accurate location data collection. Cloud technology is transforming the GIS landscape by enabling real-time data access and collaboration. The transportation sector is leveraging GIS for route optimization, asset management, and predictive maintenance.
Urbanization and population growth are fueling the demand for GIS in city planning and disaster management. Additionally, GIS is increasingly being adopted in sectors like agriculture for precision farming and soil mapping, and in the construction industry for Building Information Modeling (BIM). The market is also witnessing the emergence of innovative applications in areas such as video games and natural disasters risk assessment. Mobile devices are further expanding the reach of GIS, making it accessible to a wider audience. Overall, the market is poised for significant growth, driven by the increasing need for data-driven decision-making and the integration of geospatial technology into various industries.

How is this GIS Industry segmented and which is the largest segment?

The gis industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Software
  Data
  Services


Type

  Telematics and navigation
  Mapping
  Surveying
  Location-based services


Device

  Desktop
  Mobile


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK
    France


  APAC

    China
    Japan
    South Korea


  South America

    Brazil


  Middle East and Africa

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.

The market encompasses desktop, mobile, cloud, and server software solutions, catering to various industries. Open-source software with limited features poses a challenge due to the prevalence of counterfeit products. Yet, the market witnesses an emerging trend toward cloud-based GIS software adoption. However, standardization and interoperability concerns hinder widespread adoption. Geospatial technology is utilized extensively in sectors such as Transportation, Utilities, Oil and Gas, Agriculture, and Urbanization, driven by population growth, urban planning, and sustainable development. Key applications include smart city planning, green buildings, BIM, 4D GIS software, augmented reality platforms, GIS collectors, LIDAR, and GNSS/GPS antennas. Cloud technology, mobile devices, and satellite imaging are critical enablers.

Get a glance at the GIS Industry report of share of various segments Request Free Sample

The software segment was valued at USD 5.06 billion in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 38% 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 th

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. GIS Database 2002-2005: Project Size = 1,898 acres Fort Larned National Historic Site (including the Rut Site) = 705 acres 16 Map Classes 11 Vegetated 5 Non-vegetated Minimum Mapping Unit = ½ hectare is the program standard but this was modified at FOLS to ¼ acre. Total Size = 229 Polygons Average Polygon Size = 8.3 acres Overall Thematic Accuracy = 92% To produce the digital map, a combination of 1:8,500-scale (0.75 meter pixels) color infrared digital ortho-imagery acquired on October 26, 2005 by the Kansas Applied Remote Sensing Program and 1:12,000-scale true color ortho-rectified imagery acquired in 2005 by the U.S. Department of Agriculture - Farm Service Agency’s Aerial Photography Field Office, and all of the GPS referenced ground data were used to interpret the complex patterns of vegetation and land-use. In the end, 16 map units (11 vegetated and 5 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 and six accuracy assessment (AA) data points were collected in 2006 by KNSHI and used to determine the map’s accuracy. After final revisions, the accuracy assessment revealed an overall thematic accuracy of 92%.

This map is designated as Final.Land-Use Data Quality ControlEvery published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process.Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legendspecific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level.Provisional data sets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.The 2011 Marin County land use survey data was developed by the State of California, Department of Water Resources (DWR) through its Division of Integrated Regional Water Management (DIRWM) and Division of Statewide Integrated Water Management (DSIWM). Land use boundaries were digitized and land use data was gathered by staff of DWR’s North Central Region using extensive field visits and aerial photography. Land use polygons in agricultural areas were mapped in greater detail than areas of urban or native vegetation. Quality control procedures were performed jointly by staff at DWR’s DSIWM headquarters, under the leadership of Jean Woods, and North Central Region, under the supervision of Kim Rosmaier. This data was developed to aid DWR’s ongoing efforts to monitor land use for the main purpose of determining current and projected water uses. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov. This data represents a land use survey of Marin County conducted by the California Department of Water Resources, North Central Regional Office staff. The field work for this survey was conducted during June 2011 by staff visiting each field and noting what was grown. Land use field boundaries were digitized using ArcGIS 9.3 then ArcGIS 10.0 using 2010 National Agriculture Imagery Program (NAIP) one-meter imagery as the base. To facilitate digitizing, Marin was divided in 2 portions, the Point Reyes area and all other areas of Marin County. These two areas were recombined after each portion was finished. The outer boundary of this land use survey coincides with the county line revisions completed by the California Department of Forestry and Fire Protection in 2009. Field boundaries were not drawn to represent legal parcel (ownership) boundaries, or meant to be used as parcel boundaries. Images and land use boundaries were loaded onto laptop computers that were used as the field data collection tools. Staff took these laptops into the field and virtually all the areas were visited to positively identify the land uses. Land use codes were digitized in the field using ESRI ArcMAP software, version 10.0. Global positioning system (GPS) units connected to the laptops were used to confirm the field team's location with respect to the fields. Staff took these laptops into the field and virtually all the areas were visited to positively identify the land uses. Land use codes were digitized in the field on laptop computers using ESRI ArcMAP software, version 10.0. The field team used a customized menu program to facilitate the gathering of field data. Before final processing, standard quality control procedures were performed jointly by staff at DWR’s North Central Region, and at DSIWM headquarters under the leadership of Jean Woods. Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the orthorectified NAIP imagery, is approximately 6 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.

The Census of Agriculture, produced by the USDA National Agricultural Statistics Service (USDA), provides a complete count of America's farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2017, and provides an in-depth look at the agricultural industry.This layer summarizes payments made to producers by the Federal government from the 2017 Census of Agriculture at the county level. This layer was produced from data downloaded using the USDA's QuickStats Application. The data was transformed using the Pivot Table tool in ArcGIS Pro and joined to the county boundary file provided by the USDA. The layer was published as feature layer in ArcGIS Online.Dataset SummaryPhenomenon Mapped: Payments made to producers by the Federal government Coordinate System: Web Mercator Auxiliary SphereExtent: United States including Hawaii and AlaskaVisible Scale: All ScalesSource: USDA National Agricultural Statistics Service QuickStats ApplicationPublication Date: 2017AttributesThis layer provides values for the following attributes. Note that some values are not disclosed (coded as -1 in the layer) to protect the privacy of producers in areas with limited production.Federal Payments - Operations with ReceiptsFederal Payments - Receipts in US DollarsFederal Payments - Receipts in US Dollars per OperationFederal Payments not Including Conservation and Wetland Programs - Operations with ReceiptsFederal Payments not Including Conservation and Wetland Programs - Receipts in US DollarsFederal Payments not Including Conservation and Wetland Programs - Receipts in US Dollars per OperationFederal Payments for Conservation and Wetland Programs - Operations with ReceiptsFederal Payments for Conservation and Wetland Programs - Receipts in US DollarsFederal Payments for Conservation and Wetland Programs - Receipts in US Dollars per OperationConservation and wetland programs include:Conservation Reserve Program (CRP)Wetlands Reserve Program (WRP)Farmable Wetlands Program (FWP)Conservation Reserve Enhancement Program (CREP)Other programs with payments to producers include:2014 Agricultural Act (Farm Bill)Agriculture Risk Coverage (ARC)Price Loss Coverage (PLC)Commodity Credit Corporation (CCC)Loan Deficiency PaymentsDisaster Assistance ProgramsState and local government agricultural program payments and Federal crop insurance payments are not included.Additionally, attributes of State Name, State Code, County Name and County Code are included to facilitate cartography and use with other layers.What can you do with this layer?This layer can be used throughout the ArcGIS system. Feature layers can be used just like any other vector layer. You can use feature layers as an input to geoprocessing tools in ArcGIS Pro or in Analysis in ArcGIS Online. Combine the layer with others in a map and set custom symbology or create a pop-up tailored for your users. For the details of working with feature layers the help documentation for ArcGIS Pro or the help documentation for ArcGIS Online are great places to start. The ArcGIS Blog is a great source of ideas for things you can do with feature layers. This layer is part of ArcGIS Living Atlas of the World that provides an easy way to find and explore many other beautiful and authoritative layers, maps, and applications on hundreds of topics.

The Digital Geologic-GIS Map of parts of the Zapata Ranch and Mosca Pass Quadrangles, Colorado is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) an ESRI file geodatabase (zara_geology.gdb), and a 2.) Open Geospatial Consortium (OGC) geopackage. The file geodatabase format is supported with a 1.) ArcGIS Pro 3.X map file (.mapx) file (zara_geology.mapx) and individual Pro 3.X layer (.lyrx) files (for each GIS data layer). Upon request, the GIS data is also available in ESRI shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (grsa_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (grsa_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (zara_geology_metadata_faq.pdf). Please read the grsa_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. QGIS software is available for free at: https://www.qgis.org/en/site/. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri.htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (zara_geology_metadata.txt or zara_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in ArcGIS Pro, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

Land Management Software Market size was valued at USD 1.69 Billion in 2024 and is projected to reach USD 2.62 Billion by 2031, growing at a CAGR of 5.65% from 2024 to 2031.

The growth of land management software is primarily driven by the increasing demand for efficient land use, advancements in geospatial technology, regulatory compliance, and the need for data-driven decision-making. As global populations grow and urbanization accelerates, there is a growing need for efficient land resource management. Land management software offers tools to optimize land use, enhance productivity in agriculture, forestry, and urban planning, and ensure sustainable development practices.

Advancements in geospatial technology, such as Geographic Information Systems (GIS), remote sensing, and satellite imagery, have significantly enhanced the capabilities of land management software, enabling more accurate mapping, monitoring, and analysis of land resources. Regulatory compliance and environmental concerns also drive the adoption of land management software among government agencies, landowners, and businesses.

Data-driven decision-making is another driving factor, as land management software provides powerful analytical tools for processing large volumes of spatial data, generating insights, and supporting data-driven decision-making processes. The growing awareness of climate change risks and the need for resilient land management practices drives the adoption of software solutions that enable climate-smart land management.

Precision agriculture practices are increasingly emphasized in the agricultural sector, with land management software playing a critical role in supporting these practices. The emergence of integrated land management platforms that combine GIS, asset management, and workflow automation capabilities is also driving the adoption of comprehensive software solutions.

In conclusion, the growth of land management software is driven by the need for efficient land use, advancements in technology, regulatory requirements, and the recognition of the importance of sustainable land management practices in addressing global challenges such as food security, environmental degradation, and climate change.

This map is designated as Final.Land-Use Data Quality ControlEvery published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process. Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legendspecific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level.Provisional datasets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.The 2006 Del Norte County land use survey data set was developed by DWR through its Division of Planning and Local Assistance which, following reorganization in 2009 has been subdivided into the Division of Statewide Integrated Water Management (DSIWM) and the Division of Integrated Regional Water Management (DIRWM). The data was gathered using aerial photography and extensive field visits. The land use boundaries and attributes were digitized and the resultant data went through standard quality control procedures before finalizing. The land uses that were gathered were detailed agricultural land uses, and lesser detailed urban and native vegetation land uses. The data was gathered and digitized by staff of DWR’s Northern Regional Office. Quality control procedures were performed jointly by staff at DWR’s Statewide Integrated Water Management headquarters and Northern Regional Office, under the supervision of Tito Cervantes, Senior Land and Water Use Scientist. This data was developed to aid DWR’s ongoing efforts to monitor land use for the main purpose of determining current and projected water uses. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov. This data represents a land use survey of Butte County conducted by DWR, Northern District Office staff, under the leadership of Tito Cervantes, Senior Land and Water Use Supervisor. The field work for this survey was conducted during the summer of 2004. ND staff physically visited each delineated field, noting the crops grown at each location. Field survey boundary data was developed using: 1. The county was surveyed using the 2005 one-meter resolution National Agriculture Imagery Program (NAIP) digital aerial photos as a digital reference for line work and field work. 2. From the 2005 NAIP imagery, digital 7.5’quadrangle sized images were created, with one-meter resolution. These were used in the spring of 2006 to develop the digital land use boundaries that would be used in the survey. The digitizing of these boundaries was done using AutoCAD Map software. 3. The digital images and land use boundaries were copied onto laptop computers that, in most cases, were used as the field data collection tools. The staff took these laptops into the field and virtually all the areas were visited to positively identify the agricultural land use. The site visits occurred between June and August 2006. Land use codes were digitized directly into the laptop computers using AUTOCAD (using a standardized digitizing process). Some staff took the printed aerial photos into the field and wrote land use codes directly onto these photo field sheets. The data from the photo field sheets were digitized back in the office. For both data gathering techniques any land use boundary changes were noted and corrected in the office. Urban and native classes of land use were mapped by both field observation and photo interpretation. 4. The linework and attributes from each quadrangle drawing file were brought into ARCINFO and both quadrangle and survey-wide coverages were created, and underwent quality checks. These coverages were converted to shapefiles using ArcMAP. 5. After quality control/assurance procedures were completed on each file, the data was finalized. The primary focus of this land use survey is mapping agricultural fields. Urban residences and other urban areas were delineated using aerial photo interpretation. Some urban areas may have been missed, especially in forested areas. Before final processing, standard quality control procedures were performed jointly by staff at DWR's Northern District, and at DPLA headquarters under the leadership of Jean Woods, Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the 2005 one-meter resolution National Agriculture Imagery Program (NAIP), is approximately 12.1 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.

GIS Market is Segmented by Component (Hardware and Software), by Function (Mapping, Surveying, Telematics and Navigation, Location-Based Services), by End User (Agriculture, Utilities, and Mining, Among Others), and by Geography (North America, Europe, Asia Pacific, and Rest of the World). The Report Offers Market Forecasts and Size in Value (USD) for all the Above Segments.

The United States geographic information system (GIS) market size reached USD 4.3 Billion in 2024. Looking forward, IMARC Group expects the market to reach USD 10.1 Billion by 2033, exhibiting a growth rate (CAGR) of 9.9% during 2025-2033.

IMARC Group provides an analysis of the key trends in each segment of the United States geographic information system (GIS) market report, along with forecasts at the regional and country level from 2025-2033. Our report has categorized the market based on component, function, device and end use industry.

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%

Snapshot img

GIS In Telecom Sector Market Size 2024-2028

The GIS in telecom sector market size is forecast to increase by USD 1.91 billion at a CAGR of 14.68% between 2023 and 2028.

Geographic Information Systems (GIS) have gained significant traction In the telecom sector due to the increasing adoption of advanced technologies such as big data, sensors, drones, and LiDAR. The use of GIS enables telecom companies to effectively manage and analyze large volumes of digital data, including satellite and GPS information, to optimize infrastructure monitoring and antenna placement. In the context of smart cities, GIS plays a crucial role in enabling efficient communication between developers and end-users by providing real-time data on construction progress and infrastructure status. Moreover, the integration of LiDAR technology with drones offers enhanced capabilities for surveying and mapping telecom infrastructure, leading to improved accuracy and efficiency.
However, the implementation of GIS In the telecom sector also presents challenges, including data security concerns and the need for servers and computers to handle the large volumes of data generated by these technologies. In summary, the telecom sector's growing reliance on digital technologies such as GIS, big data, sensors, drones, and LiDAR is driving market growth, while the need for effective data management and security solutions presents challenges that must be addressed.

What will be the Size of the GIS In Telecom Sector Market During the Forecast Period?

Request Free Sample

The Geographic Information System (GIS) market In the telecom sector is experiencing significant growth due to the increasing demand for electronic information and visual representation of data in various industries. This market encompasses a range of hardware and software solutions, including GNSS/GPS antennas, Lidar, GIS collectors, total stations, imaging sensors, and more. Major industries such as agriculture, oil & gas, architecture, and infrastructure monitoring are leveraging GIS technology for data analysis and decision-making. The adoption rate of GIS In the telecom sector is driven by the need for efficient data management and analysis, as well as the integration of real-time data from various sources.
Data formats and sources vary widely, from satellite and aerial imagery to ground-based sensors and IoT devices. The market is also witnessing innovation from startups and established players, leading to advancements in data processing capabilities and integration with other technologies like 5G networks and AI. Applications of GIS In the telecom sector include smart urban planning, smart utilities, and smart public works, among others.

How is this GIS In Telecom Sector Industry segmented and which is the largest segment?

The GIS in telecom sector 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.

Product

  Software
  Data
  Services


Deployment

  On-premises
  Cloud


Geography

  APAC

    China


  North America

    Canada
    US


  Europe

    UK
    Italy


  South America



  Middle East and Africa

By Product Insights

The software segment is estimated to witness significant growth during the forecast period. The telecom sector's Global GIS market encompasses software solutions for desktops, mobiles, cloud, and servers, along with developers' platforms. companies provide industry-specific GIS software, expanding the growth potential of this segment. Telecom companies heavily utilize intelligent maps generated by GIS for informed decisions on capacity planning and enhancements, such as improved service and next-generation networks. This drives significant growth In the software segment. Commercial entities offer open-source GIS software to counteract the threat of counterfeit products.
GIS technologies are integral to telecom network management, spatial data analysis, infrastructure planning, location-based services, network coverage mapping, data visualization, asset management, real-time network monitoring, design, wireless network mapping, integration, maintenance, optimization, and geospatial intelligence. Key applications include 5G network planning, network visualization, outage management, geolocation, mobile network optimization, and smart infrastructure planning. The GIS industry caters to major industries, including agriculture, oil & gas, architecture, engineering, construction, mining, utilities, retail, healthcare, government, and smart city planning. GIS solutions facilitate real-time data management, spatial information, and non-spatial information, offering enterprise solutions and transportation applications.

Get a glance at the market report of share of variou

This map is designated as Final.Land-Use Data Quality ControlEvery published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process.Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legendspecific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level.Provisional data sets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.The 2015 Sacramento County land use survey data was developed by the State of California, Department of Water Resources (DWR) through its Division of Integrated Regional Water Management (DIRWM) and Division of Statewide Integrated Water Management (DSIWM). Land use boundaries were digitized and land use data were gathered by staff of DWR’s North Central Region using extensive field visits and aerial photography. Land use polygons in agricultural areas were mapped in greater detail than areas of urban or native vegetation. Quality control procedures were performed jointly by staff at DWR’s DSIWM headquarters, under the leadership of Jean Woods, and North Central Region, under the supervision of Kim Rosmaier. This data was developed to aid DWR’s ongoing efforts to monitor land use for the main purpose of determining current and projected water uses. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov. This data represents a land use survey of Sacramento County conducted by the California Department of Water Resources, North Central Regional Office staff. Land use field boundaries were digitized with ArcGIS 10.3 using 2014 U.S.D.A National Agriculture Imagery Program (NAIP) one-meter imagery as the base. Agricultural fields were delineated by following actual field boundaries instead of using the centerlines of roads to represent the field borders. Field boundaries were reviewed and updated using 2015 Landsat 8 imagery. Field boundaries were not drawn to represent legal parcel (ownership) boundaries, and are not meant to be used as parcel boundaries. The field work for this survey was conducted from July 2015 through August 2015. Images, land use boundaries and ESRI ArcMap software were loaded onto laptop computers that were used as the field data collection tools. Staff took these laptops into the field and virtually all agricultural fields were visited to identify the land use. Global positioning System (GPS) units connected to the laptops were used to confirm the surveyor's location with respect to the fields. Land use codes were digitized in the field using dropdown selections from defined domains. Agricultural fields the staff were unable to access were designated 'E' in the Class field for Entry Denied in accordance with the 2009 Landuse Legend. Upon completion of the survey, a Python script was used to convert the data table into the standard land use format. ArcGIS geoprocessing tools and topology rules were used to locate errors for quality control. The primary focus of this land use survey is mapping agricultural fields. Urban residences and other urban areas were delineated using aerial photo interpretation. Some urban areas may have been missed. Rural residential land use was delineated by drawing polygons to surround houses and other buildings along with some of the surrounding land. These footprint areas do not represent the entire footprint of urban land. Sources of irrigation water were identified for general areas and occasionally supplemented by information obtained from landowners. Water source information was not collected for each field in the survey, so the water source listed for a specific agricultural field may not be accurate. Before final processing, standard quality control procedures were performed jointly by staff at DWR’s North Central Region, and at DSIWM headquarters under the leadership of Jean Woods. Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the orthorectified NAIP imagery, is approximately 6 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.

The Unpublished Digital Geologic-GIS Map of Parts of Great Sand Dunes National Park and Preserve (Sangre de Cristo Mountains and part of the Dunes), Colorado is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (gsam_geology.gdb), a 10.1 ArcMap (.mxd) map document (gsam_geology.mxd), individual 10.1 layer (.lyr) files for each GIS data layer, an ancillary map information document (grsa_geology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.txt) and FAQ (.pdf) formats, and a GIS readme file (grsa_geology_gis_readme.pdf). Please read the grsa_geology_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.1 shapefile format contact Stephanie O'Meara (stephanie.omeara@colostate.edu; see contact information below). The data is also available as a 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. Google Earth software is available for free at: http://www.google.com/earth/index.html. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (gsam_geology_metadata.txt or gsam_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data projection is NAD83, UTM Zone 13N, however, for the KML/KMZ format the data is projected upon export to WGS84 Geographic, the native coordinate system used by Google Earth. The data is within the area of interest of Great Sand Dunes National Park and Preserve.

The livestock holdings estimates in this coverage are intend for use in estimating regional livestock holdings, and in producing visual displays and mapping relative amounts of agricultural livestock holdings across broad regions of the United States.

This coverage contains estimates of livestock holdings in counties in the conterminous United States as reported in the 1987 Census of Agriculture (U.S. Department of Commerce, 1989a). Livestock holdings data are reported as either a number (for example, number of milk cows), number of farms, or in thousands of dollars. Livestock holdings estimates were generated from surveys of all farms where $1,000 or more of agricultural products were sold, or normally would have been sold, during the census year.

Most of the attributes summarized represent 1987 data, but some information for the 1982 Census of Agriculture also was included.

The polygons representing county boundaries in the conterminous United States, as well as lakes, estuaries, and other nonland-area features were derived from the Digital Line Graph (DLG) files representing the 1:2,000,000-scale map in the National Atlas of the United States (1970).

Livestock Census of Agriculture Counties United States

Procedures_Used: CENSUS DATA An automated procedure was developed for processing the raw census data into ARC/INFO coverage attributes. The procedure is summarized below: 1) copy county2m coverage to coverage representing type of census data (i.e. ag_expn or ag_land), 2) run agadd.aml for each item added to the coverage, giving coverage name and attribute field number as arguments.

The agadd.aml program runs a fortran program to extract field data from the raw census data files, and then processes that raw data finally adding it as a column of attribute data to the county coverage. Other programs were developed to calculate summary statistics of the census attribute data, and to make graphics representing attribute values across the United States. COUNTY

BOUNDARIES

This series of maps was published as part of the National Atlas of the United States (U.S.Geological Survey, 1970). The maps for the conterminou United States were digitized in 15 sheets and published in the Digital Lin Graph (DLG) format as described by Domeratz and others (1983).

Each sheet was prepared by reading the DLG files of the political and water bodies layers, converting them to ARC/INFO, extracting the county boundaries and the coastline, respectively, and joining the two layers. FIPS codes were assigned to all polygons by using available sources and were checked manually.

Boundaries with adjacent sheets of the 15-sheet set were edgematched manually, arbitrarily choosing one of the sheets as the "correct" border. Edgematching operations adjusted the linework as far as was necessary so that the coverages would fit to a tolerance of 100 meters. The coverage (referred to herein as Version 1.0) was stored as 49 separate coverages (48 States and the District of Columbia) because the ARC/INFO software in use at the time could not process the entire coverage. Individual States could be joined by specifying a tolerance of 100 meters.

From time to time, adjustments were made to the State coverages to reflect changes in U.S. counties. It is believed the accuracy of these adjustments is comparable to the original linework.

For Version 2.0, all State coverages were rejoined and manually edited to produce a perfect edgematch between all States. For States on the original map sheet boundaries, this adjustment averaged less than 20 meters and in no case was more than 100 meters. The whole coverage was CLEANed to a tolerance of 20 meters, which resulted in few, if any, effect on small offshore islands. The coverage also was checked to ensure that it represented current U.S. counties or county equivalents.

The coverage in Version 1.0 stopped at the coastline. There was no attempt to depict offshore areas. This created some problems when the coverage was used to assign county codes to sampling stations located near the coast. To help in this matter, Version 2.0 includes offshore extensions of the county polygons. The (water) boundaries of many of these polygons are arbitrary.

The Canadian Great Lakes features are another new addition to Version 2. They were added to improve the utility of the coverage for visual displays Although the Canadian Great Lakes are logically represented by a single polygon, practical considerations -- the inability of some software to plo polygons with a large number of vertices -- made it necessary to separate them into four polygons. The dividing lines are located in narrow channel to minimize interference with plotting patterns. Canadian islands within the Great Lakes also were included.

All ticks were relocated to places that are easily visible on maps of the United States, to help in registering maps that may not otherwise have adequate registration information.

To expedite accessing parts o

Background and Data Limitations The Massachusetts 1830 map series represents a unique data source that depicts land cover and cultural features during the historical period of widespread land clearing for agricultural. To our knowledge, Massachusetts is the only state in the US where detailed land cover information was comprehensively mapped at such an early date. As a result, these maps provide unusual insight into land cover and cultural patterns in 19th century New England. However, as with any historical data, the limitations and appropriate uses of these data must be recognized: (1) These maps were originally developed by many different surveyors across the state, with varying levels of effort and accuracy. (2) It is apparent that original mapping did not follow consistent surveying or drafting protocols; for instance, no consistent minimum mapping unit was identified or used by different surveyors; as a result, whereas some maps depict only large forest blocks, others also depict small wooded areas, suggesting that numerous smaller woodlands may have gone unmapped in many towns. Surveyors also were apparently not consistent in what they mapped as ‘woodlands’: comparison with independently collected tax valuation data from the same time period indicates substantial lack of consistency among towns in the relative amounts of ‘woodlands’, ‘unimproved’ lands, and ‘unimproveable’ lands that were mapped as ‘woodlands’ on the 1830 maps. In some instances, the lack of consistent mapping protocols resulted in substantially different patterns of forest cover being depicted on maps from adjoining towns that may in fact have had relatively similar forest patterns or in woodlands that ‘end’ at a town boundary. (3) The degree to which these maps represent approximations of ‘primary’ woodlands (i.e., areas that were never cleared for agriculture during the historical period, but were generally logged for wood products) varies considerably from town to town, depending on whether agricultural land clearing peaked prior to, during, or substantially after 1830. (4) Despite our efforts to accurately geo-reference and digitize these maps, a variety of additional sources of error were introduced in converting the mapped information to electronic data files (see detailed methods below). Thus, we urge considerable caution in interpreting these maps. Despite these limitations, the 1830 maps present an incredible wealth of information about land cover patterns and cultural features during the early 19th century, a period that continues to exert strong influence on the natural and cultural landscapes of the region. For users without access to GIS software, the data are available for viewing at: http://harvardforest.fas.harvard.edu/research/1830instructions.html Acknowledgements Financial support for this project was provided by the BioMap Project of the Massachusetts Natural Heritage and Endangered Species Program, the National Science Foundation, and the Andrew Mellon Foundation. This project is a contribution of the Harvard Forest Long Term Ecological Research Program.