The global GIS mapping tools market is experiencing robust growth, driven by increasing demand across diverse sectors. The market, estimated at $15 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033, reaching approximately $28 billion by 2033. This growth is fueled by several key factors. Firstly, the burgeoning adoption of cloud-based solutions offers scalability, cost-effectiveness, and enhanced accessibility to a wider user base, including small and medium-sized enterprises (SMEs). Secondly, the escalating need for precise spatial data analysis in various applications, such as urban planning, geological exploration, and water resource management, is significantly boosting market demand. The increasing integration of GIS with other technologies like AI and IoT further amplifies its capabilities, leading to more sophisticated applications and increased market penetration. Finally, government initiatives promoting digitalization and smart city development across the globe are indirectly fueling this market expansion. However, certain restraints limit market growth. The high initial investment cost for advanced GIS software and the requirement for skilled professionals to operate these systems can be a barrier, especially for smaller organizations. Additionally, data security and privacy concerns related to the handling of sensitive geographical information pose challenges to wider adoption. Market segmentation reveals strong growth in the cloud-based GIS segment, driven by its inherent advantages, while applications in urban planning and geological exploration lead the application-based segmentation. North America and Europe currently hold significant market shares, with strong growth potential in the Asia-Pacific region due to increasing infrastructure development and government investments. Leading companies like Esri, Hexagon, and Autodesk are shaping the market landscape through continuous innovation and competitive pricing strategies, while the emergence of open-source options like QGIS and GRASS GIS provides alternative, cost-effective solutions.

Geoform is a configurable app template for form based data editing of a Feature Service. This application allows users to enter data through a form instead of a map's pop-up while leveraging the power of the Web Map and editable Feature Services. This app geo-enables data and workflows by lowering the barrier of entry for completing simple tasks. Use CasesProvides a form-based experience for entering data through a form instead of a map pop-up. This is a good choice for users who find forms a more intuitive format than pop-ups for entering data.Useful to collect new point data from a large audience of non technical staff or members of the community.Configurable OptionsGeoform has an interactive builder used to configure the app in a step-by-step process. Use Geoform to collect new point data and configure it using the following options:Choose a web map and the editable layer(s) to be used for collection.Provide a title, logo image, and form instructions/details.Control and choose what attribute fields will be present in the form. Customize how they appear in the form, the order they appear in, and add hint text.Select from over 15 different layout themes.Choose the display field that will be used for sorting when viewing submitted entries.Enable offline support, social media sharing, default map extent, locate on load, and a basemap toggle button.Choose which locate methods are available in the form, including: current location, search, latitude and longitude, USNG coordinates, MGRS coordinates, and UTM coordinates.Supported DevicesThis application is responsively designed to support use in browsers on desktops, mobile phones, and tablets.Data RequirementsThis web app includes the capability to edit a hosted feature service or an ArcGIS Server feature service. Creating hosted feature services requires an ArcGIS Online organizational subscription or an ArcGIS Developer account. Get Started This application can be created in the following ways:Click the Create a Web App button on this pageShare a map and choose to Create a Web AppOn the Content page, click Create - App - From Template Click the Download button to access the source code. Do this if you want to host the app on your own server and optionally customize it to add features or change styling.

This packaged data collection contains two sets of two additional model runs that used the same inputs and parameters as our primary model, with the exception being we implemented a "maximum corridor length" constraint that allowed us to identify and visualize the corridors as being well-connected (≤15km) or moderately connected (≤45km). This is based on an assumption that corridors longer than 45km are too long to sufficiently accommodate dispersal. One of these sets is based on a maximum corridor length that uses Euclidean (straight-line) distance, while the other set is based on a maximum corridor length that uses cost-weighted distance. These two sets of corridors can be compared against the full set of corridors from our primary model to identify the remaining corridors, which could be considered poorly connected. This package includes the following data layers: Corridors classified as well connected (≤15km) based on Cost-weighted Distance Corridors classified as moderately connected (≤45km) based on Cost-weighted Distance Corridors classified as well connected (≤15km) based on Euclidean Distance Corridors classified as moderately connected (≤45km) based on Euclidean Distance Please refer to the embedded metadata and the information in our full report for details on the development of these data layers. Packaged data are available in two formats: Geodatabase (.gdb): A related set of file geodatabase rasters and feature classes, packaged in an ESRI file geodatabase. ArcGIS Pro Map Package (.mpkx): The same data included in the geodatabase, presented as fully-symbolized layers in a map. Note that you must have ArcGIS Pro version 2.0 or greater to view. See Cross-References for links to individual datasets, which can be downloaded in raster GeoTIFF (.tif) format.

This Layer is the result of Data pipeline: Alternative Fuel Stations in Los AngelesThe Layer is updated weekly (the underlaying layer covering US and Canada is updated daily) on Sundays around 11 pm.Alternative fuel sources include biodiesel, compressed natural gas, electric, ethanol, hydrogen, liquefied natural gas, propane and renewable diesel. Attributes include the station name, location, access, hours and more. Zoom into the map for more detail.This data is maintained by an Aggregated Live Feed routine that accesses the US Department of Energy's National Renewable Energy Laboratory (NREL) API website.source: NREL Alternate Fuel Stations (ALL)

The global GIS mapping tools market is experiencing robust growth, driven by increasing demand across diverse sectors. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching an estimated market value of approximately $45 billion by 2033. Key drivers include the rising adoption of cloud-based GIS solutions, enhanced data analytics capabilities, the proliferation of location-based services, and the growing need for precise spatial data analysis in various industries like urban planning, geological exploration, and water resource management. The market is segmented by application (Geological Exploration, Water Conservancy Projects, Urban Planning, Others) and type (Cloud-based, Web-based). Cloud-based solutions are gaining significant traction due to their scalability, accessibility, and cost-effectiveness. The increasing availability of high-resolution satellite imagery and advancements in artificial intelligence (AI) and machine learning (ML) are further fueling market expansion. While data security concerns and the high initial investment costs for some advanced solutions present restraints, the overall market outlook remains positive, with significant opportunities for both established players and emerging technology providers. Geographical expansion is another key aspect of market growth. North America and Europe currently hold a significant market share, owing to established GIS infrastructure and early adoption of advanced technologies. However, the Asia-Pacific region is expected to witness rapid growth in the coming years, driven by rising government investments in infrastructure development and increasing urbanization in countries like China and India. Competitive dynamics are shaping the market, with major players like Esri, Autodesk, Hexagon, and Mapbox competing on the basis of software features, data integration capabilities, and customer support. The emergence of open-source GIS solutions like QGIS and GRASS GIS is also challenging the dominance of proprietary software, offering cost-effective alternatives for various applications. The continued development and integration of advanced technologies like 3D mapping, real-time data visualization, and location intelligence will further enhance the capabilities of GIS mapping tools, driving market expansion and innovation across various sectors.

The global satellite remote sensing software market is experiencing robust growth, driven by increasing demand across diverse sectors. While precise figures for market size and CAGR aren't provided, considering the technological advancements and applications in agriculture (precision farming, crop monitoring), water conservancy (flood management, irrigation optimization), forest management (deforestation monitoring, resource assessment), and the public sector (urban planning, disaster response), a conservative estimate places the 2025 market size at approximately $2 billion. This figure reflects the substantial investments in satellite imagery acquisition and analysis capabilities worldwide. The market is further fueled by the rising adoption of cloud-based solutions, enhancing accessibility and scalability of software platforms. Trends such as the integration of AI and machine learning for automated image processing, the proliferation of high-resolution satellite imagery, and the increasing availability of open-source software are accelerating market expansion. However, factors such as the high cost of specialized software licenses and the need for skilled professionals to operate the sophisticated systems act as restraints. The market is segmented by application (agriculture, water conservancy, forest management, public sector, others) and software type (open-source, non-open-source). The North American and European markets currently hold significant shares, but the Asia-Pacific region is witnessing rapid growth due to increasing infrastructure development and government initiatives promoting geospatial technologies. This dynamic market landscape presents lucrative opportunities for both established players and emerging companies in the years to come. The forecast period (2025-2033) anticipates continued growth, with a projected CAGR of approximately 12%, driven by the aforementioned technological advancements and broadening applications across various industry verticals. The competitive landscape is comprised of both major players like ESRI, Trimble, and PCI Geomatica, offering comprehensive suites of software, and smaller, specialized companies focusing on niche applications or open-source solutions. The market is characterized by both proprietary and open-source software options. Open-source solutions like QGIS and GRASS GIS offer cost-effective alternatives, particularly for research and smaller organizations, while commercial solutions provide advanced functionalities and support. The increasing availability of cloud-based solutions is blurring the lines between these segments, with hybrid models emerging that combine the benefits of both. Future growth will be significantly influenced by collaborations between software providers and satellite imagery providers, fostering a more integrated ecosystem and streamlining the data acquisition and processing workflow. The market will continue to benefit from advancements in satellite technology, producing higher-resolution, more frequent, and more affordable imagery.

Alternative Medicine

The Alternative Fueling Stations dataset is updated daily from the National Renewable Energy Laboratory (NREL) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). For more information about the update cycle and data collection methods, please refer to https://afdc.energy.gov/stations/#/find/nearest?show_about=true. This dataset shows all station access types (public and private) and statuses (available, planned, and temporarily unavailable) by default. To view only publicly available stations, use the access and status filters. The U.S. Department of Energy collects these data in partnership with Clean Cities coalitions and their stakeholders to help fleets and consumers find alternative fueling stations. Clean Cities coalitions foster the nation's economic, environmental, and energy security by working locally to advance affordable, efficient, and clean transportation fuels and technologies. This data can be found on the Alternative Fuels Data Center: https://doi.org/10.21949/1519144. For more information about the data schema and data dictionary, please see https://developer.nrel.gov/docs/transportation/alt-fuel-stations-v1/all/#response-fields. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529008

A common approach to simplify a problem with numerous objectives is to combine the cost layers into a composite a priori weighted single-objective raster grid. This dissertation examines new methods used for determining a spatially diverse set of near-optimal alternatives, and develops parallel computing techniques for brute-force near-optimal path enumeration, as well as more elegant methods that take advantage of the hierarchical structure of the underlying path-tree computation to select sets of spatially diverse near optimal paths.

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GIS In Utility Industry Market Size 2025-2029

The gis in utility industry market size is forecast to increase by USD 3.55 billion, at a CAGR of 19.8% between 2024 and 2029.

The utility industry's growing adoption of Geographic Information Systems (GIS) is driven by the increasing need for efficient and effective infrastructure management. GIS solutions enable utility companies to visualize, analyze, and manage their assets and networks more effectively, leading to improved operational efficiency and customer service. A notable trend in this market is the expanding application of GIS for water management, as utilities seek to optimize water distribution and reduce non-revenue water losses. However, the utility GIS market faces challenges from open-source GIS software, which can offer cost-effective alternatives to proprietary solutions. These open-source options may limit the functionality and support available to users, necessitating careful consideration when choosing a GIS solution. To capitalize on market opportunities and navigate these challenges, utility companies must assess their specific needs and evaluate the trade-offs between cost, functionality, and support when selecting a GIS provider. Effective strategic planning and operational execution will be crucial for success in this dynamic market.

What will be the Size of the GIS In Utility Industry 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 Global Utilities Industry Market for Geographic Information Systems (GIS) continues to evolve, driven by the increasing demand for advanced data management and analysis solutions. GIS services play a crucial role in utility infrastructure management, enabling asset management, data integration, project management, demand forecasting, data modeling, data analytics, grid modernization, data security, field data capture, outage management, and spatial analysis. These applications are not static but rather continuously unfolding, with new patterns emerging in areas such as energy efficiency, smart grid technologies, renewable energy integration, network optimization, and transmission lines. Spatial statistics, data privacy, geospatial databases, and remote sensing are integral components of this evolving landscape, ensuring the effective management of utility infrastructure. Moreover, the adoption of mobile GIS, infrastructure planning, customer service, asset lifecycle management, metering systems, regulatory compliance, GIS data management, route planning, environmental impact assessment, mapping software, GIS consulting, GIS training, smart metering, workforce management, location intelligence, aerial imagery, construction management, data visualization, operations and maintenance, GIS implementation, and IoT sensors is transforming the industry. The integration of these technologies and services facilitates efficient utility infrastructure management, enhancing network performance, improving customer service, and ensuring regulatory compliance. The ongoing evolution of the utilities industry market for GIS reflects the dynamic nature of the sector, with continuous innovation and adaptation to meet the changing needs of utility providers and consumers.

How is this GIS In Utility Industry Industry segmented?

The gis in utility industry 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. ProductSoftwareDataServicesDeploymentOn-premisesCloudGeographyNorth AmericaUSCanadaEuropeFranceGermanyRussiaMiddle East and AfricaUAEAPACChinaIndiaJapanSouth AmericaBrazilRest of World (ROW).

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.In the utility industry, Geographic Information Systems (GIS) play a pivotal role in optimizing operations and managing infrastructure. Utilities, including electricity, gas, water, and telecommunications providers, utilize GIS software for asset management, infrastructure planning, network performance monitoring, and informed decision-making. The GIS software segment in the utility industry encompasses various solutions, starting with fundamental GIS software that manages and analyzes geographical data. Additionally, utility companies leverage specialized software for field data collection, energy efficiency, smart grid technologies, distribution grid design, renewable energy integration, network optimization, transmission lines, spatial statistics, data privacy, geospatial databases, GIS services, project management, demand forecasting, data modeling, data analytics, grid modernization, data security, field data capture, outage ma

This layer represents the planned or future transit alternatives for Prince William County including mass transit, rail, ferry, and other transportation alternatives. The layer shows the planned or future possibilities for reducing the number of vehicles on area roadways. It includes transportation options like VRE, PRTC, high-capacity transit (bus and other options) that are in the Mobility Chapter of the Comprehensive Plan.

The U.S. Department of Transportation Federal Highway Administration (FHWA) designates a national network of plug-in electric vehicle (EV) charging and hydrogen, propane, and natural gas fueling infrastructure along national highway system corridors. To designate these Alternative Fuel Corridors (AFC), FHWA solicits nominations from state and local officials and works with other federal officials and industry stakeholders. Highways designed as AFCs are eligible for California's NEVI funding program. This layer displays all of the designated AFCs in California.

Chugach National Forest Mangement Area Alternatives 2018

The Viewshed analysis layer is used to identify visible areas. You specify the places you are interested in, either from a file or interactively, and the Viewshed service combines this with Esri-curated elevation data to create output polygons of visible areas. Some questions you can answer with the Viewshed task include:What areas can I see from this location? What areas can see me?Can I see the proposed wind farm?What areas can be seen from the proposed fire tower?The maximum number of input features is 1000.Viewshed has the following optional parameters:Maximum Distance: The maximum distance to calculate the viewshed.Maximum Distance Units: The units for the Maximum Distance parameter. The default is meters.DEM Resolution: The source elevation data; the default is 90m resolution SRTM. Other options include 30m, 24m, 10m, and Finest.Observer Height: The height above the surface of the observer. The default value of 1.75 meters is an average height of a person. If you are looking from an elevation location such as an observation tower or a tall building, use that height instead.Observer Height Units: The units for the Observer Height parameter. The default is meters.Surface Offset: The height above the surface of the object you are trying to see. The default value is 0. If you are trying to see buildings or wind turbines add their height here.Surface Offset Units: The units for the Surface Offset parameter. The default is meters.Generalize Viewshed Polygons: Determine if the viewshed polygons are to be generalized or not. The viewshed calculation is based upon a raster elevation model which creates a result with stair-stepped edges. To create a more pleasing appearance, and improve performance, the default behavior is to generalize the polygons. This generalization will not change the accuracy of the result for any location more than one half of the DEM's resolution.By default, this tool currently works worldwide between 60 degrees north and 56 degrees south based on the 3 arc-second (approximately 90 meter) resolution SRTM dataset. Depending upon the DEM resolution pick by the user, different data sources will be used by the tool. For 24m, tool will use global dataset WorldDEM4Ortho (excluding the counties of Azerbaijan, DR Congo and Ukraine) 0.8 arc-second (approximately 24 meter) from Airbus Defence and Space GmbH. For 30m, tool will use 1 arc-second resolution data in North America (Canada, United States, and Mexico) from the USGS National Elevation Dataset (NED), SRTM DEM-S dataset from Geoscience Australia in Australia and SRTM data between 60 degrees north and 56 degrees south in the remaining parts of the world (Africa, South America, most of Europe and continental Asia, the East Indies, New Zealand, and islands of the western Pacific). For 10m, tool will use 1/3 arc-second resolution data in the continental United States from USGS National Elevation Dataset (NED) and approximately 10 meter data covering Netherlands, Norway, Finland, Denmark, Austria, Spain, Japan Estonia, Latvia, Lithuania, Slovakia, Italy, Northern Ireland, Switzerland and Liechtenstein from various authoritative sources.To learn more, read the developer documentation for Viewshed or follow the Learn ArcGIS exercise called I Can See for Miles and Miles. To use this Geoprocessing service in ArcGIS Desktop 10.2.1 and higher, you can either connect to the Ready-to-Use Services, or create an ArcGIS Server connection. Connect to the Ready-to-Use Services by first signing in to your ArcGIS Online Organizational Account:Once you are signed in, the Ready-to-Use Services will appear in the Ready-to-Use Services folder or the Catalog window:If you would like to add a direct connection to the Elevation ArcGIS Server in ArcGIS for Desktop or ArcGIS Pro, use this URL to connect: https://elevation.arcgis.com/arcgis/services. You will also need to provide your account credentials. ArcGIS for Desktop:ArcGIS Pro:The ArcGIS help has additional information about how to do this:Learn how to make a ArcGIS Server Connection in ArcGIS Desktop. Learn more about using geoprocessing services in ArcGIS Desktop.This tool is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks.

This abstract contains links to public ArcGIS maps that include locations of carbonate springs and some of their characteristics. Information for accessing and navigating through the maps are included in a PowerPoint presentation IN THE FILE UPLOAD SECTION BELOW. Three separate data sets are included in the maps:

1. Geochemistry data from the US Water Quality Portal (WQP), which compiles geochemistry data from the USGS and other federal agencies.
2. Discharge data from WoKaS, a world wide spring discharge data set (Olarinoye et al., 2020).
3. Regional karst data from selected US state agencies.

Several base maps are included in the links. The US carbonate map describes and categorizes carbonates (e.g., depth from surface, overlying geology/ice, climate). The carbonate springs map categorizes springs as being urban, specifically within 1000 ft of a road, or rural. The basis for this categorization was that the heat island effect defines urban as within a 1000 ft of a road. There are other methods for defining urban versus rural to consider. Map links and details of the information they contain are listed below.

Map set 1: The WQP map provides three mapping options separated by the parameters available at each spring site. These maps summarize discrete water quality samples, but not data logger availability. Information at each spring provides links for where users can explore further data.

Option 1: WQP data with urban and rural springs labeled, with highlight of springs with or without NWIS data https://www.arcgis.com/home/item.html?id=2ce914ec01f14c20b58146f5d9702d8a

Options 2: WQP data by major ions and a few other solutes https://www.arcgis.com/home/item.html?id=5a114d2ce24c473ca07ef9625cd834b8

Option 3:WQP data by various carbon species https://www.arcgis.com/home/item.html?id=ae406f1bdcd14f78881905c5e0915b96

Map 2: The worldwide carbonate map in the WoKaS data set (citation below) includes a description of carbonate purity and distribution of urban and rural springs, for which discharge data are available: https://www.arcgis.com/apps/mapviewer/index.html?webmap=5ab43fdb2b784acf8bef85b61d0ebcbe.

Reference: Olarinoye, T., Gleeson, T., Marx, V., Seeger, S., Adinehvand, R., Allocca, V., Andreo, B., Apaéstegui, J., Apolit, C., Arfib, B. and Auler, A., 2020. Global karst springs hydrograph dataset for research and management of the world’s fastest-flowing groundwater. Scientific Data, 7(1), pp.1-9.

Map 3: Karst and spring data from selected states: This map includes sites that members of the RCN have suggested to our group.

https://uageos.maps.arcgis.com/apps/mapviewer/index.html?webmap=28ed22a14bb749e2b22ece82bf8a8177

This data set is incomplete (as of October 13, 2022 it includes Florida and Missouri). We are looking for more information. You can share data links to additional data by typing them into the hydroshare page created for our group. Then new sites will periodically be added to the map: https://www.hydroshare.org/resource/0cf10e9808fa4c5b9e6a7852323e6b11/

Acknowledgements: These maps were created by Michael Jones, University of Arkansas and Shishir Sarker, University of Kentucky with help from Laura Toran and Francesco Navarro, Temple University.

TIPS FOR NAVIGATING THE MAPS ARE IN THE POWERPOINT DOCUMENT IN THE FILE UPLOAD SECTION BELOW.

Locations for alternative fuel in Los Angeles CountyThis dataset is maintained through the County of Los Angeles Location Management System. The Location Management System is used by the County of Los Angeles GIS Program to maintain a single, comprehensive geographic database of locations countywide. For more information on the Location Management System, visithttp://egis3.lacounty.gov/lms/.

Crowther_Nature_Files.zip This description pertains to the original download. Details on revised (newer) versions of the datasets are listed below. When more than one version of a file exists in Figshare, the original DOI will take users to the latest version, though each version technically has its own DOI. -- Two global maps (raster files) of tree density. These maps highlight how the number of trees varies across the world. One map was generated using biome-level models of tree density, and applied at the biome scale. The other map was generated using ecoregion-level models of tree density, and applied at the ecoregion scale. For this reason, transitions between biomes or between ecoregions may be unrealistically harsh, but large-scale estimates are robust (see Crowther et al 2015 and Glick et al 2016). At the outset, this study was intended to generate reliable estimates at broad spatial scales, which inherently comes at the cost of fine-scale precision. For this reason, country-scale (or larger) estimates are generally more robust than individual pixel-level estimates. Additionally, due to data limitations, estimates for Mangroves and Tropical coniferous forest (as identified by WWF and TNC) were generated using models constructed from Topical moist broadleaf forest data and Temperate coniferous forest data, respectively. Because we used ecological analogy, the estimates for these two biomes should be considered less reliable than those of other biomes . These two maps initially appeared in Crowther et al (2015), with the biome map being featured more prominently. Explicit publication of the data is associated with Glick et al (2016). As they are produced, updated versions of these datasets, as well as alternative formats, will be made available under Additional Versions (see below).

Methods: We collected over 420,000 ground-sources estimates of tree density from around the world. We then constructed linear regression models using vegetative, climatic, topographic, and anthropogenic variables to produce forest tree density estimates for all locations globally. All modeling was done in R. Mapping was done using R and ArcGIS 10.1.

Viewing Instructions: Load the files into an appropriate geographic information system (GIS). For the original download (ArcGIS geodatabase files), load the files into ArcGIS to view or export the data to other formats. Because these datasets are large and have a unique coordinate system that is not read by many GIS, we suggest loading them into an ArcGIS dataframe whose coordinate system matches that of the data (see File Format). For GeoTiff files (see Additional Versions), load them into any compatible GIS or image management program.

Comments: The original download provides a zipped folder that contains (1) an ArcGIS File Geodatabase (.gdb) containing one raster file for each of the two global models of tree density – one based on biomes and one based on ecoregions; (2) a layer file (.lyr) for each of the global models with the symbology used for each respective model in Crowther et al (2015); and an ArcGIS Map Document (.mxd) that contains the layers and symbology for each map in the paper. The data is delivered in the Goode homolosine interrupted projected coordinate system that was used to compute biome, ecoregion, and global estimates of the number and density of trees presented in Crowther et al (2015). To obtain maps like those presented in the official publication, raster files will need to be reprojected to the Eckert III projected coordinate system. Details on subsequent revisions and alternative file formats are list below under Additional Versions.----------

Additional Versions: Crowther_Nature_Files_Revision_01.zip contains tree density predictions for small islands that are not included in the data available in the original dataset. These predictions were not taken into consideration in production of maps and figures presented in Crowther et al (2015), with the exception of the values presented in Supplemental Table 2. The file structure follows that of the original data and includes both biome- and ecoregion-level models.

Crowther_Nature_Files_Revision_01_WGS84_GeoTiff.zip contains Revision_01 of the biome-level model, but stored in WGS84 and GeoTiff format. This file was produced by reprojecting the original Goode homolosine files to WGS84 using nearest neighbor resampling in ArcMap. All areal computations presented in the manuscript were computed using the Goode homolosine projection. This means that comparable computations made with projected versions of this WGS84 data are likely to differ (substantially at greater latitudes) as a product of the resampling. Included in this .zip file are the primary .tif and its visualization support files.

References:

Crowther, T. W., Glick, H. B., Covey, K. R., Bettigole, C., Maynard, D. S., Thomas, S. M., Smith, J. R., Hintler, G., Duguid, M. C., Amatulli, G., Tuanmu, M. N., Jetz, W., Salas, C., Stam, C., Piotto, D., Tavani, R., Green, S., Bruce, G., Williams, S. J., Wiser, S. K., Huber, M. O., Hengeveld, G. M., Nabuurs, G. J., Tikhonova, E., Borchardt, P., Li, C. F., Powrie, L. W., Fischer, M., Hemp, A., Homeier, J., Cho, P., Vibrans, A. C., Umunay, P. M., Piao, S. L., Rowe, C. W., Ashton, M. S., Crane, P. R., and Bradford, M. A. 2015. Mapping tree density at a global scale. Nature, 525(7568): 201-205. DOI: http://doi.org/10.1038/nature14967Glick, H. B., Bettigole, C. B., Maynard, D. S., Covey, K. R., Smith, J. R., and Crowther, T. W. 2016. Spatially explicit models of global tree density. Scientific Data, 3(160069), doi:10.1038/sdata.2016.69.

The Transportation Alternatives Program (TAP) was authorized by the Moving Ahead for Progress in the 21st Century Act (MAP-21) in 2012 and has been continued by the Fixing America’s Surface Transportation (FAST) Act, through federal fiscal year 2020. Eligible project activities for TAP funding include a variety of smaller-scale transportation projects such as pedestrian and bicycle facilities, recreational trails, safe routes to school projects, and community improvements such as historic preservation, vegetation management, and some environmental mitigation related to storm water and habitat connectivity. Detailed information about Iowa’s TAP program may be found in the Iowa TAP Program Guidance. Additional information may be found in the program implementation guidance provided by the FHWA.

Iowa’s TAP program can be accessed in several ways. Statewide and multi-regional projects should apply directly to the Iowa DOT by October 1 annually for consideration in the Statewide TAP program. Access to smaller, local projects is dependent on geographic location and Iowa DOT endeavors to award projects based largely on geographic equity and local/regional priorities. Iowa’s small Metropolitan Planning Organizations and Regional Planning Affiliations will conduct an initial review of applications submitted for the competitive Local Projects TAP program administered by Iowa DOT. The large Metropolitan Planning Organizations (pop. greater than 200,000) will solicit and select project for TAP funding according to a process they determine. The map below will provide application submittal deadlines based on the project location.

The Alternative Fuel Corridors dataset was created in 2016 and was updated on January 16, 2025 with new Round 8 designations from the Federal Highway Administration (FHWA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The dataset is a highway layer of corridors, primarily along the NHS, that are designated as Corridor Ready or Corridor Pending. It includes designations of five types of alternative fuels, Electric Vehicle Charging (EV), Compressed Natural Gas (CNG), Liquefied Natural Gas (LNG), Propane (LPG), and Hydrogen. Corridor-ready segments currently contain a sufficient number of fueling facilities to allow for corridor travel with the designated alternative fuel, and to qualify for highway signage. Corridors that do not have sufficient alternative fuel facilities to support alternative fuel vehicle travel are designated as corridor pending. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529007

The geographical mapping software market is experiencing robust growth, driven by increasing demand across diverse sectors. The market's expansion is fueled by several key factors, including the rising adoption of cloud-based solutions for enhanced accessibility and collaboration, the growing need for precise location data in various applications, and the increasing integration of GIS technology with other analytical tools. Applications such as geological exploration, water conservancy projects, and urban planning are major contributors to market growth, benefiting from the ability to visualize and analyze spatial data efficiently. While the market faces certain restraints, such as the high initial investment costs associated with some software solutions and the need for specialized expertise, these are being mitigated by the emergence of more affordable and user-friendly options, as well as increased training and educational resources. The market is segmented by application (Geological Exploration, Water Conservancy Project, Urban Plan, Others) and type (Cloud Based, Web Based), with cloud-based solutions gaining significant traction due to their scalability and cost-effectiveness. Major players in the market, including Esri, Autodesk, Mapbox, and others, are continuously innovating and introducing new features to cater to the evolving needs of their customers. This competitive landscape ensures continuous improvement in software capabilities and affordability, further propelling market expansion. The geographical distribution of this market is broad, with North America and Europe currently holding significant market shares due to established infrastructure and high adoption rates. However, the Asia-Pacific region is exhibiting particularly rapid growth, driven by increasing urbanization, infrastructure development, and government initiatives promoting the use of GIS technologies. This regional shift indicates significant future growth potential in emerging markets. The forecast period of 2025-2033 suggests continued expansion, with a projected CAGR reflecting the sustained demand across different geographical regions and application areas. While precise figures are unavailable, based on industry trends and available data, a conservative estimate for the current market size would place it in the high hundreds of millions of dollars, with steady and significant growth anticipated.