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The global spatial analysis software market is experiencing robust growth, driven by increasing adoption across diverse sectors. The market, currently valued at approximately $5 billion (estimated based on typical market sizes for similar software segments), is projected to witness a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This expansion is fueled by several key factors. The rising availability of geospatial data, coupled with advancements in cloud computing and artificial intelligence (AI), is enabling more sophisticated and accessible spatial analysis capabilities. Industries such as urban planning, environmental management, logistics, and retail are leveraging these advancements for optimized resource allocation, improved decision-making, and enhanced operational efficiency. The integration of spatial analysis tools into Geographic Information Systems (GIS) platforms further enhances market penetration, streamlining workflows and facilitating comprehensive data analysis. Demand for predictive modeling and location intelligence solutions is also a major growth driver, particularly among businesses seeking to understand customer behavior, optimize supply chains, and mitigate risks. However, market growth is not without its challenges. The high cost of implementation and maintenance of advanced spatial analysis software can be a barrier to entry for smaller organizations. Furthermore, the complexity of these tools requires skilled professionals, leading to a shortage of trained personnel in some regions. Despite these restraints, the long-term outlook for the spatial analysis software market remains positive, with continued innovation and wider adoption expected across various applications and geographic locations. Specific segments like those focused on 3D spatial analysis and real-time data processing are anticipated to experience particularly strong growth in the coming years. The increasing prevalence of big data and the need for effective data visualization are key elements underpinning this dynamic market.

The geographical mapping software 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 12% from 2025 to 2033, reaching an estimated $45 billion by 2033. This expansion is fueled by several key factors. The rise of cloud-based solutions offers enhanced accessibility, scalability, and collaboration features, attracting a broader user base. Furthermore, the increasing adoption of GIS (Geographic Information Systems) technology in various applications, including geological exploration, water conservancy projects, and urban planning, is significantly boosting market demand. Government initiatives promoting digital infrastructure development and smart city projects are further contributing to market growth. The rising need for precise location-based services and real-time data analysis across sectors like transportation, logistics, and agriculture also plays a significant role. While data security concerns and the high initial investment costs associated with implementing sophisticated mapping software can act as restraints, the overall market outlook remains highly positive. The market segmentation reveals strong growth in both cloud-based and web-based solutions. Cloud-based solutions are gaining traction due to their flexibility and cost-effectiveness, while web-based solutions remain popular for their ease of use and accessibility. The application segment demonstrates considerable potential, with geological exploration and urban planning leading the way. This suggests a robust future for geographical mapping software, particularly in areas requiring detailed spatial analysis and data visualization. Geographical regions like North America and Europe currently hold a significant market share, but the Asia-Pacific region is expected to witness rapid growth in the coming years driven by increasing infrastructure development and technological advancements. Competition in the market is intense, with established players like Esri and Autodesk alongside emerging innovative companies vying for market share. The continuous evolution of GIS technology, encompassing features like AI-powered analytics and 3D mapping capabilities, is set to further shape market dynamics in the years to come.

The Digital Geomorphic-GIS Map of Gulf Islands National Seashore (5-meter accuracy and 1-foot resolution 2006-2007 mapping), Mississippi and Florida is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (guis_geomorphology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (guis_geomorphology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (guis_geomorphology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (guis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (guis_geomorphology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (guis_geomorphology_metadata_faq.pdf). Please read the guis_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (guis_geomorphology_metadata.txt or guis_geomorphology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:26,000 and United States National Map Accuracy Standards features are within (horizontally) 13.2 meters or 43.3 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

The arrival of ArcGIS Pro has brought a challenge to ArcMap users. The new software is sufficiently different in architecture and layout that switching from the old to the new is not a simple process. In some ways, Pro is harder to learn for ArcMap users than for new GIS users, because some workflows have to be unlearned, or at least heavily modified. Current ArcMap users are pressed for time, trying to learn the new software while still completing their daily tasks, so a book that teaches Pro from the start is not an efficient method.Switching to ArcGIS Pro from ArcMap aims to quickly transition ArcMap users to ArcGIS Pro. Rather than teaching Pro from the start, as for a novice user, this book focuses on how Pro is different from ArcMap. Covering the most common and important workflows required for most GIS work, it leverages the user’s prior experience to enable a more rapid adjustment to Pro.AUDIENCEProfessional and scholarly; College/higher education; General/trade.AUTHOR BIOMaribeth H. Price, PhD, South Dakota School of Mines and Technology, has been using Esri products since 1991, teaching college GIS since 1995 and writing textbooks utilizing Esri’s software since 2001. She has extensive familiarity with both ArcMap/ArcCatalog and Pro, both as a user and in the classroom, as well as long experience writing about GIS concepts and developing software tutorials. She teaches GIS workshops, having offered more than 100 workshops to over 1,200 participants since 2000.Pub Date: Print: 2/14/2019 Digital: 1/28/2019 Format: PaperbackISBN: Print: 9781589485440 Digital: 9781589485457 Trim: 8 x 10 in.Price: Print: $49.99 USD Digital: $49.99 USD Pages: 172Table of ContentsPreface1 Contemplating the switch to ArcGIS ProBackgroundSystem requirementsLicensingCapabilities of ArcGIS ProWhen should I switch?Time to exploreObjective 1.1: Downloading the data for these exercisesObjective 1.2: Starting ArcGIS Pro, signing in, creating a project, and exploring the interfaceObjective 1.3: Accessing maps and data from ArcGIS OnlineObjective 1.4: Arranging the windows and panesObjective 1.5: Accessing the helpObjective 1.6: Importing a map document2 Unpacking the GUIBackgroundThe ribbon and tabsPanesViewsTime to exploreObjective 2.1: Getting familiar with the Contents paneObjective 2.2: Learning to work with objects and tabsObjective 2.3: Exploring the Catalog pane3 The projectBackgroundWhat is a project?Items stored in a projectPaths in projectsRenaming projectsTime to exploreObjective 3.1: Exploring different elements of a projectObjective 3.2: Accessing properties of projects, maps, and other items4 Navigating and exploring mapsBackgroundExploring maps2D and 3D navigationTime to exploreObjective 4.1: Learning to use the Map toolsObjective 4.2: Exploring 3D scenes and linking views5 Symbolizing mapsBackgroundAccessing the symbol settings for layersAccessing the labeling propertiesSymbolizing rastersTime to exploreObjective 5.1: Modifying single symbolsObjective 5.2: Creating maps from attributesObjective 5.3: Creating labelsObjective 5.4: Managing labelsObjective 5.5: Symbolizing rasters6 GeoprocessingBackgroundWhat’s differentAnalysis buttons and toolsTool licensingTime to exploreObjective 6.1: Getting familiar with the geoprocessing interfaceObjective 6.2: Performing interactive selectionsObjective 6.3: Performing selections based on attributesObjective 6.4: Performing selections based on locationObjective 6.5: Practicing geoprocessing7 TablesBackgroundGeneral table characteristicsJoining and relating tablesMaking chartsTime to exploreObjective 7.1: Managing table viewsObjective 7.2: Creating and managing properties of a chartObjective 7.3: Calculating statistics for tablesObjective 7.4: Calculating and editing in tables8 LayoutsBackgroundLayouts and map framesLayout editing proceduresImporting map documents and templatesTime to exploreObjective 8.1: Creating the maps for the layoutObjective 8.2: Setting up a layout page with map framesObjective 8.3: Setting map frame extent and scaleObjective 8.4: Formatting the map frameObjective 8.5: Creating and formatting map elementsObjective 8.6: Fine-tuning the legendObjective 8.7: Accessing and copying layouts9 Managing dataBackgroundData modelsManaging the geodatabase schemaCreating domainsManaging data from diverse sourcesProject longevityManaging shared data for work groupsTime to exploreObjective 9.1: Creating a project and exporting data to itObjective 9.2: Creating feature classesObjective 9.3: Creating and managing metadataObjective 9.4: Creating fields and domainsObjective 9.5: Modifying the table schemaObjective 9.6: Sharing data using ArcGIS Online10 EditingBackgroundBasic editing functionsCreating featuresModifying existing featuresCreating and editing annotationTime to exploreObjective 10.1: Understanding the editing tools in ArcGIS ProObjective 10.2: Creating pointsObjective 10.3: Creating linesObjective 10.4: Creating polygonsObjective 10.5: Modifying existing featuresObjective 10.6: Creating an annotation feature classObjective 10.7: Editing annotationObjective 10.8: Creating annotation features11 Moving forwardData sourcesIndex

Geographic Information Systems Platform Market Outlook

The global Geographic Information Systems (GIS) Platform Market size is projected to reach remarkable heights with an estimated value of USD 12 billion in 2023 and is expected to balloon to over USD 25 billion by 2032, reflecting a robust Compound Annual Growth Rate (CAGR) of 8%. This impressive growth trajectory is largely driven by the increasing demand for location-based services across various industries, including urban planning, transportation, and agriculture. As the world becomes increasingly interconnected, the necessity for real-time location data and advanced mapping solutions has never been more crucial, thereby fuelling the expansion of the GIS platform market.

One significant growth factor for the GIS platform market is the rapid urbanization occurring on a global scale. With more than half of the world's population now living in urban areas, cities are becoming larger and more complex. This trend necessitates sophisticated urban planning solutions that can effectively map, analyze, and visualize urban growth patterns. GIS platforms provide critical tools that enable urban planners to make informed decisions about land use, transportation networks, and infrastructure development. By integrating geographic data with socio-economic data, GIS applications help cities manage resources more efficiently and sustainably, thus driving the market forward.

Another driver of growth in the GIS platform market is the escalating need for effective disaster management solutions. Natural disasters such as hurricanes, earthquakes, and floods are becoming more frequent and severe, posing significant challenges for governments and emergency services worldwide. GIS platforms enable authorities to predict, prepare for, and respond to these disasters more effectively by providing detailed maps and models that can simulate potential scenarios and outcomes. The ability to integrate real-time data with historical records allows emergency response teams to optimize resource allocation and logistics, minimizing the impact of disasters on human lives and infrastructure.

The transportation and logistics sector is also a significant contributor to the growth of the GIS platform market. As global trade and e-commerce continue to grow, the demand for efficient and reliable transportation networks is increasing. GIS platforms provide valuable insights into route optimization, traffic management, and supply chain logistics. By enabling companies to analyze geographic data, GIS applications help to reduce transportation costs, improve delivery times, and enhance overall supply chain efficiency. As businesses increasingly look to leverage location-based data to gain a competitive advantage, the GIS platform market is set to experience sustained growth.

The role of a GIS Controller is becoming increasingly vital as the GIS platform market expands. A GIS Controller is responsible for overseeing the integration and management of geographic data within an organization, ensuring that the data is accurate, up-to-date, and accessible. This role involves coordinating with various departments to implement GIS solutions that align with organizational goals and enhance decision-making processes. As organizations across industries recognize the value of geographic data, the demand for skilled GIS Controllers is on the rise. These professionals play a crucial role in optimizing the use of GIS technology, enabling organizations to leverage location-based insights for strategic advantage.

Regionally, North America is anticipated to dominate the GIS platform market due to its advanced technological infrastructure and high adoption rates among various industries. The presence of leading GIS service providers in this region further bolsters its market position. Additionally, Asia Pacific is projected to witness the fastest growth over the forecast period, driven by rapid urbanization and increasing government initiatives to integrate GIS technology into urban planning and disaster management. The Middle East & Africa and Latin America are also expected to emerge as lucrative markets, as these regions look to harness the potential of GIS platforms to address their unique geographic challenges and drive economic development.

Component Analysis

The GIS platform market can be divided into three primary components: software, hardware, and services. Each of these segments plays a vital role in the overall functionality and adap

The global Geographic Information System (GIS) mapping tools market is experiencing robust growth, driven by increasing demand across diverse sectors. The market, currently valued at approximately $15 billion (a reasonable estimation based on typical market sizes for similar software sectors), is projected to maintain a Compound Annual Growth Rate (CAGR) of around 8% from 2025 to 2033, reaching an estimated market size of approximately $25 billion by 2033. This expansion is fueled by several key factors. The rising adoption of cloud-based GIS solutions enhances accessibility and scalability for businesses of all sizes. Furthermore, the integration of GIS technology with other advanced technologies like AI and machine learning is creating sophisticated applications with increased analytical capabilities across various domains, including urban planning, environmental monitoring, and precision agriculture. Government initiatives promoting digital transformation and smart city projects significantly contribute to market growth, particularly in developing economies. The diverse application segments, including geological exploration, water conservancy projects, and urban planning, further drive market expansion. However, the market also faces certain challenges. High initial investment costs associated with implementing and maintaining GIS software can be a barrier to entry for smaller organizations. The complexity of GIS software and the need for skilled professionals to operate and interpret the data can also limit wider adoption. Competition amongst established players and emerging technology providers continues to intensify, influencing pricing and product innovation. Nevertheless, the long-term outlook for the GIS mapping tools market remains positive, driven by the increasing recognition of its value across various industries and the continuous advancement of GIS technology. The continued development and integration of more user-friendly interfaces and broader data accessibility will unlock further growth opportunities.

In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California’s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map California’s State Waters with consistent methods at a consistent scale. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. This approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of “lands” from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth. This data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013). The California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bay’s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Service–Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey. These web services for the Offshore of Coal Oil Point map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://walrus.wr.usgs.gov/mapping/csmp/index.html. Each published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and photographic imagery; these “ground-truth” surveying data are available from the CSMP Video and Photograph Portal at https://doi.org/10.5066/F7J1015K. The “seafloor character” data layer shows classifications of the seafloor on the basis of depth, slope, rugosity (ruggedness), and backscatter intensity and which is further informed by the ground-truth-survey imagery. The “potential habitats” polygons are delineated on the basis of substrate type, geomorphology, seafloor process, or other attributes that may provide a habitat for a specific species or assemblage of organisms. Representative seismic-reflection profile data from the map area is also include and provides information on the subsurface stratigraphy and structure of the map area. The distribution and thickness of young sediment (deposited over the past about 21,000 years, during the most recent sea-level rise) is interpreted on the basis of the seismic-reflection data. The geologic polygons merge onshore geologic mapping (compiled from existing maps by the California Geological Survey) and new offshore geologic mapping that is based on integration of high-resolution bathymetry and backscatter imagery seafloor-sediment and rock samplesdigital camera and video imagery, and high-resolution seismic-reflection profiles. The information provided by the map sheets, pamphlet, and data catalog has a broad range of applications. High-resolution bathymetry, acoustic backscatter, ground-truth-surveying imagery, and habitat mapping all contribute to habitat characterization and ecosystem-based management by providing essential data for delineation of marine protected areas and ecosystem restoration. Many of the maps provide high-resolution baselines that will be critical for monitoring environmental change associated with climate change, coastal development, or other forcings. High-resolution bathymetry is a critical component for modeling coastal flooding caused by storms and tsunamis, as well as inundation associated with longer term sea-level rise. Seismic-reflection and bathymetric data help characterize earthquake and tsunami sources, critical for natural-hazard assessments of coastal zones. Information on sediment distribution and thickness is essential to the understanding of local and regional sediment transport, as well as the development of regional sediment-management plans. In addition, siting of any new offshore infrastructure (for example, pipelines, cables, or renewable-energy facilities) will depend on high-resolution mapping. Finally, this mapping will both stimulate and enable new scientific research and also raise public awareness of, and education about, coastal environments and issues. Web services were created using an ArcGIS service definition file. The ArcGIS REST service and OGC WMS service include all Offshore Coal Oil Point map area data layers. Data layers are symbolized as shown on the associated map sheets.

IMPORTANT: This is the source of the feature layer template in the LearnArcGIS Lesson: Prepare for SAR Incidents and for the MapSAR Solution. If this layer is cloned or copied, the owner of the items needs to update the item details to reflect this. Purpose: This is a feature layer template for use in missing person search operations. It is based on the MapSAR (ArcGIS Desktop) Data Model but simplified for use in web maps and apps. Please see MapSAR GitHub for more information on this project.Maps are at the core of any Search and Rescue (SAR) operation. Geographic information system (GIS) software allows rescue personnel to quickly generate maps that depict specific aspects of the operation and show what is happening on the ground over time. The maps and operations data can be shared over a network to supply an enhanced common operating picture throughout the Incident Command Post (ICP). A team of GIS and SAR professionals from Sierra Madre Search and Rescue Team, Esri, Sequoia and Kings Canyon National Park, Yosemite National Park, Grand Canyon National Park, and the Mountaineer Rescue Group came together to develop the tools and instructions to fit established SAR workflows. The goal is to meet the critical need to provide standards, documents, and training to the international SAR community and establish more widespread and effective integration of GIS into operations.See Comments below for updates to the data model.

3D Mapping Management Software Market Outlook

The global market size of 3D Mapping Management Software was valued at USD 4.2 billion in 2023 and is forecasted to reach USD 12.6 billion by 2032, growing at an impressive CAGR of 13.2% during the forecast period. This remarkable growth can be attributed to increased urbanization, technological advancements, and the rising adoption of 3D visualization in various industries.

The proliferation of smart city projects worldwide is a significant growth driver for the 3D Mapping Management Software market. Governments and urban planners are increasingly leveraging this technology to create accurate and detailed 3D maps for better planning and management of urban spaces. These maps assist in visualizing infrastructure, zoning, and landscape features, thus enabling more efficient and sustainable city planning. The technology's capability to integrate various data sources, such as satellite imagery, LiDAR data, and GIS, enhances its utility and application range, further fueling market growth.

Another major growth factor is the increasing need for disaster management and mitigation solutions. With climate change leading to more frequent and severe natural disasters, the demand for advanced tools to predict, simulate, and manage such events is on the rise. 3D Mapping Management Software offers robust solutions for simulating disaster scenarios, mapping vulnerable areas, and planning emergency responses. The ability to visualize and analyze complex geographical data in three dimensions provides a significant advantage in planning and executing disaster management strategies, thereby driving market demand.

Infrastructure development projects, particularly in emerging economies, are also propelling the 3D Mapping Management Software market. The construction sector is increasingly adopting 3D mapping for project planning, design, and management. These tools enable the creation of accurate and detailed 3D models of construction sites, which help in visualizing the project from different angles, identifying potential issues, and improving overall efficiency. Additionally, asset management within the infrastructure sector benefits greatly from 3D mapping, as it allows for precise tracking and maintenance planning of various assets.

The development and utilization of High-Precision 3D Map technology are becoming increasingly crucial in the realm of urban planning and infrastructure management. These maps provide an unparalleled level of detail and accuracy, which is essential for the meticulous planning and execution of large-scale projects. By offering a comprehensive view of the terrain and existing structures, high-precision 3D maps enable planners and engineers to make informed decisions that enhance the efficiency and sustainability of urban development. This technology is particularly beneficial in the context of smart city initiatives, where the integration of precise mapping data can significantly improve the management of resources and services.

In terms of regional outlook, North America holds a significant share in the 3D Mapping Management Software market. The presence of numerous leading technology companies and widespread adoption of advanced mapping solutions in various sectors drive the market in this region. Additionally, Europe and Asia Pacific are expected to witness substantial growth due to increasing investments in smart city projects, infrastructure development, and disaster management initiatives. The rapid urbanization in Asia Pacific, coupled with government initiatives promoting advanced mapping technologies, makes it a lucrative market for 3D mapping solutions.

Component Analysis

The 3D Mapping Management Software market can be segmented by component into Software and Services. The software segment dominates the market, driven by the increasing adoption of advanced 3D mapping software solutions across various industries. These software solutions offer a range of functionalities, including data integration, visualization, simulation, and analysis. Continuous advancements in software capabilities, such as real-time data processing and AI integration, further enhance their appeal, leading to higher adoption rates.

The services segment, although smaller than the software segment, is witnessing steady growth. This segment includes consulting, implementation, training, and support services. As organizations increasingly adopt 3D mapping softw

A 40-minute tutorial to use OGC webservices offered by the Mission Atlantic GeoNode in your data analysis. The workshop makes use of Python Notebooks and common GIS Software (ArcGIS and QGIS), basic knowledge of Python and/or GIS software is recommended. • Introduction to OGC services • Search through metadata using the OGC Catalogue Service (CSW) • Visualize data using OGC Web Mapping Service (WMS) • Subset and download data using OGC Web Feature and Coverage Services (WFS/WCS) • Use OGC services with QGIS and/or ArcGIS

This dataset displays areas where mariners have to be made aware of circumstances influencing the safety of navigation. NOAA ENC Direct to GIS Internet Mapping Service is designed to allow for the visualization, querying and downloading of NOAA's Electronic Navigational Chart's (NOAA ENC) data in common Geographic Information System (GIS) formats for purposes outside of navigation. NOAA ENC Direct to GIS data is not intended for navigational purposes. This data is provided for use in GIS software packages for coastal planning and research.View Dataset on the Gateway

A major objective of plant ecology research is to determine the underlying processes responsible for the observed spatial distribution patterns of plant species. Plants can be approximated as points in space for this purpose, and thus, spatial point pattern analysis has become increasingly popular in ecological research. The basic piece of data for point pattern analysis is a point location of an ecological object in some study region. Therefore, point pattern analysis can only be performed if data can be collected. However, due to the lack of a convenient sampling method, a few previous studies have used point pattern analysis to examine the spatial patterns of grassland species. This is unfortunate because being able to explore point patterns in grassland systems has widespread implications for population dynamics, community-level patterns and ecological processes. In this study, we develop a new method to measure individual coordinates of species in grassland communities. This method records plant growing positions via digital picture samples that have been sub-blocked within a geographical information system (GIS). Here, we tested out the new method by measuring the individual coordinates of Stipa grandis in grazed and ungrazed S. grandis communities in a temperate steppe ecosystem in China. Furthermore, we analyzed the pattern of S. grandis by using the pair correlation function g(r) with both a homogeneous Poisson process and a heterogeneous Poisson process. Our results showed that individuals of S. grandis were overdispersed according to the homogeneous Poisson process at 0-0.16 m in the ungrazed community, while they were clustered at 0.19 m according to the homogeneous and heterogeneous Poisson processes in the grazed community. These results suggest that competitive interactions dominated the ungrazed community, while facilitative interactions dominated the grazed community. In sum, we successfully executed a new sampling method, using digital photography and a Geographical Information System, to collect experimental data on the spatial point patterns for the populations in this grassland community.

Methods 1. Data collection using digital photographs and GIS

A flat 5 m x 5 m sampling block was chosen in a study grassland community and divided with bamboo chopsticks into 100 sub-blocks of 50 cm x 50 cm (Fig. 1). A digital camera was then mounted to a telescoping stake and positioned in the center of each sub-block to photograph vegetation within a 0.25 m2 area. Pictures were taken 1.75 m above the ground at an approximate downward angle of 90° (Fig. 2). Automatic camera settings were used for focus, lighting and shutter speed. After photographing the plot as a whole, photographs were taken of each individual plant in each sub-block. In order to identify each individual plant from the digital images, each plant was uniquely marked before the pictures were taken (Fig. 2 B).

Digital images were imported into a computer as JPEG files, and the position of each plant in the pictures was determined using GIS. This involved four steps: 1) A reference frame (Fig. 3) was established using R2V software to designate control points, or the four vertexes of each sub-block (Appendix S1), so that all plants in each sub-block were within the same reference frame. The parallax and optical distortion in the raster images was then geometrically corrected based on these selected control points; 2) Maps, or layers in GIS terminology, were set up for each species as PROJECT files (Appendix S2), and all individuals in each sub-block were digitized using R2V software (Appendix S3). For accuracy, the digitization of plant individual locations was performed manually; 3) Each plant species layer was exported from a PROJECT file to a SHAPE file in R2V software (Appendix S4); 4) Finally each species layer was opened in Arc GIS software in the SHAPE file format, and attribute data from each species layer was exported into Arc GIS to obtain the precise coordinates for each species. This last phase involved four steps of its own, from adding the data (Appendix S5), to opening the attribute table (Appendix S6), to adding new x and y coordinate fields (Appendix S7) and to obtaining the x and y coordinates and filling in the new fields (Appendix S8).

1. Data reliability assessment

To determine the accuracy of our new method, we measured the individual locations of Leymus chinensis, a perennial rhizome grass, in representative community blocks 5 m x 5 m in size in typical steppe habitat in the Inner Mongolia Autonomous Region of China in July 2010 (Fig. 4 A). As our standard for comparison, we used a ruler to measure the individual coordinates of L. chinensis. We tested for significant differences between (1) the coordinates of L. chinensis, as measured with our new method and with the ruler, and (2) the pair correlation function g of L. chinensis, as measured with our new method and with the ruler (see section 3.2 Data Analysis). If (1) the coordinates of L. chinensis, as measured with our new method and with the ruler, and (2) the pair correlation function g of L. chinensis, as measured with our new method and with the ruler, did not differ significantly, then we could conclude that our new method of measuring the coordinates of L. chinensis was reliable.

We compared the results using a t-test (Table 1). We found no significant differences in either (1) the coordinates of L. chinensis or (2) the pair correlation function g of L. chinensis. Further, we compared the pattern characteristics of L. chinensis when measured by our new method against the ruler measurements using a null model. We found that the two pattern characteristics of L. chinensis did not differ significantly based on the homogenous Poisson process or complete spatial randomness (Fig. 4 B). Thus, we concluded that the data obtained using our new method was reliable enough to perform point pattern analysis with a null model in grassland communities.

Mapping Software Market Outlook

The global mapping software market size was valued at approximately USD 5.7 billion in 2023 and is projected to reach USD 11.4 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.1% during the forecast period. The growth of this market is driven by the increasing need for spatial data in various industries, advancements in geographic information system (GIS) technology, and the growing trend of digitalization across different sectors.

One of the primary growth factors in the mapping software market is the rising demand for location-based services (LBS). These services are essential for numerous applications, from navigation and route planning to marketing and asset tracking. The proliferation of smartphones and wearable devices equipped with GPS has significantly boosted the use of LBS, thereby driving the demand for advanced mapping software. Furthermore, businesses are increasingly leveraging spatial data to enhance decision-making processes, optimize operations, and improve customer experiences, all of which contribute to the market's expansion.

Another significant driver is the increasing usage of mapping software in urban planning and smart city initiatives. With the global urban population expected to rise continuously, cities are turning to technology to manage resources efficiently, ensure sustainable development, and improve the quality of life for residents. Mapping software plays a crucial role in urban planning by providing detailed spatial data, enabling planners to visualize and analyze various urban scenarios, plan infrastructure development, and manage urban growth effectively. Additionally, governments are investing heavily in smart city projects, creating a substantial demand for sophisticated mapping tools.

Technological advancements in GIS and remote sensing technologies are also fueling the growth of the mapping software market. Innovations such as 3D mapping, real-time data integration, and cloud-based GIS solutions have expanded the capabilities and applications of mapping software. These advancements allow for more accurate and comprehensive spatial analysis, facilitating better decision-making and problem-solving in numerous fields, including environmental monitoring, disaster management, and transportation planning. Moreover, the integration of artificial intelligence (AI) and machine learning (ML) with mapping software is further enhancing its functionality, enabling predictive analytics and automated data processing.

Regionally, North America holds a significant share of the mapping software market, driven by the widespread adoption of advanced technologies and the presence of major market players. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, attributed to rapid urbanization, increasing investments in infrastructure development, and the growing adoption of digital solutions across various sectors. Europe also presents substantial growth opportunities due to the increasing focus on smart city projects and environmental sustainability initiatives.

Component Analysis

The mapping software market is segmented by component into software and services. The software segment is further categorized into desktop, web-based, and mobile software, each catering to different user needs and preferences. Desktop software continues to be widely used due to its robust functionalities and ability to handle complex spatial data analysis. Web-based software, on the other hand, offers flexibility and ease of access, making it popular among users who require real-time data and collaboration capabilities. Mobile mapping software is gaining traction, especially among field workers and on-the-go professionals, due to its portability and convenience.

Services in the mapping software market encompass a range of offerings, including consulting, implementation, training, and support services. Consulting services are essential for organizations looking to integrate mapping software into their existing systems and workflows. Implementation services ensure the smooth deployment and customization of software solutions to meet specific business requirements. Training services are crucial for enhancing user proficiency and maximizing the software's potential, while support services provide necessary technical assistance and software maintenance. The growing complexity of spatial data applications and the need for expert guidance are driving the demand for these services.

The software segment dominates the mapping softwar

Can your desktop computer crunch the large GIS datasets that are becoming increasingly common across the geosciences? Do you have access to or the know-how to take advantage of advanced high performance computing (HPC) capability? Web based cyberinfrastructure takes work off your desk or laptop computer and onto infrastructure or "cloud" based data and processing servers. This talk will describe the HydroShare collaborative environment and web based services being developed to support the sharing and processing of hydrologic data and models. HydroShare supports the upload, storage, and sharing of a broad class of hydrologic data including time series, geographic features and raster datasets, multidimensional space-time data, and other structured collections of data. Web service tools and a Python client library provide researchers with access to HPC resources without requiring them to become HPC experts. This reduces the time and effort spent in finding and organizing the data required to prepare the inputs for hydrologic models and facilitates the management of online data and execution of models on HPC systems. This presentation will illustrate the use of web based data and computation services from both the browser and desktop client software. These web-based services implement the Terrain Analysis Using Digital Elevation Model (TauDEM) tools for watershed delineation, generation of hydrology-based terrain information, and preparation of hydrologic model inputs. They allow users to develop scripts on their desktop computer that call analytical functions that are executed completely in the cloud, on HPC resources using input datasets stored in the cloud, without installing specialized software, learning how to use HPC, or transferring large datasets back to the user's desktop. These cases serve as examples for how this approach can be extended to other models to enhance the use of web and data services in the geosciences.

Slides for AGU 2015 presentation IN51C-03, December 18, 2015

This dataset displays the linear boundaries of the solid portion of the Earth's surface, as opposed to sea, or other water (IHO Dictionary, S-32, 5th Edition , 2635). NOAA ENC Direct to GIS Internet Mapping Service is designed to allow for the visualization, querying and downloading of NOAA's Electronic Navigational Chart's (NOAA ENC) data in common Geographic Information System (GIS) formats for purposes outside of navigation. NOAA ENC Direct to GIS data is not intended for navigational purposes. This data is provided for use in GIS software packages for coastal planning and research.View Dataset on the Gateway

This dataset displays navigational aids distinctively marked to maximize their visibility in daylight. NOAA ENC Direct to GIS Internet Mapping Service is designed to allow for the visualization, querying and downloading of NOAA's Electronic Navigational Chart's (NOAA ENC) data in common Geographic Information System (GIS) formats. NOAA ENC Direct to GIS data is not intended for navigational purposes. This data is provided for use in GIS software packages for coastal planning and research.View Dataset on the Gateway

This dataset displays water areas whose depth is within a defined range of values. NOAA ENC Direct to GIS Internet Mapping Service is designed to allow for the visualization, querying and downloading of NOAA's Electronic Navigational Chart's (NOAA ENC) data in common Geographic Information System (GIS) formats for purposes outside of navigation. NOAA ENC Direct to GIS data is not intended for navigational purposes. This data is provided for use in GIS software packages for coastal planning and research.View Dataset on the Gateway

DNRGPS is an update to the popular DNRGarmin application. DNRGPS and its predecessor were built to transfer data between Garmin handheld GPS receivers and GIS software.

DNRGPS was released as Open Source software with the intention that the GPS user community will become stewards of the application, initiating future modifications and enhancements.

DNRGPS does not require installation. Simply run the application .exe

See the DNRGPS application documentation for more details.

Compatible with: Windows (XP, 7, 8, 10, and 11), ArcGIS shapefiles and file geodatabases, Google Earth, most hand-held Garmin GPSs, and other NMEA output GPSs

Limited Compatibility: Interactions with ArcMap layer files and ArcMap graphics are no longer supported. Instead use shapefile or geodatabase.

Prerequisite: .NET 4 Framework

DNR Data and Software License Agreement

Subscribe to the DNRGPS announcement list to be notified of upgrades or updates.

This dataset displays a harbour installation with a service or commercial operation of public interest. NOAA ENC Direct to GIS Internet Mapping Service is designed to allow for the visualization, querying and downloading of NOAA's Electronic Navigational Chart's (NOAA ENC) data in common Geographic Information System (GIS) formats for purposes outside of navigation. NOAA ENC Direct to GIS data is not intended for navigational purposes. This data is provided for use in GIS software packages for coastal planning and research.View Dataset on the Gateway