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GIS Market Size 2025-2029

The GIS market size is forecast to increase by USD 24.07 billion, at a CAGR of 20.3% between 2024 and 2029.

The Global Geographic Information System (GIS) market is experiencing significant growth, driven by the increasing integration of Building Information Modeling (BIM) and GIS technologies. This convergence enables more effective spatial analysis and decision-making in various industries, particularly in soil and water management. However, the market faces challenges, including the lack of comprehensive planning and preparation leading to implementation failures of GIS solutions. Companies must address these challenges by investing in thorough project planning and collaboration between GIS and BIM teams to ensure successful implementation and maximize the potential benefits of these advanced technologies.
By focusing on strategic planning and effective implementation, organizations can capitalize on the opportunities presented by the growing adoption of GIS and BIM technologies, ultimately driving operational efficiency and innovation.

What will be the Size of the GIS 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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The global Geographic Information Systems (GIS) market continues to evolve, driven by the increasing demand for advanced spatial data analysis and management solutions. GIS technology is finding applications across various sectors, including natural resource management, urban planning, and infrastructure management. The integration of Bing Maps, terrain analysis, vector data, Lidar data, and Geographic Information Systems enables precise spatial data analysis and modeling. Hydrological modeling, spatial statistics, spatial indexing, and route optimization are essential components of GIS, providing valuable insights for sectors such as public safety, transportation planning, and precision agriculture. Location-based services and data visualization further enhance the utility of GIS, enabling real-time mapping and spatial analysis.

The ongoing development of OGC standards, spatial data infrastructure, and mapping APIs continues to expand the capabilities of GIS, making it an indispensable tool for managing and analyzing geospatial data. The continuous unfolding of market activities and evolving patterns in the market reflect the dynamic nature of this technology and its applications.

How is this GIS Industry segmented?

The GIS 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


Type

  Telematics and navigation
  Mapping
  Surveying
  Location-based services


Device

  Desktop
  Mobile


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa

    UAE


  APAC

    China
    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.

The Global Geographic Information System (GIS) market encompasses a range of applications and technologies, including raster data, urban planning, geospatial data, geocoding APIs, GIS services, routing APIs, aerial photography, satellite imagery, GIS software, geospatial analytics, public safety, field data collection, transportation planning, precision agriculture, OGC standards, location intelligence, remote sensing, asset management, network analysis, spatial analysis, infrastructure management, spatial data standards, disaster management, environmental monitoring, spatial modeling, coordinate systems, spatial overlay, real-time mapping, mapping APIs, spatial join, mapping applications, smart cities, spatial data infrastructure, map projections, spatial databases, natural resource management, Bing Maps, terrain analysis, vector data, Lidar data, and geographic information systems.

The software segment includes desktop, mobile, cloud, and server solutions. Open-source GIS software, with its industry-specific offerings, poses a challenge to the market, while the adoption of cloud-based GIS software represents an emerging trend. However, the lack of standardization and interoperability issues hinder the widespread adoption of cloud-based solutions. Applications in sectors like public safety, transportation planning, and precision agriculture are driving market growth. Additionally, advancements in technologies like remote sensing, spatial modeling, and real-time mapping are expanding the market's scope.

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The Software segment was valued at USD 5.06 billion in 2019

Topography, isopach, base elevation, and tops data for various regions and depths in Kansas. The data has been generated by Kansas Geological Survey scientists, but no complete metadata about processes, data sources, or other useful information is available. For additional details about the regional geology layers, please see these presentation slides- https://www.kgs.ku.edu/PRS/publication/2013/Geologic_history_of_KS_2013.pdf.

Geographic Information System (GIS) Market Outlook

The Geographic Information System (GIS) market is witnessing robust growth with its global market size projected to reach USD 25.7 billion by 2032, up from USD 8.7 billion in 2023, at a compound annual growth rate (CAGR) of 12.4% during the forecast period. This growth is primarily driven by the increasing integration of GIS technology across various industries to improve spatial data visualization, enhance decision-making, and optimize operations. The benefits offered by GIS in terms of accuracy, efficiency, and cost-effectiveness are convincing more sectors to adopt these systems, thereby expanding the market size significantly.

A major growth factor contributing to the GIS market expansion is the escalating demand for location-based services. As businesses across different sectors recognize the importance of spatial data analytics in driving strategic decisions, the reliance on GIS applications is becoming increasingly pronounced. The rise in IoT devices, coupled with the enhanced capabilities of AI and machine learning, has further fueled the demand for GIS solutions. These technologies enable the processing and analysis of large volumes of spatial data, thereby providing valuable insights that businesses can leverage for competitive advantage. In addition, government initiatives promoting the adoption of digital infrastructure and smart city projects are playing a crucial role in the growth of the GIS market.

The advancement in satellite imaging and remote sensing technologies is another key driver of the GIS market growth. With enhanced satellite capabilities, the precision and quality of geospatial data have significantly improved, making GIS applications more reliable and effective. The availability of high-resolution satellite imagery has opened new avenues in various sectors including agriculture, urban planning, and disaster management. Moreover, the decreasing costs of satellite data acquisition and the proliferation of drone technology are making GIS more accessible to small and medium enterprises, further expanding the market potential.

The advent of 3D Geospatial Technologies is revolutionizing the way industries utilize GIS data. By providing a three-dimensional perspective, these technologies enhance spatial analysis and visualization, offering more detailed and accurate representations of geographical areas. This advancement is particularly beneficial in urban planning, where 3D models can simulate cityscapes and infrastructure, allowing planners to visualize potential developments and assess their impact on the environment. Moreover, 3D geospatial data is proving invaluable in sectors such as construction and real estate, where it aids in site analysis and project planning. As these technologies continue to evolve, they are expected to play a pivotal role in the future of GIS, expanding its applications and driving further market growth.

Furthermore, the increasing application of GIS in environmental monitoring and management is bolstering market growth. With growing concerns over climate change and environmental degradation, GIS is being extensively used for resource management, biodiversity conservation, and natural disaster risk management. This trend is expected to continue as more organizations and governments prioritize sustainability, thereby driving the demand for advanced GIS solutions. The integration of GIS with other technologies such as big data analytics, and cloud computing is also expected to enhance its capabilities, making it an indispensable tool for environmental management.

Regionally, North America is currently leading the GIS market, driven by the widespread adoption of advanced technologies and the presence of major GIS vendors. The regionÂ’s focus on infrastructure development and smart city projects is further propelling the market growth. Europe is also witnessing significant growth owing to the increasing adoption of GIS in various industries such as agriculture and transportation. The Asia Pacific region is anticipated to exhibit the highest CAGR during the forecast period, attributed to rapid urbanization, government initiatives for digital transformation, and increasing investments in infrastructure development. In contrast, the markets in Latin America and the Middle East & Africa are growing steadily as these regions continue to explore and adopt GIS technologies.

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This layer shows culverts across Southeast Alaska that are the highest ranking sites from a calculated prioritization scores. The prioritization was created from weighted calculation if several factors. This layer is the top 30 selection of all prioritized culverts of Southeast, the full layer of all culverts can be found here, and the associated prioritization web map here.With this layer, a web application has been created to have a dynamic prioritization tool to look at the top existing culvert projects for any given geographic extent in Southeast Alaska.

NFCorpus: 20 generated queries (BEIR Benchmark)

This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.

DocT5query model used: BeIR/query-gen-msmarco-t5-base-v1 id (str): unique document id in NFCorpus in the BEIR benchmark (corpus.jsonl). Questions generated: 20 Code used for generation: evaluate_anserini_docT5query_parallel.py

Below contains the old dataset card for the BEIR benchmark.

  Dataset Card for BEIR… See the full description on the dataset page: https://huggingface.co/datasets/income/cqadupstack-gis-top-20-gen-queries.

This is a raster-based suitability map of landfill sites produced after the February 6, 2023, Türkiye earthquakes centred on Kahramanmaraş - Pazarcık and Kahramanmaraş - Elbistan. In this study, a site selection model was developed using open-source Geographic Information Systems (GIS) software and the Best-Worst Method (BWM), one of the Multi-Criteria Decision-Making Methods, to determine the most suitable landfill areas immediately after the earthquake.The suitability map of the landfill sites can be accessed through the Serverless Cloud-GIS based Disaster Management Portal at https://web.itu.edu.tr/metemu/nominal/deprem.htmlThe pairwise comparison matrix, weight calculation, and sensitivity analysis are also provided in the MS Excel file.

The lower Paleogene Wilcox Group crops out around the northern edge of the Gulf of Mexico Basin and is a major coal-bearing unit and a primary oil and gas producer in the lower Paleogene section of the Gulf Coast region. The outcrop distribution of the Wilcox Group and other coal-bearing strata of the Gulf Coast region was compiled as part of a U.S. Geological Survey National Coal Assessment (Warwick and others, 1997). A structure contour map of the top of the Wilcox Group was constructed as part of a U.S. Geological Survey Petroleum Systems and Geologic Assessment of Oil and Gas of the northern Gulf of Mexico coastal region (Warwick, 2017). This surface, mainly constructed using data from oil and gas wells, depicts the overall configuration of the Wilcox Group near the outcrop belt, within the Mississippi Embayment, and near the present-day coastline where the Wilcox Group crosses over the Lower Cretaceous shelf margin in the subsurface. The structure contour map of the top of the Wilcox Group was used to help define the thermal maturity of a specific source-rock interval as part of the oil and gas assessment. This digital data release captures in digital form the mapped outcrop distribution and structural configuration of the Wilcox Group from the previously published U.S. Geological Survey assessment-related studies of the Gulf Coast region (Warwick and others, 1997; Warwick, 2017). Both the geologic map polygons and structure contours were digitized and attributed as GIS data sets so that these data could be used in digital form as part of U.S. Geological Survey and other studies of the region.

The NG9-1-1 GIS Guidelines and Best Practices Manual is a cooperative effort between the Arizona 9-1-1 Program, the AGIC 9-1-1 Committee and the Arizona 9-1-1 Program for the 9-1-1 System Administrators and GIS practitioners at different levels such as local government, tribal, County government, metropolitan planning organizations (MPO), whom support any of the 9-1-1 systems.Community. Initial Document created in 2017, Version 2 updated in 2020 based on new NENA standards and GIS platforms in Arizona.

This geodatabase includes spatial datasets that represent the Mississippian aquifer in the States of Alabama, Illinois, Indiana, Iowa, Kentucky, Maryland, Missouri, Ohio, Pennsylvania, Tennessee, Virginia and West Virginia. The aquifer is divided into three subareas, based on the data availability. In subarea 1 (SA1), which is the aquifer extent in Iowa, data exist of the aquifer top altitude and aquifer thickness. In subarea 2 (SA2), which is the aquifer extent in Missouri, data exist of the aquifer top and bottom aquifer surface altitudes. In subarea 3 (SA3), which is the aquifer area of the remaining States, no altitude or thickness data exist. Included in this geodatabase are: (1) a feature dataset "ds40MSSPPI_altitude_and_thickness_contours that includes aquifer altitude and thickness contours used to generate the surface rasters for SA1 and SA2, (2) a feature dataset "ds40MSSPPI_extents" that includes a polygon dataset that represents the subarea extents, a polygon dataset that represents the combined overall aquifer extent, and a polygon dataset of the Ft. Dodge Fault and Manson Anomaly, (3) raster datasets that represent the altitude of the top and the bottom of the aquifer in SA1 and SA2, and (4) georeferenced images of the figures that were digitized to create the aquifer top- and bottom-altitude contours or aquifer thickness contours for SA1 and SA2. The images and digitized contours are supplied for reference. The extent of the Mississippian aquifer for all subareas was produced from the digital version of the HA-730 Mississippian aquifer extent, (USGS HA-730). For the two Subareas with vertical-surface information, SA1 and SA2, data were retrieved from the sources as described below. 1. The aquifer-altitude contours for the top and the aquifer-thickness contours for the top-to-bottom thickness of SA1 were received in digital format from the Iowa Geologic Survey. The URL for the top was ftp://ftp.igsb.uiowa.edu/GIS_Library/IA_State/Hydrologic/Ground_Waters/ Mississippian_aquifer/mississippian_topography.zip. The URL for the thickness was ftp://ftp.igsb.uiowa.edu/GIS_Library/IA_State/Hydrologic/Ground_Waters/ Mississippian_aquifer/mississippian_isopach.zip Reference for the top map is Altitude and Configuration, in feet above mean sea level, of the Mississipian Aquifer modified from a scanned image of Map 1, Sheet 1, Miscellaneous Map Series 3, Mississippian Aquifer of Iowa by P.J. Horick and W.L. Steinhilber, Iowa Geological Survey, 1973; IGS MMS-3, Map 1, Sheet 1 Reference for the thickness map is Distribution and isopach thickness, in feet, of the Mississipian Aquifer, modified from a scanned image of Map 1, Sheet 2, Miscellaneous Map Series 3, Mississippian Aquifer of Iowa by P.J. Horick and W.L. Steinhilber, Iowa Geological Survey, 1973; IGS MMS-3, Map 1, Sheet 2 The altitude contours for the top and bottom of SA2 were digitized from georeferenced figures of altitude contours in U.S. Geological Survey Professional Paper 1305 (USGS PP1305), figure 6 (for the top surface) and figure 9 (for the bottom surface). The altitude contours for SA1 and SA2 were interpolated into surface rasters within a GIS using tools that create hydrologically correct surfaces from contour data, derive the altitude from the thickness (depth from the land surface), and merge the subareas into a single surface. The primary tool was an enhanced version of "Topo to Raster" used in ArcGIS, ArcMap, Esri 2014. ArcGIS Desktop: Release 10.2 Redlands, CA: Environmental Systems Research Institute. The raster surfaces were corrected in areas where the altitude of the top of the aquifer exceeded the land surface, and where the bottom of an aquifer exceeded the altitude of the corrected top of the aquifer.

Learn state-of-the-art skills to build compelling, useful, and fun Web GIS apps easily, with no programming experience required.Building on the foundation of the previous three editions, Getting to Know Web GIS, fourth edition,features the latest advances in Esri’s entire Web GIS platform, from the cloud server side to the client side.Discover and apply what’s new in ArcGIS Online, ArcGIS Enterprise, Map Viewer, Esri StoryMaps, Web AppBuilder, ArcGIS Survey123, and more.Learn about recent Web GIS products such as ArcGIS Experience Builder, ArcGIS Indoors, and ArcGIS QuickCapture. Understand updates in mobile GIS such as ArcGIS Collector and AuGeo, and then build your own web apps.Further your knowledge and skills with detailed sections and chapters on ArcGIS Dashboards, ArcGIS Analytics for the Internet of Things, online spatial analysis, image services, 3D web scenes, ArcGIS API for JavaScript, and best practices in Web GIS.Each chapter is written for immediate productivity with a good balance of principles and hands-on exercises and includes:A conceptual discussion section to give you the big picture and principles,A detailed tutorial section with step-by-step instructions,A Q/A section to answer common questions,An assignment section to reinforce your comprehension, andA list of resources with more information.Ideal for classroom lab work and on-the-job training for GIS students, instructors, GIS analysts, managers, web developers, and other professionals, Getting to Know Web GIS, fourth edition, uses a holistic approach to systematically teach the breadth of the Esri Geospatial Cloud.AUDIENCEProfessional and scholarly. College/higher education. General/trade.AUTHOR BIOPinde Fu leads the ArcGIS Platform Engineering team at Esri Professional Services and teaches at universities including Harvard University Extension School. His specialties include web and mobile GIS technologies and applications in various industries. Several of his projects have won specialachievement awards. Fu is the lead author of Web GIS: Principles and Applications (Esri Press, 2010).Pub Date: Print: 7/21/2020 Digital: 6/16/2020 Format: Trade paperISBN: Print: 9781589485921 Digital: 9781589485938 Trim: 7.5 x 9 in.Price: Print: $94.99 USD Digital: $94.99 USD Pages: 490TABLE OF CONTENTSPrefaceForeword1 Get started with Web GIS2 Hosted feature layers and storytelling with GIS3 Web AppBuilder for ArcGIS and ArcGIS Experience Builder4 Mobile GIS5 Tile layers and on-premises Web GIS6 Spatial temporal data and real-time GIS7 3D web scenes8 Spatial analysis and geoprocessing9 Image service and online raster analysis10 Web GIS programming with ArcGIS API for JavaScriptPinde Fu | Interview with Esri Press | 2020-07-10 | 15:56 | Link.

This submission contains an ESRI map package (.mpk) with an embedded geodatabase for GIS resources used or derived in the Nevada Machine Learning project, meant to accompany the final report. The package includes layer descriptions, layer grouping, and symbology. Layer groups include: new/revised datasets (paleo-geothermal features, geochemistry, geophysics, heat flow, slip and dilation, potential structures, geothermal power plants, positive and negative test sites), machine learning model input grids, machine learning models (Artificial Neural Network (ANN), Extreme Learning Machine (ELM), Bayesian Neural Network (BNN), Principal Component Analysis (PCA/PCAk), Non-negative Matrix Factorization (NMF/NMFk) - supervised and unsupervised), original NV Play Fairway data and models, and NV cultural/reference data.

See layer descriptions for additional metadata. Smaller GIS resource packages (by category) can be found in the related datasets section of this submission. A submission linking the full codebase for generating machine learning output models is available through the "Related Datasets" link on this page, and contains results beyond the top picks present in this compilation.

The Global Imagery Browse Services (GIBS) system is a core EOSDIS component which provides a scalable, responsive, highly available, and community standards based set of imagery services. These services are designed with the goal of advancing user interactions with EOSDIS’ inter-disciplinary data through enhanced visual representation and discovery.GIBS Available Imagery ProductsThe GIBS imagery archive includes approximately 1000 imagery products representing visualized science data from the NASA Earth Observing System Data and Information System (EOSDIS). Each imagery product is generated at the native resolution of the source data to provide "full resolution" visualizations of a science parameter. GIBS works closely with the science teams to identify the appropriate data range and color mappings, where appropriate, to provide the best quality imagery to the Earth science community. Many GIBS imagery products are generated by the EOSDIS LANCE near real-time processing system resulting in imagery available in GIBS within 3.5 hours of observation. These products and others may also extend from present to the beginning of the satellite mission. In addition, GIBS makes available supporting imagery layers such as data/no-data, water masks, orbit tracks, and graticules to improve imagery usage.The GIBS team is actively engaging the NASA EOSDIS Distributed Active Archive Centers (DAACs) to add more imagery products and to extend their coverage throughout the life of the mission. The remainder of this page provides a structured view of the layers currently available within GIBS grouped by science discipline and science observation. For information regarding how to access these products, see the GIBS API section of this wiki. For information regarding how to access these products through an existing client, refer to the Map Library and GIS Client sections of this wiki. If you are aware of a science parameter that you would like to see visualized, please contact us at support@earthdata.nasa.gov. https://wiki.earthdata.nasa.gov/display/GIBS/GIBS+Available+Imagery+Products#expand-AerosolOpticalDepth29ProductsNASA GIS API for Developers https://wiki.earthdata.nasa.gov/display/GIBS/GIBS+API+for+Developers

The top of IAS grid was created by using Florida Geological Survey well core and cuttings data. The surface was interpolated by using the kriging method from the ArcGIS Geostatistical Analyst package.

This dataset represents a water shortage vulnerability analysis performed by DWR using modified PLSS sections pulled from the Well Completion Report PLSS Section Summaries. The attribute table includes water shortage vulnerability indicators and scores from an analysis done by CA Department of Water Resources, joined to modified PLSS sections. Several relevant summary statistics from the Well Completion Reports are included in this table as well. This data is from the 2024 analysis.

Water Code Division 6 Part 2.55 Section 8 Chapter 10 (Assembly Bill 1668) effectively requires California Department of Water Resources (DWR), in consultation with other agencies and an advisory group, to identify small water suppliers and “rural communities” that are at risk of drought and water shortage. Following legislation passed in 2021 and signed by Governor Gavin Newsom, the Water Code Division 6, Section 10609.50 through 10609.80 (Senate Bill 552 of 2021) effectively requires the California Department of Water Resources to update the scoring and tool periodically in partnership with the State Water Board and other state agencies. This document describes the indicators, datasets, and methods used to construct this deliverable.  This is a statewide effort to systematically and holistically consider water shortage vulnerability statewide of rural communities, focusing on domestic wells and state small water systems serving between 4 and 14 connections. The indicators and scoring methodology will be revised as better data become available and stake-holders evaluate the performance of the indicators, datasets used, and aggregation and ranking method used to aggregate and rank vulnerability scores. Additionally, the scoring system should be adaptive, meaning that our understanding of what contributes to risk and vulnerability of drought and water shortage may evolve. This understanding may especially be informed by experiences gained while navigating responses to future droughts.”

A spatial analysis was performed on the 2020 Census Block Groups, modified PLSS sections, and small water system service areas using a variety of input datasets related to drought vulnerability and water shortage risk and vulnerability. These indicator values were subsequently rescaled and summed for a final vulnerability score for the sections and small water system service areas. The 2020 Census Block Groups were joined with ACS data to represent the social vulnerability of communities, which is relevant to drought risk tolerance and resources. These three feature datasets contain the units of analysis (modified PLSS sections, block groups, small water systems service areas) with the model indicators for vulnerability in the attribute table. Model indicators are calculated for each unit of analysis according to the Vulnerability Scoring documents provided by Julia Ekstrom (Division of Regional Assistance).

All three feature classes are DWR analysis zones that are based off existing GIS datasets. The spatial data for the sections feature class is extracted from the Well Completion Reports PLSS sections to be aligned with the work and analysis that SGMA is doing. These are not true PLSS sections, but a version of the projected section lines in areas where there are gaps in PLSS. The spatial data for the Census block group feature class is downloaded from the Census. ACS (American Communities Survey) data is joined by block group, and statistics calculated by DWR have been added to the attribute table. The spatial data for the small water systems feature class was extracted from the State Water Resources Control Board (SWRCB) SABL dataset, using a definition query to filter for active water systems with 3000 connections or less. None of these datasets are intended to be the authoritative datasets for representing PLSS sections, Census block groups, or water service areas. The spatial data of these feature classes is used as units of analysis for the spatial analysis performed by DWR.

These datasets are intended to be authoritative datasets of the scoring tools required from DWR according to Senate Bill 552. Please refer to the Drought and Water Shortage Vulnerability Scoring: California's Domestic Wells and State Smalls Systems documentation for more information on indicators and scoring. These estimated indicator scores may sometimes be calculated in several different ways, or may have been calculated from data that has since be updated. Counts of domestic wells may be calculated in different ways. In order to align with DWR SGMO's (State Groundwater Management Office) California Groundwater Live dashboards, domestic wells were calculated using the same query. This includes all domestic wells in the Well Completion Reports dataset that are completed after 12/31/1976, and have a 'RecordType' of 'WellCompletion/New/Production or Monitoring/NA'.

Please refer to the Well Completion Reports metadata for more information. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standard version 3.4, dated September 14, 2022. DWR makes no warranties or guarantees — either expressed or implied— as to the completeness, accuracy, or correctness of the data.

DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to GIS@water.ca.gov.

This geodatabase includes spatial datasets that represent the Basin and Range basin-fill aquifers in the States of Arizona, California, Idaho, Nevada, New Mexico, Oregon, and Utah. Included are:

    (1) polygon extents; datasets that represent the aquifer system extent, the entire extent subdivided into subareas or subunits, and any polygon extents of special interest (outcrop areas, no data available, areas underlying other aquifers, anomalies, for example), 
    (2) contours: thickness contours used to generate the surface rasters in subarea 4 (Arizona), 
    (3) modified source raster datasets for subareas 1 and 3, 
    (4) corrected altitudes of top and bottom surface rasters of the entire aquifer. The thickness contours and modified surface rasters are supplied for reference.

The extent of the Basin and Range basin-fill aquifer is from the linework of the Basin and Range aquifer extent maps in U.S. Geological Survey Hydrologic Atlas 730 Chapters B and C, and a digital version of the aquifer extent presented in the Groundwater Atlas of the United States (the U.S. Geological Survey Hydrologic Atlas.

The Basin and Range basin-fill aquifer has no aquifer subunits, but is defined by five subareas: 1. Subarea 1 is the area that overlies the Basin and Range Carbonate aquifer, which was the subject of U.S. Geological Survey Scientific Investigations Report 2010-5193 (USGS SIR 2010-5193).

2. Subarea 2 is the area of a different aquifer system, which is set to null for use within the Basin and Range basin-fill aquifer from U.S. Geological Survey Principal Aquifers, 2003 (USGS Circular 1323, Figure 2)

3. Subarea 3 is the area of the Basin and Range basin-fill aquifer that was the subject of U.S. Geological Survey Geophysical Map 1012 (USGS GP-1012) and not covered by USGS SIR 2010-5193 or the Basin and Range basin-fill aquifer in Arizona, Arizona Geological Survey, Digital Geological Map 52 (AZGS DGM-52). Top of aquifer is land surface. USGS GP-1012 dataset is depth from land surface to basin bottom.

4. Subarea 4 is the area of the 01BSNRGB aquifer in Arizona, (AZGS DGM-52)

5. Subarea 5 areas are in the Basin and Range basin-fill extent areas that do not have top/bot defined.

The resultant top and bottom surface rasters for each subarea were merged into surface rasters of the top and bottom of the entire Basin and Range basin-fill aquifer within a GIS using tools that create hydrologically correct surfaces from contour data, deriving the altitude from the thickness (depth from the land surface), and merging the subareas into a single surface. The primary tools were a version of "Topo to Raster", and "Mosaic to New Raster" used in ArcGIS, ArcMap, Esri 2014.

The top 200 locations where reported collisions occurred at intersections have been identified. The crash cluster analysis methodology for the top intersection clusters uses a fixed meter search distance of 25 meters (82 ft.) to merge crash clusters together. This analysis was based on crashes where a police officer specified one of the following junction types: Four way intersection, T-intersection, Y-intersection, five point or more. Furthermore, the methodology uses the Equivalent Property Damage Only (EPDO) weighting to rank the clusters. EPDO is based any type of injury crash (including fatal, incapacitating, non-incapacitating and possible) having a weighting of 21 compared to a property damage only crash (which has weighting of 1). The clusters were reviewed in descending EPDO order until 200 locations were obtained. The clustering analysis used crashes from the three year period from 2016-2018. The area encompassing the crash cluster may cover a larger area than just the intersection so it is critical to view these spatially.

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.

U.S. Census Bureau 2020 block groups within the City of Seattle with American Community Survey (ACS) 5-year series data of frequently requested topics. Data is pulled from block group tables for the most recent ACS vintage. Seattle neighborhood geography of Council Districts, Comprehensive Plan Growth Areas are also included based on block group assignment.The census block groups have been assigned to a neighborhood based on the distribution of the total population from the 2020 decennial census for the component census blocks. If the majority of the population in the block group were inside the boundaries of the neighborhood, the block group was assigned wholly to that neighborhood.Feature layer created for and used in the Neighborhood Profiles application.The attribute data associated with this map is updated annually to contain the most currently released American Community Survey (ACS) 5-year data and contains estimates and margins of error. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Vintages: 2023ACS Table(s): Select fields from the tables listed here.Data downloaded from: Census Bureau's Explore Census Data <div style='font-family:inher

These show the locations of open schools for the 2015-16 school year for the Detroit Tri-County area. State of Michigan Top to Bottom scores are attached to schools where available. Top to Bottom ranks come from MISchoolData.org