This site is part of pilot effort at the US Department of Energy (DOE) - Office of NEPA Policy and Compliance to evaluate providing IT web services as a shared service, hosted on the cloud, and using only Free and Open Source Software (FOSS). The site is an integrated component of the larger NEPAnode project but is a stand alone service. The site allows users to upload static map images with no geographic data so that they can be accurately referenced/rectified on an webmap. This site allows for the revitalizing of otherwise unusable/archived maps such as historic maps, site surveys, site plans, etc. turning them into usable geographic data which is subsequently made available as a KML file for use in Google Earth/Maps and as a Web Mapping Service (WMS) for using in web-based webmapping application such as NEPAnode or in desktop GIS software.

The Digital Geomorphic-GIS Map of Gulf Islands National Seashore (5-meter accuracy and 1-foot resolution 2006-2007 mapping), Mississippi and Florida 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 (guis_geomorphology.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 (guis_geomorphology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (guis_geomorphology.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 (guis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (guis_geomorphology.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 (guis_geomorphology_metadata_faq.pdf). Please read the guis_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 (guis_geomorphology_metadata.txt or guis_geomorphology_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 1:26,000 and United States National Map Accuracy Standards features are within (horizontally) 13.2 meters or 43.3 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).

👨‍💻 Author: Azhar Saleem

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Dataset Overview

Welcome to the Google Places Comprehensive Business Dataset! This dataset has been meticulously scraped from Google Maps and presents extensive information about businesses across several countries. Each entry in the dataset provides detailed insights into business operations, location specifics, customer interactions, and much more, making it an invaluable resource for data analysts and scientists looking to explore business trends, geographic data analysis, or consumer behaviour patterns.

Key Features

• Business Details: Includes unique identifiers, names, and contact information.
• Geolocation Data: Precise latitude and longitude for pinpointing business locations on a map.
• Operational Timings: Detailed opening and closing hours for each day of the week, allowing analysis of business activity patterns.
• Customer Engagement: Data on review counts and ratings, offering insights into customer satisfaction and business popularity.
• Additional Attributes: Links to business websites, time zone information, and country-specific details enrich the dataset for comprehensive analysis.

Potential Use Cases

This dataset is ideal for a variety of analytical projects, including: - Market Analysis: Understand business distribution and popularity across different regions. - Customer Sentiment Analysis: Explore relationships between customer ratings and business characteristics. - Temporal Trend Analysis: Analyze patterns of business activity throughout the week. - Geospatial Analysis: Integrate with mapping software to visualise business distribution or cluster businesses based on location.

Dataset Structure

The dataset contains 46 columns, providing a thorough profile for each listed business. Key columns include:

• business_id: A unique Google Places identifier for each business, ensuring distinct entries.
• phone_number: The contact number associated with the business. It provides a direct means of communication.
• name: The official name of the business as listed on Google Maps.
• full_address: The complete postal address of the business, including locality and geographic details.
• latitude: The geographic latitude coordinate of the business location, useful for mapping and spatial analysis.
• longitude: The geographic longitude coordinate of the business location.
• review_count: The total number of reviews the business has received on Google Maps.
• rating: The average user rating out of 5 for the business, reflecting customer satisfaction.
• timezone: The world timezone the business is located in, important for temporal analysis.
• website: The official website URL of the business, providing further information and contact options.
• category: The category or type of service the business provides, such as restaurant, museum, etc.
• claim_status: Indicates whether the business listing has been claimed by the owner on Google Maps.
• plus_code: A sho...

The dataset presents a list of laboratories set up in the humanities, digital humanities, and media studies within universities across the world in 1983-2018. The data are collected and organized in an interactive map designed in the digital StoryMapJS tool, creating a valuable visible representation of the laboratory concept from a geographical and historical perspective. Based on the interactive map, I analyze the history of the laboratory in the humanities within a global context from the 1980s to 2018. The dataset includes 214 laboratories.

Data collection

I identified laboratories by using different resources such as universities’ websites, articles, and research projects. Besides, I sent a questionnaire to the most relevant networks in October 2018 to identify even more labs created in (digital) humanities and media studies at universities.

Data organization

I collected data about each lab based on its website and other resources. I extracted the following data: year established, year ended (if applicable), lab’s name, university, city, country, affiliation and location (if provided), disciplines and keywords (based on labs’ statements and projects and aiming to situate a lab), selected projects (if provided), purpose (a short quotation of a lab’s statement published on its website), website, and geographical latitude and longitude. I organized all the data in chronological order according to year established in Google Sheets. Next, I used StoryMapJS, a free tool designed by the Northwestern University’s Knight Lab, to map my data.

USGS scientists acquired aerial imagery from unmanned aerial vehicles (UAV) to map sagebrush at 25 sites in the Northern Great Plains during June 2023. Data include imagery with 1.5-centimeter (cm) resolution for each 300x300 meter (m) site and imagery at 0.75 cm for each of 10 to 12, 5x5 m plots within each site, and predicted areas of sagebrush for each site from models that classify cover types in imagery. Supporting data include training data, model variables, and polygons for plots used in validation. These sagebrush maps were developed to train satellite imagery for mapping over larger areas.

Dataset of the IS BK 5 Ground Map for Forestry Site Sensing of NRW 1: 5.000. The data set gives the contents of all digitally processed large-scale ground maps, usually in scale 1: 5,000, again. For this purpose, the individual soil mapping projects (“procedures”) were integrated into a largely break-free overall package. Because the large-scale floor map was not created nationwide, the data set also shows white, uncharted areas. For these areas, medium-scale soil information can be extracted from the BK50 dataset. Each individual area is described upon retrieval of information from a GIS with regard to soil unit, simplified soil type, soil type group of the upper soil, dams, groundwater (former and current stage), soil worthy of protection, rootability, forest location characteristics, need for soil protection limescale, optimum land clearance, erodibility of the upper floor, capillary ascent of groundwater, usable field capacity, field capacity, air capacity, saturated water conductivity, leachability, cation exchange capacity and further evaluations.

Derek H. Alderman, Joshua F.J. Inwood, Ethan BottoneThe mapping behind the movement: On recovering the critical cartographies of the African American Freedom Struggle,Geoforum,Volume 120,2021,Pages 67-78,ISSN 0016-7185,https://doi.org/10.1016/j.geoforum.2021.01.022.(https://www.sciencedirect.com/science/article/pii/S0016718521000300)Abstract: Responding to recent work in critical cartographic studies and Black Geographies, the purpose of this paper is to offer a conceptual framework and a set of evocative cartographic engagements that can inform geography as it recovers the seldom discussed history of counter-mapping within the African American Freedom Struggle. Black resistant cartographies stretch what constitutes a map, the political work performed by maps, and the practices, spaces, and political-affective dimensions of mapping. We offer an extended illustration of the conventional and unconventional mapping behind USA anti-lynching campaigns of the late 19th and early 20th centuries, highlighting the knowledge production practices of the NAACP and the Tuskegee Institute’s Monroe Work, and the embodied counter-mapping of journalist/activist Ida B. Wells. Recognizing that civil rights struggles are long, always unfolding, and relationally tied over time and space, we link this look from the past to contemporary, ongoing resistant cartographical practices as scholars/activists continue to challenge racialized violence and advance transitional justice, including the noted memory-work of the Equal Justice Initiative. An understanding of African American traditions of counter-mapping is about more than simply inserting the Black experience into our dominant ideas about cartography or even resistant mapping. Black geographies has much to teach cartography and geographers about what people of color engaged in antiracist struggles define as geographic knowledge and mapping practices on their own terms—hopefully provoking a broader and more inclusive definition of the discipline itself.Keywords: African American; Anti-lynching; Black geographies; Civil rights; Counter-mapping; Critical cartography

This data set provides local LAI maps for the selected measured sites in Canada. These derived maps may also be useful for validating other LAI maps over these same sites given that the areas are protected from disturbance. The maps should be used for the given period of validity. The LAI data are suitable for use in modeling the carbon, water, energy, energy and trace gas exchange between the land surface and the atmosphere at regional scales. The data set may also be useful for monitoring changes in the land surface.The Leaf Area Index (LAI) maps are at 30-m resolution for the selected sites. LAI is defined here as half the total (all-sided) live foliage area per unit horizontal projected ground surface area. Overstory LAI corresponds to all tree foliage except for treeless areas where it corresponds to total foliage. The algorithms were developed from ground measurements and Landsat TM and ETM+ images (Fernandes et. al., 2003). A mask was developed using the Landsat ETM+/TM5 image and available land cover map to identify only those areas with land cover belonging to the sample land cover classes and with Landsat ETM+/TM5 spectral reflectance values that fell within the convex hull of the spectral reflectance values over the plots. LAI was mapped within the masked region using the Landsat ETM+/TM5 image and the developed transfer function. The final LAI map was scaled by a factor of 20 (offset 0). The LAI maps are in Tagged Image File Format (TIFF).

The Digital City Map (DCM) data represents street lines and other features shown on the City Map, which is the official street map of the City of New York. The City Map consists of 5 different sets of maps, one for each borough, totaling over 8000 individual paper maps. The DCM datasets were created in an ongoing effort to digitize official street records and bring them together with other street information to make them easily accessible to the public. The Digital City Map (DCM) is comprised of seven datasets; Digital City Map, Street Center Line, City Map Alterations, Arterial Highways and Major Streets, Street Name Changes (areas), Street Name Changes (lines), and Street Name Changes (points).

All of the Digital City Map (DCM) datasets are featured on the Streets App

All previously released versions of this data are available at BYTES of the BIG APPLE- Archive

Updates for this dataset, along with other multilayered maps on NYC Open Data, are temporarily paused while they are moved to a new mapping format. Please visit https://www.nyc.gov/site/planning/data-maps/open-data/dwn-digital-city-map.page to utilize this data in the meantime.

RTB Maps is a cloud-based electronic Atlas. We used ArGIS 10 for Desktop with Spatial Analysis Extension, ArcGIS 10 for Server on-premise, ArcGIS API for Javascript, IIS web services based on .NET, and ArcGIS Online combining data on the cloud with data and applications on our local server to develop an Atlas that brings together many of the map themes related to development of roots, tubers and banana crops. The Atlas is structured to allow our participating scientists to understand the distribution of the crops and observe the spatial distribution of many of the obstacles to production of these crops. The Atlas also includes an application to allow our partners to evaluate the importance of different factors when setting priorities for research and development. The application uses weighted overlay analysis within a multi-criteria decision analysis framework to rate the importance of factors when establishing geographic priorities for research and development.Datasets of crop distribution maps, agroecology maps, biotic and abiotic constraints to crop production, poverty maps and other demographic indicators are used as a key inputs to multi-objective criteria analysis.Further metadata/references can be found here: http://gisweb.ciat.cgiar.org/RTBmaps/DataAvailability_RTBMaps.htmlDISCLAIMER, ACKNOWLEDGMENTS AND PERMISSIONS:This service is provided by Roots, Tubers and Bananas CGIAR Research Program as a public service. Use of this service to retrieve information constitutes your awareness and agreement to the following conditions of use.This online resource displays GIS data and query tools subject to continuous updates and adjustments. The GIS data has been taken from various, mostly public, sources and is supplied in good faith.RTBMaps GIS Data Disclaimer• The data used to show the Base Maps is supplied by ESRI.• The data used to show the photos over the map is supplied by Flickr.• The data used to show the videos over the map is supplied by Youtube.• The population map is supplied to us by CIESIN, Columbia University and CIAT.• The Accessibility map is provided by Global Environment Monitoring Unit - Joint Research Centre of the European Commission. Accessibility maps are made for a specific purpose and they cannot be used as a generic dataset to represent "the accessibility" for a given study area.• Harvested area and yield for banana, cassava, potato, sweet potato and yam for the year 200, is provided by EarthSat (University of Minnesota’s Institute on the Environment-Global Landscapes initiative and McGill University’s Land Use and the Global Environment lab). Dataset from Monfreda C., Ramankutty N., and Foley J.A. 2008.• Agroecology dataset: global edapho-climatic zones for cassava based on mean growing season, temperature, number of dry season months, daily temperature range and seasonality. Dataset from CIAT (Carter et al. 1992)• Demography indicators: Total and Rural Population from Center for International Earth Science Information Network (CIESIN) and CIAT 2004.• The FGGD prevalence of stunting map is a global raster datalayer with a resolution of 5 arc-minutes. The percentage of stunted children under five years old is reported according to the lowest available sub-national administrative units: all pixels within the unit boundaries will have the same value. Data have been compiled by FAO from different sources: Demographic and Health Surveys (DHS), UNICEF MICS, WHO Global Database on Child Growth and Malnutrition, and national surveys. Data provided by FAO – GIS Unit 2007.• Poverty dataset: Global poverty headcount and absolute number of poor. Number of people living on less than $1.25 or $2.00 per day. Dataset from IFPRI and CIATTHE RTBMAPS GROUP MAKES NO WARRANTIES OR GUARANTEES, EITHER EXPRESSED OR IMPLIED AS TO THE COMPLETENESS, ACCURACY, OR CORRECTNESS OF THE DATA PORTRAYED IN THIS PRODUCT NOR ACCEPTS ANY LIABILITY, ARISING FROM ANY INCORRECT, INCOMPLETE OR MISLEADING INFORMATION CONTAINED THEREIN. ALL INFORMATION, DATA AND DATABASES ARE PROVIDED "AS IS" WITH NO WARRANTY, EXPRESSED OR IMPLIED, INCLUDING BUT NOT LIMITED TO, FITNESS FOR A PARTICULAR PURPOSE. By accessing this website and/or data contained within the databases, you hereby release the RTB group and CGCenters, its employees, agents, contractors, sponsors and suppliers from any and all responsibility and liability associated with its use. In no event shall the RTB Group or its officers or employees be liable for any damages arising in any way out of the use of the website, or use of the information contained in the databases herein including, but not limited to the RTBMaps online Atlas product.APPLICATION DEVELOPMENT:• Desktop and web development - Ernesto Giron E. (GeoSpatial Consultant) e.giron.e@gmail.com• GIS Analyst - Elizabeth Barona. (Independent Consultant) barona.elizabeth@gmail.comCollaborators:Glenn Hyman, Bernardo Creamer, Jesus David Hoyos, Diana Carolina Giraldo Soroush Parsa, Jagath Shanthalal, Herlin Rodolfo Espinosa, Carlos Navarro, Jorge Cardona and Beatriz Vanessa Herrera at CIAT, Tunrayo Alabi and Joseph Rusike from IITA, Guy Hareau, Reinhard Simon, Henry Juarez, Ulrich Kleinwechter, Greg Forbes, Adam Sparks from CIP, and David Brown and Charles Staver from Bioversity International.Please note these services may be unavailable at times due to maintenance work.Please feel free to contact us with any questions or problems you may be having with RTBMaps.

These Soil Mapping Data Packages include 1. a Soil Map dataset which includes the equivalents to Soil Project Boundaries, Soil Survey Spatial View mapping polygons with attributes from the Soil Name and Layer Files, plus + A Soil Site dataset which includes soil pit site information and detailed soil pit descriptions and any associated lab analyses, and + The Soil Data Dictionary which documents the fields and allowable codes within the data. The Soil Map geodatabase contains the 'best available' data ranging from 1:20,000 scale to 1:250,000 scale with overlapping data removed. The choice of the datasets that remain is based on connectivity to the soil attributes (soil name and layer files), map scale and survey date. (Note: the BC Soil Landscapes of Canada (BCSLC) 1:1,000,000 data has not been included in the Soil_Map or SIFT, but is available from: CANSIS. (A complete soils data package with overlapping soil survey mapping and BCSLC is available on request. Note that the soil survey data with attributes can also be viewed interactively in the [Soil Information Finder Tool](The Soil Map dataset is also available for interactive map viewing or as KMZs from the Soil Information Finder Tool website.

This is a point dataset representing City of Boise Greenbelt map locations. A Greenbelt map is a sign along the Boise River that shows a map of the user's location relative to the system. The Greenbelt is a 25-mile pathway system, primarily along the Boise River. The Greenbelt has map signage along its length that is intended to both guide and orient visitors during use. The data was created by the City of Boise. The data is updated as needed. It is current to the date of publication. For more information about the Boise Greenbelt please visit City of Boise Parks & Recreation.

A complete list of live websites using the Map Categories To Pages technology, compiled through global website indexing conducted by WebTechSurvey.

The Google Maps dataset is ideal for getting extensive information on businesses anywhere in the world. Easily filter by location, business type, and other factors to get the exact data you need. The Google Maps dataset includes all major data points: timestamp, name, category, address, description, open website, phone number, open_hours, open_hours_updated, reviews_count, rating, main_image, reviews, url, lat, lon, place_id, country, and more.

This map references the OpenStreetMap tile layer hosted by Esri. This tile layer presents a new vector basemap of OpenStreetMap (OSM) data created and hosted by Esri, now in beta release. Esri produced this vector tile basemap in ArcGIS Pro from a live replica of OSM data, hosted by Esri, that was rendered using OSM cartography. The vector tiles are updated every few weeks; refer to the OpenStreetMap tile layer for details on when it was last updated. When fully released, this vector basemap will be freely available for any user or developer to build into their web map or web mapping apps.OpenStreetMap (OSM) is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project and is excited to make this new vector basemap available available to the OSM, GIS, and Developer communities.

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 a USNVC association-level vegetation map that satisfied the standards of the USGS/NPS Vegetation Mapping Program, the alliance-level vegetation map developed by Agius was edited and refined onscreen in ArcGIS 9.1. The Agius (2003b) vegetation map was not developed following the USGS/NPS Vegetation Mapping Program standards and therefore could not be used as the final vegetation classification map. Polygons that represented vegetation were readily attributed to existing associations in the U.S. National Vegetation Classification. Polygons that represented intensive land uses were attributed with names modified from the Anderson Level II categories.. Because Saugus Iron Works National Historic Park is a small park with only 21 polygons, the mapping did not rely entirely on aerial photograph interpretation, but also incorporated lines sketched onto a hard-copy map on site.

Using ArcGIS 9.1, polygon boundaries were drawn onscreen based on the plot data and additional field observations. Each polygon was attributed with the name of an USNVC association or an Anderson Level II (modified) land use/land cover map class based on plot data, field observations, aerial photography signatures, and topographic maps. The shapefile was projected in Universal Transverse Mercator Zone 19 North, North American Datum 1983, meters, in ArcGIS 9.1.

This dataset provides gridded average annual wetland salinity concentrations in practical salinity units (PSU) at 30-meter resolution within 24 coastal estuary sites in the United States predicted for 2020. Salinity in estuaries can serve as a proxy for sulfate concentration, which can inhibit methanogenesis. Data were derived from a hybrid approach to mapping salinity as a continuous variable using a combination of physical watershed and stream characteristics, optical remote sensing based on vegetation characteristics, and climate variables. Data are provided in cloud-optimized GeoTIFF format covering 33 Hydrologic Unit Code 8-digit (HUC8) watersheds to the extent of palustrine and estuarine wetlands as defined by NOAA's 2016 Coastal Change Analysis Program (C-CAP) Coastal Land Cover layer. Additionally, model outputs are provided in comma separated values (CSV) files, and code scripts are provided in a compressed (*.zip) file.

High Precision 3D Map Market Outlook

The global high precision 3D map market size was valued at approximately USD 2.5 billion in 2023 and is expected to reach around USD 12.3 billion by 2032, growing at an impressive CAGR of 19.6% during the forecast period. The growth of this market is driven by the increasing demand for advanced navigation systems, autonomous driving technology, and the rising integration of augmented reality in various industries.

The growth factors contributing to the expansion of the high precision 3D map market are numerous and diverse. One of the key drivers is the rapid advancement in autonomous vehicle technology. As the development and adoption of self-driving cars continue to accelerate, the need for highly accurate and detailed 3D maps becomes paramount. These maps play a critical role in enabling autonomous vehicles to navigate efficiently and safely. Additionally, the increasing focus on smart city initiatives is further propelling the demand for high precision 3D maps. Governments and urban planners are leveraging these maps to optimize infrastructure development, improve traffic management, and enhance emergency response systems.

Another significant growth factor is the rising popularity of augmented reality (AR) and virtual reality (VR) applications. High precision 3D maps are essential for creating realistic and immersive AR/VR experiences across various industries, including gaming, entertainment, healthcare, and education. The gaming industry, in particular, is witnessing a surge in demand for 3D maps to create lifelike environments and interactive gaming experiences. Furthermore, the healthcare sector is utilizing 3D mapping technology for advanced medical imaging, surgical planning, and augmented reality-based medical training, thereby driving the market growth.

The construction and engineering sector is also a major contributor to the growth of the high precision 3D map market. The use of 3D mapping technology in construction projects enhances project planning, site analysis, and monitoring. Construction companies are increasingly adopting 3D maps to create accurate and detailed representations of construction sites, which help in minimizing errors, reducing costs, and improving overall project efficiency. Additionally, the integration of 3D mapping technology with Building Information Modeling (BIM) systems is further boosting its adoption in the construction industry.

In the realm of intelligence and defense, 3D Mapping and Modeling in the Intelligence and Defense Communities have become indispensable tools. These technologies provide military and intelligence agencies with detailed and accurate representations of terrains and urban environments, which are crucial for mission planning and situational awareness. By leveraging 3D mapping, defense personnel can simulate various scenarios, assess potential threats, and strategize effectively. The integration of 3D mapping with real-time data feeds enhances decision-making processes, enabling rapid responses to dynamic situations. Furthermore, these technologies support reconnaissance missions by providing high-resolution imagery and topographical data, which are essential for identifying strategic points and assessing enemy movements. As the geopolitical landscape continues to evolve, the demand for advanced 3D mapping solutions in the intelligence and defense sectors is expected to grow, driving further innovations and applications.

On a regional level, North America is expected to dominate the high precision 3D map market during the forecast period. The presence of major technology companies, coupled with significant investments in autonomous vehicle development and smart city projects, is driving the market growth in this region. Europe is also witnessing substantial growth due to the increasing adoption of 3D mapping technology in automotive and construction sectors. The Asia Pacific region is projected to exhibit the highest growth rate, driven by rapid urbanization, infrastructure development, and the rising demand for advanced navigation systems in countries like China and India.

Component Analysis

The high precision 3D map market can be segmented by component into hardware, software, and services. Each of these components plays a critical role in the creation, management, and utilization of high precision 3D maps. The hardware segment includes devices and equipment used in the collection and proces

This map data product delivers high-precision, real-time, and historical GPS event records across North America. It is designed for organizations that require granular spatial data for applications such as mapping, movement tracking, retail analytics, and infrastructure planning.

Data Contents: Latitude & longitude coordinates Timestamp (epoch & human-readable date) Device ID (MAID: IDFA/GAID) Country code (ISO3) Horizontal accuracy (85% fill rate) Optional metadata: IP address, mobile carrier, device model

Access & Delivery: Available via API with custom polygon queries (up to 10,000 tiles) for targeted location insights. Data can be delivered hourly or daily in JSON, CSV, or Parquet formats, through AWS S3, Google Cloud Storage, or direct API access. Historical coverage extends back to September 2024, with 95% of events delivered within 3 days for near-real-time analysis.

Compliance & Flexibility: GDPR and CCPA compliant Credit-based query pricing for scalability Custom schema mapping and folder structure available

Applications: Map creation and enhancement POI visitation analytics Urban mobility and transit modeling Retail site selection and catchment area mapping Real estate and zoning analysis Geospatial risk and environmental planning

Urban heat islands are small areas where temperatures are unnaturally high - usually due to dense buildings, expansive hard surfaces, or a lack of tree cover or greenspace. People living in these communities are exposed to more dangerous conditions, especially as daytime high and nighttime low temperatures increase over time. NOAA Climate Program Office and CAPA Strategies have partnered with cities around the United States to map urban heat islands. The NOAA Visualization Lab, part of the NOAA Satellite and Information Service, has made the original heat mapping data available as feature services.