This dataset provides resources for identifying flight lines of interest for the MODIS/ASTER Airborne Simulator (MASTER) instrument based on spatial and temporal criteria. MASTER first flew in 1998 and has ongoing deployments as a Facility Instrument in the NASA Airborne Science Program (ASP). MASTER is a joint project involving the Airborne Sensor Facility (ASF) at the Ames Research Center, the Jet Propulsion Laboratory (JPL), and the Earth Resources Observation and Science Center (EROS). The primary goal of these airborne campaigns is to demonstrate important science and applications research that is uniquely enabled by the full suite of MASTER thermal infrared bands as well as the contiguous spectroscopic measurements of the AVIRIS (also flown in similar campaigns), or combinations of measurements from both instruments. This dataset includes a table of flight lines with dates, bounding coordinates, site names, investigators involved, flight attributes, and associated campaigns for the MASTER Facility Instrument Collection. A shapefile containing flights for all years, a GeoJSON version of the shapefile, and separate KMZ files for each year allow users to visualize flight line locations using GIS software.

The U.S. Fish and Wildlife Service Corporate Master Table (CMT) is the official source of Service organization codes and related information. Information in the CMT includes, but is not limited to, organization codes, organization names, Federal Budget Management System (FBMS), cost center codes, fire unit identifiers, program names, mailing and physical/shipping addresses, telephone and fax numbers as well as latitude and longitude coordinates. The CMT enables all Service automated systems to utilize a corporate data set of known quality, eliminating the workload required to maintain each system's data set, and thereby facilitating data sharing. Other customers for the CMT are Service personnel who maintain directories, communicate with Congress and with the Public, maintain World Wide Web sites, etc. These spatial data were created using the information in the CMT. The CMT contains location information on all the offices within the Service that have an organization code. Unstaffed offices and some other facilities may not be included. The latitude and longitude points used are usually the location of the main administrative site. The latitude and longitude data is not completely verified but is the best we have at this time. This data set is intended to give an overview of where USFWS has stations across the United States and Territories, including locations outside the 50 states. It is not intended to be the exact location of every USFWS office. The CMT is primarily used for accounting purposes and therefore one location in the CMT can represent many different offices. Some points are duplicates where a station, most usually an Ecological Field Office, may be associated with more than one USFWS program. This data is updated from an internal authoritative source every night at 2:30am EST.For a direct link to the official Enterprise Geospatial dataset and metadata: https://ecos.fws.gov/ServCat/Reference/Profile/60076.Dataset contact: fwsgis@fws.gov

Addresses of buildings, businesses, parks, and open spaces in the City of Cambridge. This dataset contains the complete list of addresses in Cambridge, along with each address's geospatial coordinates and relevant administrative boundaries (e.g., Census block, polling district, public safety area). The dataset does not include individual apartment units.The dataset is sourced from Cambridge's master address and GIS databases. Shapefiles for this data and other Cambridge geospatial data can be found on on the City's GIS Data Dictionary at https://www.cambridgema.gov/GIS/gisdatadictionary

The Kansas Master Ground-water Well Inventory (MWI) is a central repository that imports and links together the State's primary ground-water well data sets- KDHE's WWC5, KDA-DWR's WIMAS, and KGS' WIZARD into a single, online source. The most "accurate" of the common source fields are used to represent the well sites, for example- GPS coordinates if available are used over other methods to locate a well. The MWI maintains the primary identification tags to allow specific well records to be linked back to the original data sources.This data is compiled by the Kansas Geological Survey. For more information, please see the Groundwater Master Well Inventory page.

The Digital Bedrock Geologic-GIS Map of Minuteman National Historical Site and Vicinity, Massachusetts 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 (mima_bedrock_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 and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (mima_bedrock_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.) this file (mima_geology.gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (mima_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 (mima_bedrock_geology_metadata_faq.pdf). Please read the mima_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: http://www.google.com/earth/index.html. 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: Boston College 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 (mima_bedrock_geology_metadata.txt or mima_bedrock_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) 25.4 meters or 83.3 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 global geospatial analytics market is predicted to expand significantly, with a projected CAGR of 11.28% from 2025 to 2033. Valued at 89.23 billion USD in 2025, the market is expected to reach new heights during the forecast period. Key drivers fueling this growth include increasing adoption of GIS (Geographic Information Systems) and GPS (Global Positioning Systems), rising demand for location-based services, and growing awareness of the benefits of geospatial data in decision-making. Additionally, advancements in cloud computing, artificial intelligence, and machine learning further contribute to the market's expansion. Key segments in the geospatial analytics market include services, types, technologies, and regions. Consulting, integration and deployment, support and maintenance are prominent services offered in the market. Surface and field analytics, network and location analytics, geovisualization, and other types are also significant segments. Remote sensing GIS GPS, other technologies, and their applications across various regions, including North America, Europe, Asia Pacific, Middle East & Africa, and South America, shape the market dynamics. Recent developments include: Sept 2022 Sanborn Map Company Inc., a provider of geospatial solutions for government and commercial clients, has acquired Applied Geographics, Inc., which helped numerous organisations in finding the most effective GIS, location intelligence, and geospatial solutions., January 2022 With the help of integrated and improved data, ideal site analysis and path planning, and customized customer experiences, Blueprint Technologies and Precisely have announced a partnership to help businesses gain a competitive edge., Geospatial analytics is being used by telecom companies like T-Mobile to optimise coverage and quality of service while planning deployments. While organising service deployments and coverage, telecommunications providers must consider a wide range of criteria. They must take into account the varying usage patterns, service demands, and the dynamic nature of the areas they serve., According to industry analysts, the abundance of geospatial data accessible is outpacing people's capacity to comprehend it as government and business deploy more satellites, drones, and sensors than ever before. Artificial intelligence, according to Mark Munsell, Deputy Director for Data and Digital Innovation at the National Geospatial-Intelligence Agency., Geospatial intelligence experts Orbital Insight and Carahsoft Technologies Corp. have joined forces. Carahsoft will act as Orbital Insight's Master Government Aggregator in accordance with the agreement. Through Carahsoft's reseller partners, Information Technology Enterprise Solutions - Software 2 (ITES-SW2), NASA Solutions for Enterprise-Wide Procurement (SEWP) V, National Association of State Procurement Officials (NASPO), ValuePoint, National Cooperative Purchasing Alliance (NCPA), and OMNIA Partners contracts, the company's AI-powered geospatial data analytics are now accessible to the public sector.. Potential restraints include: High Initial Investment Cost.

This feature layer replaces the Master_RC_Geo_July_2022 feature layer. The original name does not change to allow future data updates without generating a new feature layer. This feature layer represents the changes to the Red Cross corporate geography for Fiscal Year 2025. The data was updated in January 2025 based on the January 2025 update from the original source files from Humanitarian Services, Operations. This Feature Layer supersedes all previous versions of the Red Cross Master Geography and should be used to update any Web Maps using a previous version.

The TIGER/Line shapefiles and related database files (.dbf) 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 shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The All Roads Shapefile includes all features within the MTDB Super Class 'Road/Path Features 'distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in MTDB that begins with 'S '. This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, and stairways.

Formation transfrontalière UniGR: Erasmus Mundus Master in Language and Communication Technologies (MA) - Source: UniGR

The TIGER/Line shapefiles and related database files (.dbf) 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 shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The All Roads Shapefile includes all features within the MTDB Super Class 'Road/Path Features 'distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in MTDB that begins with 'S '. This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, and stairways.

Street intersections in the City of Cambridge. This dataset contains the complete list of intersections in Cambridge, along with each intersection's geospatial coordinates and relevant administrative boundaries (e.g., Census block, polling district, public safety area). The dataset is sourced from Cambridge's GIS databases. Shapefiles for this data and other Cambridge geospatial data can be found on on the City's GIS Data Dictionary at https://www.cambridgema.gov/GIS/gisdatadictionary

The TIGER/Line shapefiles and related database files (.dbf) 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 shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The All Roads Shapefile includes all features within the MTDB Super Class 'Road/Path Features 'distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in MTDB that begins with 'S '. This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, and stairways.

The TIGER/Line shapefiles and related database files (.dbf) 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 shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The All Roads Shapefile includes all features within the MTDB Super Class 'Road/Path Features 'distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in MTDB that begins with 'S '. This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, and stairways.

Updated Continually

The TIGER/Line shapefiles and related database files (.dbf) 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 shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The All Roads Shapefile includes all features within the MTDB Super Class 'Road/Path Features 'distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in MTDB that begins with 'S '. This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, and stairways.

Count of high school graduates for each public school in Alaska. Data covers the School Year 2013 to the present. Each year's count includes students graduating at any point during the school year (July 1 to June 30).Source: Alaska Department of Education & Early Development

This data has been visualized in a Geographic Information Systems (GIS) format and is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: Alaska Department of Education & Early Development Data Center.

The TIGER/Line shapefiles and related database files (.dbf) 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 shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The All Roads Shapefile includes all features within the MTDB Super Class 'Road/Path Features 'distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in MTDB that begins with 'S '. This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, and stairways.

These geospatial files are the essential components for the Geologic Map of the Stibnite Mining Area in Valley County, Idaho, which was published by the Idaho Geological Survey in 2022. Three main file types are in this dataset: geographic, geologic, and mining. Geographic files are map extent, lidar base, topographic contours, labels for contours, waterways, and roads. Geologic files are geologic map units, faults, structural lines meaning axial traces, structural points like bedding strike and dip locations, cross section lines, and drill core sample locations. Lastly, mining files are disturbed ground features including open pit polygons or outlines, and general mining features such as the location of an adit. File formats are shape, layer, or raster. Of the 14 shapefiles, 7 have layer files that provide pre-set symbolization for use in ESRI ArcMap that match up with the Geologic Map of the Stibnite Mining Area in Valley County, Idaho. The lidar data have two similar, but distinct, raster format types (ESRI GRID and TIFF) intended to increase end user accessibility. This dataset is a compilation of both legacy data (from Smitherman’s 1985 masters thesis published in 1988, Midas Gold Corporation employees, the Geologic Map of the Stibnite Quadrangle (Stewart and others, 2016) and Reed S. Lewis of the Idaho Geological Survey) and new data from 2013, 2015, and 2016 field work by Niki E. Wintzer.

Small Area plan polygons as determined by the planning department of the City of Alexandria. Small Area Plans are the 18 geographic planning areas within the City that together create the City Master Plan. These master plans are guiding documents that provide community-based long-range planning and analysis regarding the physical development and appearance of neighborhoods across the City. Overlay plans are Supplemental plans and amendments to existing Small Area Plans that provide greater standards or regulations. Properties located within the boundaries are subject to the requirements and regulations per the overlay plan in addition to other City standards and policies. If the overlay plan is silent to or does not address a specific issue or topic, the underlying Small Area Plan applies.