Geospatial Solutions Market size was valued at USD 282.75 Billion in 2024 and is projected to reach USD 650.14 Billion by 2032, growing at a CAGR of 12.10% during the forecast period 2026-2032.

Geospatial Solutions Market: Definition/ Overview

Geospatial solutions are applications and technologies that use spatial data to address geography, location, and Earth's surface problems. They use tools like GIS, remote sensing, GPS, satellite imagery analysis, and spatial modelling. These solutions enable informed decision-making, resource allocation optimization, asset management, environmental monitoring, infrastructure planning, and addressing challenges in sectors like urban planning, agriculture, transportation, disaster management, and natural resource management. They empower users to harness spatial information for better understanding and decision-making in various contexts.

Geospatial solutions are technologies and methodologies used to analyze and visualize spatial data, ranging from urban planning to agriculture. They use GIS, remote sensing, and GNSS to gather, process, and interpret data. These solutions help users make informed decisions, solve complex problems, optimize resource allocation, and enhance situational awareness. They are crucial in addressing challenges and unlocking opportunities in today's interconnected world, such as mapping land use patterns, monitoring ecosystem changes, and real-time asset tracking.

Links to recordings of the Integrated Services Program and 9-1-1 & Geospatial Services Bureau webinar series, including NG9-1-1 GIS topics such as: data preparation; data provisioning and maintenance; boundary best practices; and extract, transform, and load (ETL). Offerings include:Topic: Virginia Next Generation 9-1-1 Dashboard and Resources Update Description: Virginia recently updated the NG9-1-1 Dashboard with some new tabs and information sources and continues to develop new resources to assist the GIS data work. This webinar provides an overview of changes, a demonstration of new functionality, and a guide to finding and using new resources that will benefit Virginia public safety and GIS personnel with roles in their NG9-1-1 projects. Wednesday 16 June 2021. Recording available at: https://vimeo.com/566133775Topic: Emergency Service Boundary GIS Data Layers and Functions in your NG9-1-1 PSAP Description: Law, Fire, and Emergency Medical Service (EMS) Emergency Service Boundary (ESB) polygons are required elements of the NENA NG9-1-1 GIS data model stack that indicate which agency is responsible for primary response. While this requirement must be met in your Virginia NG9-1-1 deployment with AT&T and Intrado, there are quite a few ways you could choose to implement these polygons. PSAPs and their GIS support must work together to understand how this information will come into a NG9-1-1 i3 PSAP and how it will replace traditional ESN information in order to make good choices while implementing these layers. This webinar discusses:the function of ESNs in your legacy 9-1-1 environment, the role of ESBs in NG9-1-1, and how ESB information appears in your NG9-1-1 PSAP. Wednesday, 22 July 2020. Recording available at: https://vimeo.com/441073056#t=360sTopic: "The GIS Folks Handle That": What PSAP Professionals Need to Know about the GIS Project Phase of Next Generation 9-1-1 DeploymentDescription: Next Generation 9-1-1 (NG9-1-1) brings together the worlds of emergency communication and spatial data and mapping. While it may be tempting for PSAPs to outsource cares and concerns about road centerlines and GIS data provisioning to 'the GIS folks', GIS staff are crucial to the future of emergency call routing and location validation. Data required by NG9-1-1 usually builds on data that GIS staff already know and use for other purposes, so the transition requires them to learn more about PSAP operations and uses of core data. The goal of this webinar is to help the PSAP and GIS worlds come together by explaining the role of the GIS Project in the Virginia NG9-1-1 Deployment Steps, exploring how GIS professionals view NG9-1-1 deployment as a project, and fostering a mutual understanding of how GIS will drive NG9-1-1. 29 January 2020. Recording available at: https://vimeo.com/showcase/9791882/video/761225474Topic: Getting Your GIS Data from Here to There: Processes and Best Practices for Extract, Transform and Load (ETL) Description: During the fall of 2019, VITA-ISP staff delivered workshops on "Tools and Techniques for Managing the Growing Role of GIS in Enterprise Software." This session presents information from the workshops related to the process of extracting, transforming, and loading data (ETL), best practices for ETL, and methods for data schema comparison and field mapping as a webinar. These techniques and skills assist GIS staff with their growing role in Next Generation 9-1-1 but also apply to many other projects involving the integration and maintenance of GIS data. 19 February 2020. Recording available at: https://vimeo.com/showcase/9791882/video/761225007Topic: NG9-1-1 GIS Data Provisioning and MaintenanceDescription: VITA ISP pleased to announce an upcoming webinar about the NG9-1-1 GIS Data Provisioning and Maintenance document provided by Judy Doldorf, GISP with the Fairfax County Department of Information Technology and RAC member. This document was developed by members of the NG9-1-1 GIS workgroup within the VITA Regional Advisory Council (RAC) and is intended to provide guidance to local GIS and PSAP authorities on the GIS datasets and associated GIS to MSAG/ALI validation and synchronization required for NG9-1-1 services. The document also provides guidance on geospatial call routing readiness and the short- and long-term GIS data maintenance workflow procedures. In addition, some perspective and insight from the Fairfax County experience in GIS data preparation for the AT&T and West solution will be discussed in this webinar. 31 July 2019. Recording available at: https://vimeo.com/showcase/9791882/video/761224774Topic: NG9-1-1 Deployment DashboardDescription: I invite you to join us for a webinar that will provide an overview of our NG9-1-1 Deployment Dashboard and information about other online ISP resources. The ISP website has been long criticized for being difficult to use and find information. The addition of the Dashboard and other changes to the website are our attempt to address some of these concerns and provide an easier way to find information especially as we undertake NG9-1-1 deployment. The Dashboard includes a status map of all Virginia PSAPs as it relates to the deployment of NG9-1-1, including the total amount of funding requested by the localities and awards approved by the 9-1-1 Services Board. During this webinar, Lyle Hornbaker, Regional Coordinator for Region 5, will navigate through the dashboard and provide tips on how to more effectively utilize the ISP website. 12 June 2019. Recording not currently available. Please see the Virginia Next Generation 9-1-1 Dashboard and Resources Update webinar recording from 16 June 2021. Topic: PSAP Boundary Development Tools and Process RecommendationDescription: This webinar will be presented by Geospatial Program Manager Matt Gerike and VGIN Coordinator Joe Sewash. With the release of the PSAP boundary development tools and PSAP boundary segment compilation guidelines on the VGIN Clearinghouse in March, this webinar demonstrates the development tools, explains the process model, and discusses methods, tools, and resources available for you as you work to complete PSAP boundary segments with your neighbors. 15 May 2019. Recording available at: https://www.youtube.com/watch?v=kI-1DkUQF9Q&feature=youtu.beTopic: NG9-1-1 Data Preparation - Utilizing VITA's GIS Data Report Card ToolDescription: This webinar, presented by VGIN Coordinator Joe Sewash, Geospatial Program Manager Matt Gerike, and Geospatial Analyst Kenny Brevard will provide an overview of the first version of the tools that were released on March 25, 2019. These tools will allow localities to validate their GIS data against the report card rules, the MSAG and ALI checks used in previous report cards, and the analysis listed in the NG9-1-1 migration proposal document. We will also discuss the purpose of the tools, input requirements, initial configuration, how to run them, and how to make sense of your results. 10 April 2019. Recording available at: https://vimeo.com/showcase/9791882/video/761224495Topic: NG9-1-1 PSAP Boundary Best Practice WebinarDescription: During the months of November and December, VITA ISP staff hosted regional training sessions about best practices for PSAP boundaries as they relate to NG9-1-1. These sessions were well attended and very interactive, therefore we feel the need to do a recap and allow those that may have missed the training to attend a makeup session. 30 January 2019. Recording not currently available. Please see the PSAP Boundary Development Tools and Process Recommendation webinar recording from 15 May 2019.Topic: NG9-1-1 GIS Overview for ContractorsDescription: The Commonwealth of Virginia has started its migration to next generation 9-1-1 (NG9-1-1). This migration means that there will be a much greater reliance on geographic information (GIS) to locate and route 9-1-1 calls. VITA ISP has conducted an assessment of current local GIS data and provided each locality with a report. Some of the data from this report has also been included in the localities migration proposal, which identifies what data issues need to be resolved before the locality can migrate to NG9-1-1. Several localities in Virginia utilize a contractor to maintain their GIS data. This webinar is intended for those contractors to review the data in the report, what is included in the migration proposal and how they may be called on to assist the localities they serve. It will still ultimately be up to each locality to determine whether they engage a contractor for assistance, but it is important for the contractor community to understand what is happening and have an opportunity to ask questions about the intent and goals. This webinar will provide such an opportunity. 22 August 2018. Recording not currently available. Please contact us at NG911GIS@vdem.virginia.gov if you are interested in this content.

The Minnesota Geospatial Image Service provides versatile access to Minnesota air photos, hillshades, and scanned topographic maps using a Web Map Service (WMS). Using this service means you don't need to download and store these very large files on your own computer.

For a list of imagery data sets available through this service, see https://www.mngeo.state.mn.us/chouse/wms/wms_image_server_layers.html.

For technical specifications for using this service, see https://www.mngeo.state.mn.us/chouse/wms/wms_image_server_specs.html.

For information on how to use a Web Map Service (WMS), see https://www.mngeo.state.mn.us/chouse/wms/how_to_use_wms.html.

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. The WHSA vegetation map was developed using a combined strategy of automated digital image classification and direct analog image interpretation of aerial photography and satellite imagery. Initially, the aerial photography and satellite imagery were processed and entered into a GIS along with ancillary spatial layers. A working map legend of ecologically based vegetation map units was developed using the vegetation classification described in the report as the foundation. The intent was to develop map units that targeted the plant-association level wherever possible within the constraints of image quality, information content, and resolution. With the provisional legend and ground-control points provided by the field-plot data (the same data used to develop the vegetation classification), a combination of heads-up screen digitizing of polygons based on image interpretation and supervised image classifications were conducted. The outcome was a vegetation map composed of a suite of map units defined by plant associations and represented by sets of mapped polygons with similar spectral and site characteristics.

Abstract

This mapping project aimed to create a database of all financial, health, education, and agricultural service access points in Zambia.

Geographic coverage

National coverage except Ikelenge district

Analysis unit

Health care facilities, financial institutions, educational institutions, agricultural service providers

Universe

The project aimed to capture all open and operational touch-points at the time of fieldwork. Active points were considered to have done a transaction in the last 90 days. Not all points are captured due to several factors including:

i) non-location of the points ii) security areas iii) resistance or lack of cooperation iv) dormancy v) safety of fieldwork staff

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires used in the MSMP are specific to each sector service intermediary. This means that there are 14 different questionnaires, each with its own set of specific questions.

The Port and Port Statistical Area web service allows users to visualize and access two USACE enterprise-wide feature classes: the Port Feature Class and the Port Statistical Area Feature Class, both of which include polygon geometries used to generate statistics for commerce data and vessel movements. The GIS service includes attributes such as port name, boundary description, and associated legislative documentation.

USACE works with port authorities from across the United States to develop the statistical port boundaries through an iterative and collaborative process. Port boundary information is prepared by USACE to increase transparency on public waterborne commerce statistic reporting, as well as to modernize how the data type is stored, analyzed, and reported.

A Port Area is defined by the limits set by overarching legislative enactments of state, county, or city governments, or the corporate limits of a municipality. A port typically refers to a geographical area that includes operational activities related to maritime transport as well as acquisition, operation, and management of port infrastructure and property, such as might be associated with ownership, concession, construction approval, or policy decision-making authority.

A Port Statistical Area (PSA) is a region with formally justified shared economic interests and collective reliance on infrastructure related to waterborne movements of commodities that is formally recognized by legislative enactments of state, county, or city governments. PSAs generally contain groups of county legislation for the sole purpose of statistical reporting. Through GIS mapping, legislative boundaries, and stakeholder collaboration, PSAs often serve as the primary unit for aggregating and reporting commerce statistics for broader geographical areas.

Per Engineering Regulation 1130-2-520, the U.S. Army Corps of Engineers' Navigation Data Center is responsible to collect, compile, publish, and disseminate waterborne commerce statistics. This task has subsequently been charged to the Waterborne Commerce Statistics Center to perform. Performance of this work is in accordance with the Rivers and Harbors Appropriation Act of 1922. Included in this work is the definition of a port area. A port area is defined in Engineering Pamphlet 1130-2-520 as: (1) Port limits defined by legislative enactments of state, county, or city governments. (2) The corporate limits of a municipality. The USACE enterprise-wide port and port statistical area feature classes per EP 1130-2-520 are organized in SDSFIE 4.0.2 format.

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. Inc. (AIS) out of Redlands, CA. The mapping effort began in 1996 and by 2004 they had produced a vegetation map (referred to as the 2005 version of the map), along with two reports (see Appendix F and G) titled, Photo-Interpretation Report, USGS-NPS Vegetation and Inventory and Mapping Program, Joshua Tree National Park and USGS-NPS Vegetation Mapping Program, Joshua Tree National Park Mapping Classification. AIS was hired again in 2009-2010 to assist in updating the map; they hosted the meeting in August 2009, then proceeded to make changes to the map as discussed at the meeting. For the most part, this involved revisiting aerial photos and reevaluating the map class assigned to each problematic polygon, as well as correcting any global recodes and minor edits to the nomenclature. Aerial imagery used for the project was from 1998, including the revisits in 2009, and the minimum mapping unit was defined as 0.50 hectares. For more detail on methods used by AIS to produce the map and a summary of the project pre-2005, refer to the reports mentioned above.

UAE Geospatial Analytics Market size was valued at $ 285 Mn in 2024 and is expected to reach $ 758.11 Million by 2032, growing at a CAGR of 15% from 2026 to 2032UAE Geospatial Analytics Market: Definition/ OverviewGeospatial analytics refers to the process of collecting, analyzing, and interpreting data related to geographic locations. It integrates spatial data with other types of data to gain insights into patterns, trends, and relationships across various industries.

The National Land Cover Database 2001 Percent Developed Imperviousness was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of federal agencies (www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (EPA), the U.S. Department of Agriculture - Forest Service (USDA-FS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (FWS), the Bureau of Land Management (BLM) and the USDA Natural Resources Conservation Service (NRCS). One of the primary goals of the project is to generate a current, consistent, seamless, and accurate National Land Cover Database (NLCD) circa 2001 for the United States at medium spatial resolution. For a detailed definition and discussion on MRLC and the NLCD 2001 products, refer to Homer et al. (2003) and http://www.mrlc.gov/mrlc2k.asp. The NLCD 2001 was created by partitioning the U.S. into mapping zones. A total of 66 mapping zones were delineated within the conterminous U.S. based on ecoregion and geographical characteristics, edge-matching features and the size requirement of Landsat mosaics. This update represents a seamless assembly of updated NLCD 2001 Percent Developed Imperviousness for all 66 MRLC mapping zones. Questions about the NLCD 2001 Percent Developed Imperviousness 2011 Edition can be directed to the NLCD 2001 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.

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GIS In Telecom Sector Market Size 2025-2029

The GIS in telecom sector market size is forecast to increase by USD 2.35 billion at a CAGR of 15.7% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing adoption of Geographic Information Systems (GIS) for capacity planning in the telecommunications industry. GIS technology enables telecom companies to optimize network infrastructure, manage resources efficiently, and improve service delivery. Telecommunication assets and network management systems require GIS integration for efficient asset management and network slicing. However, challenges persist in this market. A communication gap between developers and end-users poses a significant obstacle.
Companies seeking to capitalize on opportunities in the market must focus on addressing these challenges, while also staying abreast of technological advancements and market trends. Effective collaboration between developers and end-users, coupled with strategic investments, will be essential for success in this dynamic market. Telecom companies must bridge this divide to ensure the development of user-friendly and effective GIS solutions. Network densification and virtualization platforms are key trends, allowing for efficient spectrum management and data monetization. Additionally, the implementation of GIS in the telecom sector requires substantial investment in technology and infrastructure, which may deter smaller players from entering the market.

What will be the Size of the GIS In Telecom Sector Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the dynamic telecom sector, GIS technology plays a pivotal role in customer analysis, network planning, and infrastructure development. Customer experiences are enhanced through location-based services and real-time data analysis, enabling telecom companies to tailor offerings and improve service quality. Network simulation and capacity planning are crucial for network evolution, with machine learning and AI integration facilitating network optimization and compliance with industry standards.
IOT connectivity and network analytics platforms offer valuable insights for smart city infrastructure development, with 3D data analysis and network outage analysis ensuring network resilience. Telecom industry partnerships foster innovation and collaboration, driving the continuous evolution of the sector. Consulting firms offer expertise in network compliance and network management, ensuring regulatory adherence and optimal network performance.

How is this GIS In Telecom Sector Industry segmented?

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

Product

  Software
  Data
  Services


Deployment

  On-premises
  Cloud


Application

  Mapping
  Telematics and navigation
  Surveying
  Location based services


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Product Insights

The software segment is estimated to witness significant growth during the forecast period. In the telecom sector, the deployment of 5G networks is driving the need for advanced Geographic Information Systems (GIS) to optimize network performance and efficiency. GIS technology enables spatial analysis, network automation, capacity analysis, and bandwidth management, all crucial elements in the rollout of 5G networks. Large enterprises and telecom consulting firms are integrating GIS data into their operations for network planning, optimization, and troubleshooting. Machine learning and artificial intelligence are transforming GIS applications, offering predictive analytics and real-time network performance monitoring. Network virtualization and software-defined networking are also gaining traction, enhancing network capacity and improving network reliability and maintenance.

GIS software companies provide solutions for desktops, mobiles, cloud, and servers, catering to various industry needs. Smart city initiatives and location-based services are expanding the use cases for GIS in telecom, offering new opportunities for growth. Infrastructure deployment and population density analysis are critical factors in network rollout and capacity enhancement. Network security and performance monitoring are essential components of GIS applications, ensuring network resilience and customer experience management. Edge computing and network latency reduction are also signi

The Wildland Fire Interagency Geospatial Services (WFIGS) Group provides authoritative geospatial data products under the interagency Wildland Fire Data Program. Hosted in the National Interagency Fire Center ArcGIS Online Organization (The NIFC Org), WFIGS provides both internal and public facing data, accessible in a variety of formats.This service includes perimeters for wildland fire incidents that meet the following criteria:Categorized in the IRWIN (Integrated Reporting of Wildland Fire Information) integration service as a valid Wildfire (WF), Prescribed Fire (RX), or Incident Complex (CX) record with a Fire Discovery Date in the year 2021Is not "quarantined" in IRWIN due to potential conflicts with other recordsAttribution of the source polygon is set to a Feature Access of Public, a Feature Status of Approved, and an Is Visible setting of YesPerimeters are not available for every incident. For a complete set of features that meet the same IRWIN criteria, see the 2021 Wildland Fire Locations to Date service.No "fall-off" rules are applied to this service. Criteria were determined by an NWCG Geospatial Subcommittee task group. Data are refreshed every 5 minutes. Changes in the perimeter source may take up to 15 minutes to display.Perimeters are pulled from multiple sources with rules in place to ensure the most current or most authoritative shape is used.Warning: Please refrain from repeatedly querying the service using a relative date range. This includes using the “(not) in the last” operators in a Web Map filter and any reference to CURRENT_TIMESTAMP. This type of query puts undue load on the service and may render it temporarily unavailable.Attributes and their definitions can be found below. More detail about the NWCG Wildland Fire Event Polygon standard can be found here.Attributes:Incident Name (Polygon)The Incident Name from the source polygon.Feature CategoryType of wildland fire perimeter set for the source polygon.Map MethodControlled vocabulary to define how the source polygon was derived. Map Method may help define data quality.GIS AcresUser-calculated acreage on the source polygon.Polygon Create DateSystem field. Time stamp for the source polygon feature creation.Polygon Modified DateSystem field. Time stamp for the most recent edit to the source polygon feature.Polygon Collection Date TimeDate time for the source polygon feature collection.Acres Auto CalculatedAutomated calculation of the source polygon acreage.Polygon SourceData source of the perimeter geometry.{Year} NIFS: Annual National Incident Feature ServiceFFP: Final Fire Perimeter Service (Certified Perimeters)ABCD MiscA FireCode used by USDA FS to track and compile cost information for emergency initial attack fire suppression expenditures. for A, B, C & D size class fires on FS lands.ADS Permission StateIndicates the permission hierarchy that is currently being applied when a system utilizes the UpdateIncident operation.IRWIN Archived OnA date set by IRWIN that indicates when an incident's data has met the rules defined for the record to become part of the historical fire records rather than an operational incident record. The value will be set the current date/time if any of the following criteria are met: 1. ContainmentDataTime or ControlDateTime or FireOutDateTime or ModifiedOnDateTime > 12 months from the current DateTime2. FinalFireReportDate is not null and ADSPermissionState is 'certified'.Calculated AcresA measure of acres calculated (i.e., infrared) from a geospatial perimeter of a fire. More specifically, the number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands. The minimum size must be 0.1.Containment Date TimeThe date and time a wildfire was declared contained. Control Date TimeThe date and time a wildfire was declared under control.Created By SystemArcGIS Server Username of system that created the IRWIN Incident record.IRWIN Created On Date TimeDate/time that the IRWIN Incident record was created.IRWIN Daily AcresA measure of acres reported for a fire. More specifically, the number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands. The minimum size must be 0.1.Discovery AcresAn estimate of acres burning upon the discovery of the fire. More specifically when the fire is first reported by the first person that calls in the fire. The estimate should include number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.Dispatch Center IDA unique identifier for a dispatch center responsible for supporting the incident.Final Fire Report Approved By TitleThe title of the person that approved the final fire report for the incident.Final Fire Report Approved By UnitNWCG Unit ID associated with the individual who approved the final report for the incident.Final Fire Report Approved DateThe date that the final fire report was approved for the incident.Fire Behavior GeneralA general category describing the manner in which the fire is currently reacting to the influences of fuel, weather, and topography. Fire Behavior General 1A more specific category further describing the general fire behavior (manner in which the fire is currently reacting to the influences of fuel, weather, and topography). Fire Behavior General 2A more specific category further describing the general fire behavior (manner in which the fire is currently reacting to the influences of fuel, weather, and topography). Fire Behavior General 3A more specific category further describing the general fire behavior (manner in which the fire is currently reacting to the influences of fuel, weather, and topography). Fire CauseBroad classification of the reason the fire occurred identified as human, natural or unknown. Fire Cause GeneralAgency or circumstance which started a fire or set the stage for its occurrence; source of a fire's ignition. For statistical purposes, fire causes are further broken into specific causes. Fire Cause SpecificA further categorization of each General Fire Cause to indicate more specifically the agency or circumstance which started a fire or set the stage for its occurrence; source of a fire's ignition. Fire CodeA code used within the interagency wildland fire community to track and compile cost information for emergency fire suppression expenditures for the incident. Fire Department IDThe U.S. Fire Administration (USFA) has created a national database of Fire Departments. Most Fire Departments do not have an NWCG Unit ID and so it is the intent of the IRWIN team to create a new field that includes this data element to assist the National Association of State Foresters (NASF) with data collection.Fire Discovery Date TimeThe date and time a fire was reported as discovered or confirmed to exist. May also be the start date for reporting purposes.Fire Mgmt ComplexityThe highest management level utilized to manage a wildland fire event. Fire Out Date TimeThe date and time when a fire is declared out. Fire Strategy Confine PercentIndicates the percentage of the incident area where the fire suppression strategy of "Confine" is being implemented.Fire Strategy Full Supp PercentIndicates the percentage of the incident area where the fire suppression strategy of "Full Suppression" is being implemented.Fire Strategy Monitor PercentIndicates the percentage of the incident area where the fire suppression strategy of "Monitor" is being implemented.Fire Strategy Point Zone PercentIndicates the percentage of the incident area where the fire suppression strategy of "Point Zone Protection" is being implemented.FS Job CodeA code use to indicate the Forest Service job accounting code for the incident. This is specific to the Forest Service. Usually displayed as 2 char prefix on FireCode.FS Override CodeA code used to indicate the Forest Service override code for the incident. This is specific to the Forest Service. Usually displayed as a 4 char suffix on FireCode. For example, if the FS is assisting DOI, an override of 1502 will be used.GACCA code that identifies one of the wildland fire geographic area coordination center at the point of origin for the incident.A geographic area coordination center is a facility that is used for the coordination of agency or jurisdictional resources in support of one or more incidents within a geographic coordination area.ICS 209 Report Date TimeThe date and time of the latest approved ICS-209 report.ICS 209 Report For Time Period FromThe date and time of the beginning of the time period for the current ICS-209 submission.ICS 209 Report For Time Period ToThe date and time of the end of the time period for the current ICS-209 submission. ICS 209 Report StatusThe version of the ICS-209 report (initial, update, or final). There should never be more than one initial report, but there can be numerous updates, and even multiple finals (as determined by business rules).Incident Management OrganizationThe incident management organization for the incident, which may be a Type 1, 2, or 3 Incident Management Team (IMT), a Unified Command, a Unified Command with an IMT, National Incident Management Organization (NIMO), etc. This field is null if no team is assigned.Incident NameThe name assigned to an incident.Incident Short DescriptionGeneral descriptive location of the incident such as the number of miles from an identifiable town. Incident Type CategoryThe Event Category is a sub-group of the Event Kind code and description. The Event Category further breaks down the Event Kind into more specific event categories.Incident Type KindA general, high-level code and description of the types of incidents and planned events to which the interagency wildland fire community responds.Initial LatitudeThe latitude location of the initial reported point of origin specified in decimal degrees.Initial LongitudeThe longitude location

The dataset and the validation are fully described in a Nature Scientific Data Descriptor https://www.nature.com/articles/s41597-019-0265-5

If you want to use this dataset in an interactive environment, then use this link https://mybinder.org/v2/gh/GeographerAtLarge/TravelTime/HEAD

The following text is a summary of the information in the above Data Descriptor.

The dataset is a suite of global travel-time accessibility indicators for the year 2015, at approximately one-kilometre spatial resolution for the entire globe. The indicators show an estimated (and validated), land-based travel time to the nearest city and nearest port for a range of city and port sizes.

The datasets are in GeoTIFF format and are suitable for use in Geographic Information Systems and statistical packages for mapping access to cities and ports and for spatial and statistical analysis of the inequalities in access by different segments of the population.

These maps represent a unique global representation of physical access to essential services offered by cities and ports.

The datasets travel_time_to_cities_x.tif (where x has values from 1 to 12) The value of each pixel is the estimated travel time in minutes to the nearest urban area in 2015. There are 12 data layers based on different sets of urban areas, defined by their population in year 2015 (see PDF report).

travel_time_to_ports_x (x ranges from 1 to 5)

The value of each pixel is the estimated travel time to the nearest port in 2015. There are 5 data layers based on different port sizes.

Format Raster Dataset, GeoTIFF, LZW compressed Unit Minutes

Data type Byte (16 bit Unsigned Integer)

No data value 65535

Flags None

Spatial resolution 30 arc seconds

Spatial extent

Upper left -180, 85

Lower left -180, -60 Upper right 180, 85 Lower right 180, -60 Spatial Reference System (SRS) EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long)

Temporal resolution 2015

Temporal extent Updates may follow for future years, but these are dependent on the availability of updated inputs on travel times and city locations and populations.

Methodology Travel time to the nearest city or port was estimated using an accumulated cost function (accCost) in the gdistance R package (van Etten, 2018). This function requires two input datasets: (i) a set of locations to estimate travel time to and (ii) a transition matrix that represents the cost or time to travel across a surface.

The set of locations were based on populated urban areas in the 2016 version of the Joint Research Centre’s Global Human Settlement Layers (GHSL) datasets (Pesaresi and Freire, 2016) that represent low density (LDC) urban clusters and high density (HDC) urban areas (https://ghsl.jrc.ec.europa.eu/datasets.php). These urban areas were represented by points, spaced at 1km distance around the perimeter of each urban area.

Marine ports were extracted from the 26th edition of the World Port Index (NGA, 2017) which contains the location and physical characteristics of approximately 3,700 major ports and terminals. Ports are represented as single points

The transition matrix was based on the friction surface (https://map.ox.ac.uk/research-project/accessibility_to_cities) from the 2015 global accessibility map (Weiss et al, 2018).

Code The R code used to generate the 12 travel time maps is included in the zip file that can be downloaded with these data layers. The processing zones are also available.

Validation The underlying friction surface was validated by comparing travel times between 47,893 pairs of locations against journey times from a Google API. Our estimated journey times were generally shorter than those from the Google API. Across the tiles, the median journey time from our estimates was 88 minutes within an interquartile range of 48 to 143 minutes while the median journey time estimated by the Google API was 106 minutes within an interquartile range of 61 to 167 minutes. Across all tiles, the differences were skewed to the left and our travel time estimates were shorter than those reported by the Google API in 72% of the tiles. The median difference was −13.7 minutes within an interquartile range of −35.5 to 2.0 minutes while the absolute difference was 30 minutes or less for 60% of the tiles and 60 minutes or less for 80% of the tiles. The median percentage difference was −16.9% within an interquartile range of −30.6% to 2.7% while the absolute percentage difference was 20% or less in 43% of the tiles and 40% or less in 80% of the tiles.

This process and results are included in the validation zip file.

Usage Notes The accessibility layers can be visualised and analysed in many Geographic Information Systems or remote sensing software such as QGIS, GRASS, ENVI, ERDAS or ArcMap, and also by statistical and modelling packages such as R or MATLAB. They can also be used in cloud-based tools for geospatial analysis such as Google Earth Engine.

The nine layers represent travel times to human settlements of different population ranges. Two or more layers can be combined into one layer by recording the minimum pixel value across the layers. For example, a map of travel time to the nearest settlement of 5,000 to 50,000 people could be generated by taking the minimum of the three layers that represent the travel time to settlements with populations between 5,000 and 10,000, 10,000 and 20,000 and, 20,000 and 50,000 people.

The accessibility layers also permit user-defined hierarchies that go beyond computing the minimum pixel value across layers. A user-defined complete hierarchy can be generated when the union of all categories adds up to the global population, and the intersection of any two categories is empty. Everything else is up to the user in terms of logical consistency with the problem at hand.

The accessibility layers are relative measures of the ease of access from a given location to the nearest target. While the validation demonstrates that they do correspond to typical journey times, they cannot be taken to represent actual travel times. Errors in the friction surface will be accumulated as part of the accumulative cost function and it is likely that locations that are further away from targets will have greater a divergence from a plausible travel time than those that are closer to the targets. Care should be taken when referring to travel time to the larger cities when the locations of interest are extremely remote, although they will still be plausible representations of relative accessibility. Furthermore, a key assumption of the model is that all journeys will use the fastest mode of transport and take the shortest path.

The National Land Cover Database 2001 Land Cover 2011 Edition layer is produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of federal agencies (www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (EPA), the U.S. Department of Agriculture - Forest Service (USDA-FS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (FWS), the Bureau of Land Management (BLM) and the USDA Natural Resources Conservation Service (NRCS). One of the primary goals of the project is to generate a current, consistent, seamless, and accurate National Land Cover Database (NLCD) circa 2001 for the United States at medium spatial resolution. This land cover map and all documents pertaining to it are considered "provisional" until a formal accuracy assessment can be conducted. For a detailed definition and discussion on MRLC and the NLCD 2001 products, refer to Homer et al. (2004) and http://www.mrlc.gov/mrlc2k.asp. The NLCD 2001 is created by partitioning the U.S. into mapping zones. A total of 66 mapping zones were delineated within the conterminous U.S. based on ecoregion and geographical characteristics, edge matching features and the size requirement of Landsat mosaics. This update represents a seamless assembly of updated NLCD 2001 Land Cover (2011 Edition) for all 66 MRLC mapping zones. Questions about the NLCD the NLCD 2001 Land Cover 2011 Edition can be directed to the NLCD 2001 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.

TransportationThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Census Bureau, displays primary roads, secondary roads, local roads and railroads in the United States. According to the USCB, "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."Interstates 20 and 635Data currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (TIGERweb/Transportation) and will support mapping, analysis, data exports and OGC API – Feature access.NGDAID: 155 (Series Information for All Roads County-based TIGER/Line Shapefiles, Current)OGC API Features Link: (Transportation - OGC Features) copy this link to embed it in OGC Compliant viewersFor more information, please visit: Census Feature Class Codes (CFCC)For feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Governmental Units, and Administrative and Statistical Boundaries Theme Community. Per the Federal Geospatial Data Committee (FGDC), this theme is defined as the "boundaries that delineate geographic areas for uses such as governance and the general provision of services (e.g., states, American Indian reservations, counties, cities, towns, etc.), administration and/or for a specific purpose (e.g., congressional districts, school districts, fire districts, Alaska Native Regional Corporations, etc.), and/or provision of statistical data (census tracts, census blocks, metropolitan and micropolitan statistical areas, etc.). Boundaries for these various types of geographic areas are either defined through a documented legal description or through criteria and guidelines. Other boundaries may include international limits, those of federal land ownership, the extent of administrative regions for various federal agencies, as well as the jurisdictional offshore limits of U.S. sovereignty. Boundaries associated solely with natural resources and/or cultural entities are excluded from this theme and are included in the appropriate subject themes."For other NGDA Content: Esri Federal Datasets

Our dataset provides detailed and precise insights into the business, commercial, and industrial aspects of any given area in the USA (Including Point of Interest (POI) Data and Foot Traffic. The dataset is divided into 150x150 sqm areas (geohash 7) and has over 50 variables. - Use it for different applications: Our combined dataset, which includes POI and foot traffic data, can be employed for various purposes. Different data teams use it to guide retailers and FMCG brands in site selection, fuel marketing intelligence, analyze trade areas, and assess company risk. Our dataset has also proven to be useful for real estate investment.- Get reliable data: Our datasets have been processed, enriched, and tested so your data team can use them more quickly and accurately.- Ideal for trainning ML models. The high quality of our geographic information layers results from more than seven years of work dedicated to the deep understanding and modeling of geospatial Big Data. Among the features that distinguished this dataset is the use of anonymized and user-compliant mobile device GPS location, enriched with other alternative and public data.- Easy to use: Our dataset is user-friendly and can be easily integrated to your current models. Also, we can deliver your data in different formats, like .csv, according to your analysis requirements. - Get personalized guidance: In addition to providing reliable datasets, we advise your analysts on their correct implementation.Our data scientists can guide your internal team on the optimal algorithms and models to get the most out of the information we provide (without compromising the security of your internal data).Answer questions like: - What places does my target user visit in a particular area? Which are the best areas to place a new POS?- What is the average yearly income of users in a particular area?- What is the influx of visits that my competition receives?- What is the volume of traffic surrounding my current POS?This dataset is useful for getting insights from industries like:- Retail & FMCG- Banking, Finance, and Investment- Car Dealerships- Real Estate- Convenience Stores- Pharma and medical laboratories- Restaurant chains and franchises- Clothing chains and franchisesOur dataset includes more than 50 variables, such as:- Number of pedestrians seen in the area.- Number of vehicles seen in the area.- Average speed of movement of the vehicles seen in the area.- Point of Interest (POIs) (in number and type) seen in the area (supermarkets, pharmacies, recreational locations, restaurants, offices, hotels, parking lots, wholesalers, financial services, pet services, shopping malls, among others). - Average yearly income range (anonymized and aggregated) of the devices seen in the area.Notes to better understand this dataset:- POI confidence means the average confidence of POIs in the area. In this case, POIs are any kind of location, such as a restaurant, a hotel, or a library. - Category confidences, for example"food_drinks_tobacco_retail_confidence" indicates how confident we are in the existence of food/drink/tobacco retail locations in the area. - We added predictions for The Home Depot and Lowe's Home Improvement stores in the dataset sample. These predictions were the result of a machine-learning model that was trained with the data. Knowing where the current stores are, we can find the most similar areas for new stores to open.How efficient is a Geohash?Geohash is a faster, cost-effective geofencing option that reduces input data load and provides actionable information. Its benefits include faster querying, reduced cost, minimal configuration, and ease of use.Geohash ranges from 1 to 12 characters. The dataset can be split into variable-size geohashes, with the default being geohash7 (150m x 150m).

The Wildland Fire Interagency Geospatial Services (WFIGS) Group provides authoritative geospatial data products under the interagency Wildland Fire Data Program. Hosted in the NIFC Open Data Site, WFIGS provides both internal and public facing data, accessible in a variety of formats.This service includes perimeters for wildland fire incidents that meet the following criteria:Categorized in the IRWIN (Integrated Reporting of Wildland Fire Information) integration service as a valid Wildfire (WF), Prescribed Fire (RX), or Incident Complex (CX) record.Is not "quarantined" in IRWIN due to potential conflicts with other recordsAttribution of the source polygon is set to a Feature Access of Public, a Feature Status of Approved, and an Is Visible setting of YesPerimeters are not available for every incident. For a complete set of features that meet the same IRWIN criteria, see the 2021 Wildland Fire Locations to Date service.No "fall-off" rules are applied to this service. Criteria were determined by an NWCG Geospatial Subcommittee task group. Data are refreshed every 5 minutes. Changes in the perimeter source may take up to 15 minutes to display.Perimeters are pulled from multiple sources with rules in place to ensure the most current or most authoritative shape is used.Warning: Please refrain from repeatedly querying the service using a relative date range. This includes using the “(not) in the last” operators in a Web Map filter and any reference to CURRENT_TIMESTAMP. This type of query puts undue load on the service and may render it temporarily unavailable.Attributes and their definitions can be found below. More detail about the NWCG Wildland Fire Event Polygon standard can be found here.Attributes:Incident Name (Polygon)The Incident Name from the source polygon.Feature CategoryType of wildland fire perimeter set for the source polygon.Map MethodControlled vocabulary to define how the source polygon was derived. Map Method may help define data quality.GIS AcresUser-calculated acreage on the source polygon.Polygon Create DateSystem field. Time stamp for the source polygon feature creation.Polygon Modified DateSystem field. Time stamp for the most recent edit to the source polygon feature.Polygon Collection Date TimeDate time for the source polygon feature collection.Acres Auto CalculatedAutomated calculation of the source polygon acreage.Polygon SourceData source of the perimeter geometry.{Year} NIFS: Annual National Incident Feature ServiceFFP: Final Fire Perimeter Service (Certified Perimeters)ABCD MiscA FireCode used by USDA FS to track and compile cost information for emergency initial attack fire suppression expenditures. for A, B, C & D size class fires on FS lands.ADS Permission StateIndicates the permission hierarchy that is currently being applied when a system utilizes the UpdateIncident operation.IRWIN Archived OnA date set by IRWIN that indicates when an incident's data has met the rules defined for the record to become part of the historical fire records rather than an operational incident record. The value will be set the current date/time if any of the following criteria are met: 1. ContainmentDataTime or ControlDateTime or FireOutDateTime or ModifiedOnDateTime > 12 months from the current DateTime2. FinalFireReportDate is not null and ADSPermissionState is 'certified'.Calculated AcresA measure of acres calculated (i.e., infrared) from a geospatial perimeter of a fire. More specifically, the number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands. The minimum size must be 0.1.Containment Date TimeThe date and time a wildfire was declared contained. Control Date TimeThe date and time a wildfire was declared under control.Created By SystemArcGIS Server Username of system that created the IRWIN Incident record.IRWIN Created On Date TimeDate/time that the IRWIN Incident record was created.IRWIN Daily AcresA measure of acres reported for a fire. More specifically, the number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands. The minimum size must be 0.1.Discovery AcresAn estimate of acres burning upon the discovery of the fire. More specifically when the fire is first reported by the first person that calls in the fire. The estimate should include number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.Dispatch Center IDA unique identifier for a dispatch center responsible for supporting the incident.Final Fire Report Approved By TitleThe title of the person that approved the final fire report for the incident.Final Fire Report Approved By UnitNWCG Unit ID associated with the individual who approved the final report for the incident.Final Fire Report Approved DateThe date that the final fire report was approved for the incident.Fire Behavior GeneralA general category describing the manner in which the fire is currently reacting to the influences of fuel, weather, and topography. Fire Behavior General 1A more specific category further describing the general fire behavior (manner in which the fire is currently reacting to the influences of fuel, weather, and topography). Fire Behavior General 2A more specific category further describing the general fire behavior (manner in which the fire is currently reacting to the influences of fuel, weather, and topography). Fire Behavior General 3A more specific category further describing the general fire behavior (manner in which the fire is currently reacting to the influences of fuel, weather, and topography). Fire CauseBroad classification of the reason the fire occurred identified as human, natural or unknown. Fire Cause GeneralAgency or circumstance which started a fire or set the stage for its occurrence; source of a fire's ignition. For statistical purposes, fire causes are further broken into specific causes. Fire Cause SpecificA further categorization of each General Fire Cause to indicate more specifically the agency or circumstance which started a fire or set the stage for its occurrence; source of a fire's ignition. Fire CodeA code used within the interagency wildland fire community to track and compile cost information for emergency fire suppression expenditures for the incident. Fire Department IDThe U.S. Fire Administration (USFA) has created a national database of Fire Departments. Most Fire Departments do not have an NWCG Unit ID and so it is the intent of the IRWIN team to create a new field that includes this data element to assist the National Association of State Foresters (NASF) with data collection.Fire Discovery Date TimeThe date and time a fire was reported as discovered or confirmed to exist. May also be the start date for reporting purposes.Fire Mgmt ComplexityThe highest management level utilized to manage a wildland fire event. Fire Out Date TimeThe date and time when a fire is declared out. Fire Strategy Confine PercentIndicates the percentage of the incident area where the fire suppression strategy of "Confine" is being implemented.Fire Strategy Full Supp PercentIndicates the percentage of the incident area where the fire suppression strategy of "Full Suppression" is being implemented.Fire Strategy Monitor PercentIndicates the percentage of the incident area where the fire suppression strategy of "Monitor" is being implemented.Fire Strategy Point Zone PercentIndicates the percentage of the incident area where the fire suppression strategy of "Point Zone Protection" is being implemented.FS Job CodeA code use to indicate the Forest Service job accounting code for the incident. This is specific to the Forest Service. Usually displayed as 2 char prefix on FireCode.FS Override CodeA code used to indicate the Forest Service override code for the incident. This is specific to the Forest Service. Usually displayed as a 4 char suffix on FireCode. For example, if the FS is assisting DOI, an override of 1502 will be used.GACCA code that identifies one of the wildland fire geographic area coordination center at the point of origin for the incident.A geographic area coordination center is a facility that is used for the coordination of agency or jurisdictional resources in support of one or more incidents within a geographic coordination area.ICS 209 Report Date TimeThe date and time of the latest approved ICS-209 report.ICS 209 Report For Time Period FromThe date and time of the beginning of the time period for the current ICS-209 submission.ICS 209 Report For Time Period ToThe date and time of the end of the time period for the current ICS-209 submission. ICS 209 Report StatusThe version of the ICS-209 report (initial, update, or final). There should never be more than one initial report, but there can be numerous updates, and even multiple finals (as determined by business rules).Incident Management OrganizationThe incident management organization for the incident, which may be a Type 1, 2, or 3 Incident Management Team (IMT), a Unified Command, a Unified Command with an IMT, National Incident Management Organization (NIMO), etc. This field is null if no team is assigned.Incident NameThe name assigned to an incident.Incident Short DescriptionGeneral descriptive location of the incident such as the number of miles from an identifiable town. Incident Type CategoryThe Event Category is a sub-group of the Event Kind code and description. The Event Category further breaks down the Event Kind into more specific event categories.Incident Type KindA general, high-level code and description of the types of incidents and planned events to which the interagency wildland fire community responds.Initial LatitudeThe latitude location of the initial reported point of origin specified in decimal degrees.Initial LongitudeThe longitude location of the initial reported point of origin specified in decimal degrees.Initial Response AcresAn

The global map navigation service market is experiencing robust growth, driven by the increasing penetration of smartphones, the proliferation of connected cars, and the rising demand for location-based services (LBS). The market's expansion is fueled by advancements in mapping technologies, such as high-definition (HD) maps and real-time traffic updates, which enhance user experience and safety. Furthermore, the integration of map navigation with other services, like ride-hailing apps and delivery platforms, is creating new avenues for growth. While challenges exist, such as data privacy concerns and the need for accurate map data in remote areas, the overall market outlook remains positive. We project a Compound Annual Growth Rate (CAGR) of approximately 15% from 2025 to 2033, with significant regional variations driven by factors such as infrastructure development, smartphone adoption rates, and government regulations. The market is segmented by service type (in-car, mobile, etc.), application (consumer, commercial), and technology (GPS, satellite, etc.), each exhibiting unique growth trajectories. Key players are strategically investing in research and development, mergers and acquisitions, and partnerships to strengthen their market positions and meet the evolving needs of consumers and businesses. The competitive landscape is highly fragmented, with numerous established players and emerging startups vying for market share. Companies like Google, TomTom, Garmin, and others are continually innovating to enhance their map data, user interfaces, and overall service offerings. The focus on providing personalized experiences, incorporating augmented reality (AR) features, and leveraging artificial intelligence (AI) for route optimization and traffic prediction is transforming the map navigation service market. The integration of autonomous driving technology presents a significant long-term growth opportunity, as accurate and reliable map data is crucial for the safe and efficient operation of self-driving vehicles. However, maintaining data accuracy, addressing cybersecurity threats, and ensuring compliance with evolving regulations will be critical for sustained success in this dynamic market.

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .

Nigeria Geospatial Analytics Market size was valued at USD 86.37 Million in 2024 and is expected to reach USD 146.41 Million by 2032, growing at a CAGR of 6.82% from 2026 to 2032.

Nigeria Geospatial Analytics Market: Definition/ Overview

Geospatial analytics involves the application of computational methods to analyze spatial data, deriving insights about geographic phenomena and relationships. This process integrates geographic information systems (GIS), remote sensing, and statistical techniques to identify patterns, trends, and anomalies within spatial datasets. It facilitates informed decision-making across various domains by providing spatial context and predictive modeling capabilities.

Geospatial analytics facilitates precise environmental monitoring through the detection and analysis of land cover changes, deforestation, and urban expansion. It supports informed infrastructure planning by optimizing site selection, assessing transportation networks, and managing utilities.