This group of layers was developed by the Balmoral Group and contains the critical infrastructure layers as defined in 380.093(2)(a) Florida Statutes. The layers were sourced from various public State of Florida and Federal Sources. Critical infrastructure includes wastewater treatment facilities and lift stations, stormwater treatment facilities and pump stations, drinking water facilities, water utility conveyance systems, electric production and supply facilities, solid and hazardous waste facilities, military installations, communications facilities, and disaster debris management sites. Typically, the data are utilized in various vulnerability assessments in evaluating the exposure and sensitivity from combined events of sea level rise, precipitation, major storms, and flooding. The data will also be used in efforts to complete a comprehensive statewide assessment for the State of Florida.

The GIS data maintained by HPPM includes information on buildings and grounds related to Harvard University. Our "standard" base layers are available to Harvard affiliates and their service providers (for example, architects) working on Harvard projects in AutoCAD DWG, ESRI SHP or File Geodatabase format. Additional datasets are sometimes available by special arrangement. http://home.hppm.harvard.edu/pages/gis-data-layers

This data depicts infrastructure locations in Alaska as digitized primarily from 1:24,000, 1:63,360, and 1:250,000 USGS quadrangles.

The source document that represented the newest information and best geographic location was used to capture the data. All infrastructure from the primary source document was digitized and then supplemented with the information from other source documents for additional or updated infrastructure or attributes.

Infrastructure projects are compiled from the capital improvement plans for Transportation, Airport & Ferry, Surface Water Management, and Sewers. Programs (chipseal, paving, guardrail) are not displayed. Project location, scope, and schedule are subject to change. Please read metadata for additional information(https://matterhorn.piercecountywa.gov/GISmetadata/pdbpubw_improvement_project_points.html). Any data download constitutes acceptance of the Terms of Use (https://matterhorn.piercecountywa.gov/Disclaimer/PierceCountyGISDataTermsofUse.pdf).

The U.S. Geological Survey (USGS) has compiled a geodatabase containing mineral-related geospatial data for the People's Republic of China. The data can be used in analyses of the extractive fuel and nonfuel mineral industries and related economic and physical infrastructure integral for the successful operation of the mineral industries within the area of study as well as the movement of mineral products across domestic and global markets. This geodatabase reflects the USGS ongoing commitment to its mission of understanding the nature and distribution of global mineral commodity supply chains by updating and publishing the georeferenced locations of mineral commodity production and processing facilities, mineral exploration and development sites, and mineral commodity exporting ports for the countries in the area of study. The geodatabase contains data feature classes from USGS, foreign governmental, and open-source sources as follows: (1) mineral production and processing facili ...

This web map depicts GIS data for known Stormwater Infrastructure in the City of SeaTac, Washington. The information is based on the best available knowledge collected from construction as-builts and field inspections, with a focus on mapping features in the public right-of-way. The stormwater infrastructure contains the following datasets: discharge points, catch basins and manholes, pipes and ditches, misc structures, water quality facilities points and polygons, and access risers. The data is being continually updated as newer information becomes available.Incorporated in February 1990, the City of SeaTac is located in the Pacific Northwest, approximately midway between the cities of Seattle and Tacoma in the State of Washington. SeaTac is a vibrant community, economically strong, environmentally sensitive, and people-oriented. The City boundaries surround the Seattle-Tacoma International Airport, (approximately 3 square miles in area) which is owned and operated by the Port of Seattle. For additional information regarding the City of SeaTac, its people, or services, please visit https://www.seatacwa.gov. For additional information regarding City GIS data or maps, please visit https://www.seatacwa.gov/our-city/maps-and-gis.

The U.S. Geological Survey (USGS) has compiled a geodatabase containing mineral-related geospatial data for 10 countries of interest in Southwest Asia (area of study): Afghanistan, Cambodia, Laos, India, Indonesia, Iran, Nepal, North Korea, Pakistan, and Thailand. The data can be used in analyses of the extractive fuel and nonfuel mineral industries and related economic and physical infrastructure integral for the successful operation of the mineral industries within the area of study as well as the movement of mineral products across domestic and global markets. This geodatabase reflects the USGS ongoing commitment to its mission of understanding the nature and distribution of global mineral commodity supply chains by updating and publishing the georeferenced locations of mineral commodity production and processing facilities, mineral exploration and development sites, and mineral commodity exporting ports for the countries in the area of study. The geodatabase contains data feat ...

Instant App for viewing this individual sector, direct link at bottom right of system description page or on our NJSP/SEOC/GIS Hub Page at: https://seoc-njoem.hub.arcgis.com/Within Instant App for viewing ALL 10 Public Critical Infrastructure GIS Data - Sectors - direct link at:https://njoem.maps.arcgis.com/apps/instant/portfolio/index.html?appid=9b0f325f559a499685aa5c92a2f2696a

This geodatabase reflects the U.S. Geological Survey’s (USGS) ongoing commitment to its mission of understanding the nature and distribution of global mineral commodity supply chains by updating and publishing the georeferenced locations of mineral commodity production and processing facilities, mineral exploration and development sites, and mineral commodity exporting ports in Africa. The geodatabase and geospatial data layers serve to create a new geographic information product in the form of a geospatial portable document format (PDF) map. The geodatabase contains data layers from USGS, foreign governmental, and open-source sources as follows: (1) mineral production and processing facilities, (2) mineral exploration and development sites, (3) mineral occurrence sites and deposits, (4) undiscovered mineral resource tracts for Gabon and Mauritania, (5) undiscovered mineral resource tracts for potash, platinum-group elements, and copper, (6) coal occurrence areas, (7) electric po ...

Equipment GIS Mapping for Facilities Market Outlook

According to our latest research, the Global Equipment GIS Mapping for Facilities market size was valued at $1.6 billion in 2024 and is projected to reach $4.3 billion by 2033, expanding at a CAGR of 11.5% during 2024–2033. The primary factor fueling this robust growth is the increasing demand for advanced geospatial analytics across facility management sectors, driven by the need for real-time asset tracking, efficient resource allocation, and predictive maintenance capabilities. Organizations across industries are realizing the value of integrating Geographic Information Systems (GIS) with facility equipment mapping to optimize operational workflows, reduce downtime, and enhance decision-making. This market is also witnessing accelerated adoption due to digital transformation initiatives and the growing reliance on data-driven insights for managing complex facility infrastructures globally.

Regional Outlook

North America currently holds the largest share of the Equipment GIS Mapping for Facilities market, accounting for approximately 38% of global revenue in 2024. The region’s dominance is attributed to its mature technology landscape, widespread adoption of advanced facility management solutions, and strong presence of leading GIS software vendors. Regulatory mandates for safety, sustainability, and asset transparency in sectors such as healthcare, education, and utilities further amplify the demand for GIS mapping technologies. Additionally, substantial investments in smart building solutions and the integration of IoT with GIS platforms have positioned North America as a pioneer in this space. The region benefits from robust IT infrastructure, high digital literacy, and supportive public policies, all of which contribute to rapid market expansion and innovation.

The Asia Pacific region is expected to witness the fastest growth in the Equipment GIS Mapping for Facilities market, with a projected CAGR of 14.2% from 2024 to 2033. This growth is primarily driven by rapid urbanization, infrastructure modernization projects, and increased government focus on smart city initiatives. Countries such as China, India, Japan, and South Korea are investing heavily in digital infrastructure and public utilities, driving the adoption of GIS-based facility mapping solutions. The proliferation of cloud-based GIS platforms and mobile mapping applications is making these technologies more accessible to a broader range of end-users. Furthermore, rising awareness of the operational efficiencies and cost savings offered by GIS mapping is encouraging both public and private sector organizations to invest in these solutions, fueling robust regional growth.

Emerging economies in Latin America and the Middle East & Africa are gradually embracing Equipment GIS Mapping for Facilities, albeit at a slower pace due to infrastructural and economic constraints. Adoption in these regions is often hampered by limited access to advanced IT infrastructure, budgetary limitations, and a shortage of skilled GIS professionals. However, localized demand is increasing, particularly in sectors such as utilities, transportation, and government, where the need for efficient asset management and infrastructure planning is critical. Policy reforms, international aid, and public-private partnerships are beginning to address these challenges, creating new opportunities for market penetration. As digital transformation accelerates and awareness of GIS benefits grows, these regions are expected to contribute more significantly to the global market in the coming years.

Report Scope

The booming GIS Services market, projected to reach $27.8 billion by 2033 with an 8% CAGR, is transforming industries. Learn about key trends, applications (environmental, utilities, infrastructure), and leading companies shaping this dynamic sector. Explore regional market shares and growth forecasts for North America, Europe, and Asia Pacific.

Advancing Research on Nutrition and Agriculture (AReNA) is a 6-year, multi-country project in South Asia and sub-Saharan Africa funded by the Bill and Melinda Gates Foundation, being implemented from 2015 through 2020. The objective of AReNA is to close important knowledge gaps on the links between nutrition and agriculture, with a particular focus on conducting policy-relevant research at scale and crowding in more research on this issue by creating data sets and analytical tools that can benefit the broader research community. Much of the research on agriculture and nutrition is hindered by a lack of data, and many of the datasets that do contain both agriculture and nutrition information are often small in size and geographic scope. AReNA team constructed a large multi-level, multi-country dataset combining nutrition and nutrition-relevant information at the individual and household level from the Demographic and Health Surveys (DHS) with a wide variety of geo-referenced data on agricultural production, agroecology, climate, demography, and infrastructure (GIS data). This dataset includes 60 countries, 184 DHS, and 122,473 clusters. Over one thousand geospatial variables are linked with DHS. The entire dataset is organized into 13 individual files: DHS_distance, DHS_livestock, DHS_main, DHS_malaria, DHS NDVI, DHS_nightlight, DHS_pasture and climate (mean), DHS_rainfall, DHS_soil, DHS_SPAM, DHS_suit, DHS_temperature, and DHS_traveltime.

Discover the booming Geographic Information System (GIS) Services market! Explore its $15 Billion (2025 est.) size, 8% CAGR, key drivers, trends, and leading companies. Learn about regional market share and future growth projections in this in-depth analysis.

Tool and data set of road networks for 80 of the most populated urban areas in the world. The data consist of a graph edge list for each city and two corresponding GIS shapefiles (i.e., links and nodes).Make your own data with our ArcGIS, QGIS, and python tools available at: http://csun.uic.edu/codes/GISF2E.htmlPlease cite: Karduni,A., Kermanshah, A., and Derrible, S., 2016, "A protocol to convert spatial polyline data to network formats and applications to world urban road networks", Scientific Data, 3:160046, Available at http://www.nature.com/articles/sdata201646

This dataset provides detailed geospatial and operational information for utility assets such as poles, towers, underground cables, manholes, and pad-mounted equipment. It enables precise mapping, network connectivity analysis, and outage management planning for utility infrastructure, supporting both operational efficiency and strategic decision-making.

Building footprints from the 2011 LiDAR project. Includes outlines of buildings with an area of 40 square feet or greater. Automated classification of buildings performed using TerraScan. Manual cleanup of building classification was then carried out within point cloud data using TerraScan or LP360. Building footprints were digitized automatically using the LP360 building extraction feature. Footprints cleaned up manually using ArcGIS.This dataset is static and has not been edited since its original delivery.

This Mat-Su Borough road centerlines dataset contains assigned official road names, address ranges, and cartographic classifications. This data is used to create the MSAG table for the Enhanced 9-1-1 program and is suitable for geo-coding purposes. Note: Cartographic classification of roads now includes a classification of "NOT CONST'D" which denotes roads that have been platted but not yet constructed. Original data was aggregated by a consultant (McLane Consulting of Soldotna, AK) as a part of the original addressing/911 project. Centerlines were interpolated from existing digital CAD drawings of property and ROW lines. Consultant (McClane) then did field work to append the centerline file to include additional road segments not represented as part of ROW within the property maps. Additional segments were input using GPS and "heads up" 85 digitizing methods. Each was adjusted to fit with the existing data. Data was originally stored in MapInfo (MIF) format and later converted to ESRI shapefile (SHP) format. Additional data related to the state highway system was collected using GPS technology between 1997 and 1999 by the Alaska Department of Transportation. This data was used to supplement the Borough data set for portions of the Parks Highway, Glenn Highway, Old Glenn Highway, Petersville Road, Denali Highway, and Lake Louise Road. Replacement of those street segments based upon property map interpolation but now available within the AK-DOT GPS collection is planned for Summer 2001. Data is maintained in an ongoing basis, primarily taken from subdivision plats, right-of-way plats, or other similar documentation of road existence. Data is input based on road centerlines as shown on subdivision plats and using "heads up" digitizing from aerial imagery.

Based on continuous geographic information, building spatial information and building register attribute information of the architectural administration system (Seumteo) are integrated into a building unit, and when called with RestAPI, spatial information data such as building integrated information based on space (land) are returned in json/xml format.

The Green Infrastructure Focus Map is a new tool and evidence base to help London’s decision-makers identify where green infrastructure improvements and investments might be best targeted, and what kind of interventions might be most useful for the needs of a specific area. The Green Infrastructure Focus Map can help: identify where there is more need or less need for green infrastructure interventions describe which specific environmental or social issues have the greatest need for intervention in a particular location highlight other issues that green infrastructure can’t necessarily help with, but that are useful context for decision making (e.g. income deprivation) Please contact environment@london.gov.uk with any queries or feedback. Data and analysis from GLA GIS Team form a basis for the policy and investment decisions facing the Mayor of London and the GLA group. GLA Intelligence uses a wide range of information and data sourced from third party suppliers within its analysis and reports. GLA Intelligence cannot be held responsible for the accuracy or timeliness of this information and data. The GLA will not be liable for any losses suffered or liabilities incurred by a party as a result of that party relying in any way on the information contained in this report.

The Australian Antarctic Data Centre's Mawson Station GIS data were originally mapped from March 1996 aerial photography. Refer to the metadata record 'Mawson Station GIS Dataset'. Since then various features have been added to this data as structures have been removed, moved or established. Some of these features have been surveyed. These surveys have metadata records from which the report describing the survey can be downloaded. However, other features have been 'eyed in' as more accurate data were not available. The eyeing in has been done based on advice from Australian Antarctic Division staff and using as a guide sources such as an aerial photograph, an Engineering plan, a map or a sketch. GPS data or measurements using a measuring tape may also have been used.

The data are included in the data available for download from a Related URL below. The data conform to the SCAR Feature Catalogue which includes data quality information. See a Related URL below. Data described by this metadata record has Dataset_id = 119. Each feature has a Qinfo number which, when entered at the 'Search datasets and quality' tab, provides data quality information for the feature.