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. Our final map product is a geographic information system (GIS) database of vegetation structure and composition across the Crater Lake National Park terrestrial landscape, including wetlands. The database includes photos we took at all relevé, validation, and accuracy assessment plots, as well as the plots that were done in the previous wetlands inventory. We conducted an accuracy assessment of the map by evaluating 698 stratified random accuracy assessment plots throughout the project area. We intersected these field data with the vegetation map, resulting in an overall thematic accuracy of 86.2 %. The accuracy of the Cliff, Scree & Rock Vegetation map unit was difficult to assess, as only 9% of this vegetation type was available for sampling due to lack of access. In addition, fires that occurred during the 2017 accuracy assessment field season affected our sample design and may have had a small influence on the accuracy. Our geodatabase contains the locations where particular associations are found at 600 relevé plots, 698 accuracy assessment plots, and 803 validation plots.

This dataset provides attributed geospatial and tabular information for identifying and querying flight lines of interest for the Airborne Visible InfraRed Imaging Spectrometer-Classic (AVIRIS-C) and Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) Facility Instrument collections. It includes attributed shapefile and GeoJSON files containing polygon representation of individual flights lines for all years and separate KMZ files for each year. These files allow users to visualize and query flight line locations using Geographic Information System (GIS) software. Tables of AVIRIS-C and AVIRIS-NG flight lines with attributed information include dates, bounding coordinates, site names, investigators involved, flight attributes, associated campaigns, and corresponding file names for associated L1B (radiance) and L2 (reflectance) files in the AVIRIS-C and AVIRIS-NG Facility Instrument Collections. Tabular information is also provided in comma-separated values (CSV) 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. We used ERDAS Imagine ® Professional 9.2, ENVI ® 4.5, and ArcGIS ® 9.3 with Arc Workstation to develop the vegetation spatial database. Existing GIS datasets that we used to provide mapping information include a NPS park boundary shapefile for VICK (including a 100 meter buffer boundary around the Louisiana Circle, South Fort, and Navy Circle satellite units), a land cover shapefile created by the NWRC (Rangoonwala et al. 2007), and the National Elevation Dataset (NED) (used as the source of the 10-meter elevation model and derived streams, slope, and hillshade). To make the entire spatial data set consistent with NPSVI policies to map only to park boundaries, we clipped the vegetation in and around the previously buffered areas around the Louisiana Circle, South Fort, and Navy Circle satellite unit NPS boundaries. We also added to the spatial database vegetation polygons for the previously omitted Grant’s Canal satellite unit by heads-up digitizing this area from a National Agricultural Information Program (NAIP) image.

This U.S. Geological Survey data release consists multiple datasets used to simulate the extents of flood inundation along the Muddy River, near Moapa, Nevada. Flood-inundation extents equal the maximum area of flood inundation and were estimated using a coupled one-dimensional (1D) and two-dimensional (2D) hydraulic model. The modeled extents represent six annual exceedance probabilities simulated for the current (2019) levee location adjacent to the Muddy River and a new levee location associated with a proposed restoration of a selected reach along the Muddy River. The data release includes: 1) a polygon dataset of the flood-inundation extents (MuddyRiver_Flood_Inundation_p.shp); 2) a zip file containing all relevant files to document and run the PeakFQ flood-frequency analysis used as input into the hydraulic model (0941600_Flood_Frequency_Archive.zip); 3) a zip file containing all relevant files to document and run the coupled 1D and 2D Hydrological Engineering Center-River Analysis System (HEC-RAS) hydraulic model used to generate a polygon dataset of flood-inundation extents (SWmodel_Archive.zip); 4) a polygon dataset of the current and proposed levee locations (MuddyRiver_Levee_p.shp); 5) a point dataset of survey points (RTK-TS_survey_x.shp) collected from April 1 to August 9, 2019, using real-time kinematic global navigation satellite system (GNSS) and total station (TS); and 6) a point dataset of seven static reference locations (Static_GNSS_x.shp) collected from March 29 to August 9, 2019, using a single-baseline online positioning user service – static (OPUS-S) GNSS survey.

Earth Data Analysis Center (EDAC) at The University of New Mexico (UNM) develops, manages, and enhances the New Mexico Resource Geographic Information System (RGIS) Program and Clearinghouse. Nationally, NM RGIS is among the largest of state-based programs for digital geospatial data and information and continues to add to its data offerings, services, and technology.

The RGIS Program mission is to develop and expand geographic information and use of GIS technology, creating a comprehensive GIS resource for state and local governments, educational institutions, nonprofit organizations, and private businesses; to promote geospatial information and GIS technology as primary analytical tools for decision makers and researchers; and to provide a central Clearinghouse to avoid duplication and improve information transfer efficiency.

As a repository for digital geospatial data acquired from local and national public agencies and data created expressly for RGIS, the clearinghouse serves as a major hub in New Mexico’s network for inter-agency and intergovernmental coordination, data sharing, information transfer, and electronic communication. Data sets available for download include political and administrative boundaries, place names and locations, census data (current and historical), 30 years of digital orthophotography, 80 years of historic aerial photography, satellite imagery, elevation data, transportation data, wildfire boundaries and natural resource data.

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Geospatial Analytics Market Size 2025-2029

The geospatial analytics market size is forecast to increase by USD 178.6 billion, at a CAGR of 21.4% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing adoption of geospatial analytics in sectors such as healthcare and insurance. This trend is fueled by the ability of geospatial analytics to provide valuable insights from location-based data, leading to improved operational efficiency and decision-making. Additionally, emerging methods in data collection and generation, including the use of drones and satellite imagery, are expanding the scope and potential of geospatial analytics. However, the market faces challenges, including data privacy and security concerns. With the vast amounts of sensitive location data being collected and analyzed, ensuring its protection is crucial for companies to maintain trust with their customers and avoid regulatory penalties. Navigating these challenges and capitalizing on the opportunities presented by the growing adoption of geospatial analytics requires a strategic approach from industry players. Companies must prioritize data security, invest in advanced analytics technologies, and collaborate with stakeholders to build trust and transparency. By addressing these challenges and leveraging the power of geospatial analytics, businesses can gain a competitive edge and unlock new opportunities in various industries.

What will be the Size of the Geospatial Analytics 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.
Request Free SampleThe market continues to evolve, driven by the increasing demand for location-specific insights across various sectors. Urban planning relies on geospatial optimization and data enrichment to enhance city designs and improve infrastructure. Cloud-based geospatial solutions facilitate real-time data access, enabling location intelligence for public safety and resource management. Spatial data standards ensure interoperability among different systems, while geospatial software and data visualization tools provide valuable insights from satellite imagery and aerial photography. Geospatial services offer data integration, spatial data accuracy, and advanced analytics capabilities, including 3D visualization, route optimization, and data cleansing. Precision agriculture and environmental monitoring leverage geospatial data to optimize resource usage and monitor ecosystem health. Infrastructure management and real estate industries rely on geospatial data for asset tracking and market analysis. Spatial statistics and disaster management applications help mitigate risks and respond effectively to crises. Geospatial data management and quality remain critical as the volume and complexity of data grow. Geospatial modeling and interoperability enable seamless data sharing and collaboration. Sensor networks and geospatial data acquisition technologies expand the reach of geospatial analytics, while AI-powered geospatial analytics offer new opportunities for predictive analysis and automation. The ongoing development of geospatial technologies and applications underscores the market's continuous dynamism, providing valuable insights and solutions for businesses and organizations worldwide.

How is this Geospatial Analytics Industry segmented?

The geospatial analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. TechnologyGPSGISRemote sensingOthersEnd-userDefence and securityGovernmentEnvironmental monitoringMining and manufacturingOthersApplicationSurveyingMedicine and public safetyMilitary intelligenceDisaster risk reduction and managementOthersTypeSurface and field analyticsGeovisualizationNetwork and location analyticsOthersGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKAPACChinaIndiaJapanSouth AmericaBrazilRest of World (ROW)

By Technology Insights

The gps segment is estimated to witness significant growth during the forecast period.The market encompasses various applications and technologies, including geospatial optimization, data enrichment, location-based services (LBS), spatial data standards, public safety, geospatial software, resource management, location intelligence, geospatial data visualization, geospatial services, data integration, 3D visualization, satellite imagery, remote sensing, GIS platforms, spatial data infrastructure, aerial photography, route optimization, data cleansing, precision agriculture, spatial interpolation, geospatial databases, transportation planning, spatial data accuracy, spatial analysis, map projections, interactive maps, marketing analytics, d

Dataset

This dataset was created by jembovski

Released under CC0: Public Domain

Contents

Geospatial datasets from The Regional Centre for Mapping of Resources for Development (RCMRD)

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. Vegetation map development for KNRI has somewhat different protocols than for other Parks. Normally photointerpretation is preceded by extensive field work which includes plot selection and vegetation sampling using detailed descriptions which are subsequently analyzed using ordination and other statistical techniques. The data are then summarized and association descriptions are assigned to each plot or, if the association is previously unrecognized, then a new association name is assigned. Subsequently, the plots locations are compared to its photographic signature and a photointerpretive key is developed. Given the very small size of KNRI and the extensive historical impact and alteration of the vegetation a simplified technique was used. NatureServe developed a list of potential vegetation types prior to any field work. This list was referenced during the field visit and modified after comparison of site characteristics and vegetation descriptions. Aerial photographs were viewed prior to the field visit and areas of like signature were differentiated. All vegetation and land-use information was then transferred to a GIS database using the latest grayscale USGS digital orthophoto quarter-quads as the base map and using a combination of on-screen digitizing and scanning techniques. Overall thematic map accuracy for the Park is considered 100% as all interpreted polygons received a filed visit for verification.

Geospatial Analytics Market is Segmented by Component (Software, Services, and Hardware), Analysis Type (Surface Analysis, Network Analysis, and More), Deployment Model (On-Premises and Cloud), End-User Vertical (Government, Defense and Intelligence and More), and by Geography. The Market Forecasts are Provided in Terms of Value (USD).

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

Progress Needed on Identifying Expenditures, Building and Utilizing a Data Infrastructure, and Reducing Duplicative Efforts The federal government collects, maintains, and uses geospatial information—data linked to specific geographic locations—to help support varied missions, including national security and natural resources conservation. To coordinate geospatial activities, in 1994 the President issued an executive order to develop a National Spatial Data Infrastructure—a framework for coordination that includes standards, data themes, and a clearinghouse. GAO was asked to review federal and state coordination of geospatial data. GAO’s objectives were to (1) describe the geospatial data that selected federal agencies and states use and how much is spent on geospatial data; (2) assess progress in establishing the National Spatial Data Infrastructure; and (3) determine whether selected federal agencies and states invest in duplicative geospatial data. To do so, GAO identified federal and state uses of geospatial data; evaluated available cost data from 2013 to 2015; assessed FGDC’s and selected agencies’ efforts to establish the infrastructure; and analyzed federal and state datasets to identify duplication. What GAO Found Federal agencies and state governments use a variety of geospatial datasets to support their missions. For example, after Hurricane Sandy in 2012, the Federal Emergency Management Agency used geospatial data to identify 44,000 households that were damaged and inaccessible and reported that, as a result, it was able to provide expedited assistance to area residents. Federal agencies report spending billions of dollars on geospatial investments; however, the estimates are understated because agencies do not always track geospatial investments. For example, these estimates do not include billions of dollars spent on earth-observing satellites that produce volumes of geospatial data. The Federal Geographic Data Committee (FGDC) and the Office of Management and Budget (OMB) have started an initiative to have agencies identify and report annually on geospatial-related investments as part of the fiscal year 2017 budget process. FGDC and selected federal agencies have made progress in implementing their responsibilities for the National Spatial Data Infrastructure as outlined in OMB guidance; however, critical items remain incomplete. For example, the committee established a clearinghouse for records on geospatial data, but the clearinghouse lacks an effective search capability and performance monitoring. FGDC also initiated plans and activities for coordinating with state governments on the collection of geospatial data; however, state officials GAO contacted are generally not satisfied with the committee’s efforts to coordinate with them. Among other reasons, they feel that the committee is focused on a federal perspective rather than a national one, and that state recommendations are often ignored. In addition, selected agencies have made limited progress in their own strategic planning efforts and in using the clearinghouse to register their data to ensure they do not invest in duplicative data. For example, 8 of the committee’s 32 member agencies have begun to register their data on the clearinghouse, and they have registered 59 percent of the geospatial data they deemed critical. Part of the reason that agencies are not fulfilling their responsibilities is that OMB has not made it a priority to oversee these efforts. Until OMB ensures that FGDC and federal agencies fully implement their responsibilities, the vision of improving the coordination of geospatial information and reducing duplicative investments will not be fully realized. OMB guidance calls for agencies to eliminate duplication, avoid redundant expenditures, and improve the efficiency and effectiveness of the sharing and dissemination of geospatial data. However, some data are collected multiple times by federal, state, and local entities, resulting in duplication in effort and resources. A new initiative to create a national address database could potentially result in significant savings for federal, state, and local governments. However, agencies face challenges in effectively coordinating address data collection efforts, including statutory restrictions on sharing certain federal address data. Until there is effective coordination across the National Spatial Data Infrastructure, there will continue to be duplicative efforts to obtain and maintain these data at every level of government.https://www.gao.gov/assets/d15193.pdfWhat GAO Recommends GAO suggests that Congress consider assessing statutory limitations on address data to foster progress toward a national address database. GAO also recommends that OMB improve its oversight of FGDC and federal agency initiatives, and that FGDC and selected agencies fully implement initiatives. The agencies generally agreed with the recommendations and identified plans to implement them.

This data contain of: 1. Data of satellite imagery of PlanetScope of University of Brawijaya with 3m spatial resolution. 2. Data training and testing in CSV format 3. R Script of four different algorithms (XGBoost, Random Forest, Support Vector Machine, and Neural Networks) The manuscript that using this dataset has been submitted to F1000 Research (https://f1000research.com/)

The Geopspatial Fabric provides a consistent, documented, and topologically connected set of spatial features that create an abstracted stream/basin network of features useful for hydrologic modeling.The GIS vector features contained in this Geospatial Fabric (GF) data set cover the lower 48 U.S. states, Hawaii, and Puerto Rico. Four GIS feature classes are provided for each Region: 1) the Region outline ("one"), 2) Points of Interest ("POIs"), 3) a routing network ("nsegment"), and 4) Hydrologic Response Units ("nhru"). A graphic showing the boundaries for all Regions is provided at http://dx.doi.org/doi:10.5066/F7542KMD. These Regions are identical to those used to organize the NHDPlus v.1 dataset (US EPA and US Geological Survey, 2005). Although the GF Feature data set has been derived from NHDPlus v.1, it is an entirely new data set that has been designed to generically support regional and national scale applications of hydrologic models. Definition of each type of feature c ...

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.

Geospatial Analytics Market is estimated to be USD 173.47 billion by 2030 is expected to develop at a CAGR of 11.5% during the forecast period

The ANZ Geospatial Analytics market, valued at $0.68 million in 2025, is projected to experience robust growth, driven by increasing adoption across diverse sectors. A Compound Annual Growth Rate (CAGR) of 9.13% from 2025 to 2033 indicates a significant expansion potential. Key drivers include the rising demand for precise location intelligence in agriculture (precision farming), utility management (network optimization), and infrastructure development (real estate and construction). Furthermore, advancements in data analytics techniques, particularly AI and machine learning, are enhancing the capabilities of geospatial analytics, leading to more insightful data analysis and improved decision-making. The market segmentation reveals strong demand across various verticals, with agriculture, utilities, and defense & intelligence leading the way. While data limitations prevent precise regional breakdowns for ANZ, the global trend suggests a significant market presence in Australia and New Zealand, supported by robust government initiatives and private sector investments in digital infrastructure. The presence of established players like CoreLogic NZ Limited and Esri Australia, alongside emerging innovative companies, contributes to the market’s dynamism and future potential. The forecast period (2025-2033) presents substantial opportunities for market expansion, particularly as businesses increasingly recognize the strategic value of location-based insights. Government initiatives promoting the use of geospatial data for better resource management, infrastructure planning, and disaster response are further catalyzing market growth. Challenges include data security concerns, integration complexities across different platforms, and the need for skilled professionals to handle and interpret geospatial data. However, the overall market outlook remains positive, fueled by ongoing technological advancements and a growing awareness of the benefits derived from geospatial analytics in driving operational efficiency and informed decision-making across diverse industry sectors within Australia and New Zealand. This report provides a detailed analysis of the Australia and New Zealand (ANZ) geospatial analytics market, offering invaluable insights for businesses operating or planning to enter this dynamic sector. With a study period spanning 2019-2033, a base year of 2025, and a forecast period of 2025-2033, this report offers a comprehensive overview of market size, trends, and future projections, valued in millions. The report leverages historical data (2019-2024) to paint a robust picture of market evolution. Recent developments include: January 2023: Ecopia AI (Ecopia) and Woolpert announced an expanded collaboration to map Australia's top metropolitan areas in 3D. The resulting vector maps will offer Woolpert's Asia-Pacific clients an accurate, detailed, and up-to-date foundational layer of geospatial data representing the dimensional world. As one of the leading geospatial services providers, Woolpert works with commercial and government organizations alike to map and analyze locations for strategic decision-making., September 2022: Wellington-based Geospatial data, technology, and analytics company Lynker Analytics announced that it had been selected by Toitū Te Whenua Land Information New Zealand in order to capture the building outlines from publicly owned aerial imagery over the next three years. Toitū Te Whenua Land Information New Zealand maintains a national open dataset of the building outlines extracted from multiple years of imagery captured through airborne sensors.. Key drivers for this market are: Growing Demand for Geospatial Analytics in Smart City Development and Urban Planning, Integration of Advanced Technologies such as AI and ML in Geospatial Analytics Solutions. Potential restraints include: Higher Costs Associated With Geospatial Analytics Solutions. Notable trends are: Agriculture Segment is Anticipated to Hold Significant Market Share.

Overview

Empower your location data visualizations with our edge-matched polygons, even in difficult geographies.

Our self-hosted GIS data cover administrative and postal divisions with up to 6 precision levels: a zip code layer and up to 5 administrative levels. All levels follow a seamless hierarchical structure with no gaps or overlaps.

The geospatial data shapes are offered in high-precision and visualization resolution and are easily customized on-premise.

Use cases for the Global Boundaries Database (GIS data, Geospatial data)

• In-depth spatial analysis

• Clustering

• Geofencing

• Reverse Geocoding

• Reporting and Business Intelligence (BI)

Product Features

• Coherence and precision at every level

• Edge-matched polygons

• High-precision shapes for spatial analysis

• Fast-loading polygons for reporting and BI

• Multi-language support

For additional insights, you can combine the GIS data with:

• Population data: Historical and future trends

• UNLOCODE and IATA codes

• Time zones and Daylight Saving Time (DST)

Data export methodology

Our geospatial data packages are offered in variable formats, including - .shp - .gpkg - .kml - .shp - .gpkg - .kml - .geojson

All GIS data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Why companies choose our map data

• Precision at every level

• Coverage of difficult geographies

• No gaps, nor overlaps

Note: Custom geospatial data packages are available. Please submit a request via the above contact button for more details.

Abstract: Geospatial data is urgently needed in decision-making processes to achieve Sustainable Development Goals (SDGs) at global, national, regional and local scales. While the advancement of geo-technologies to obtain or produce geospatial data has become faster and more affordable, many countries in the global south still experience a geospatial data scarcity at the rural level due to complex geographical terrains, weak coordination among institutions and a lack of knowledge and technologies to produce visualised geospatial data like maps. We proposed a collaborative spatial learning framework that integrates the spatial knowledge of stakeholders to obtain geospatial data. By conducting participatory mapping workshops in three villages in the Deli Serdang district in Indonesia, we tested the framework in terms of facilitating communication and collaboration of the village stakeholders while also supporting knowledge co-production and social learning among them. Satellite images were used in digital and non-digital mapping workshops to support village stakeholders to produce proper village maps while fulfilling the SDGs? emphasis to make geospatial data available through a participatory approach. Date Accepted: 2020-08-19

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 vegetation map for Pecos National Historical Park 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 legend of ecologically based vegetation map units was developed using the vegetation classification described in Chapter 2 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 series of automated image segmentation and supervised image classifications were conducted, followed by fine-scale map refinement using direct image interpretation and manual editing. 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 physical characteristics