Discover the booming GIS Data Collector market! This comprehensive analysis reveals a $2.5B market in 2025, projected to reach $4.2B by 2033, fueled by precision agriculture, infrastructure development, and technological advancements. Explore key trends, drivers, restraints, and leading companies shaping this dynamic sector.

The global GIS Data Collector market is experiencing robust growth, driven by increasing adoption of precision agriculture techniques, expanding infrastructure development projects, and the rising need for accurate geospatial data across various industries. The market's Compound Annual Growth Rate (CAGR) is estimated to be around 8% for the forecast period of 2025-2033, projecting significant market expansion. This growth is fueled by technological advancements in GPS technology, improved data processing capabilities, and the increasing affordability of GIS data collection devices. Key segments driving market expansion include high-precision data collection systems and their application in agriculture, where farmers are increasingly leveraging real-time data for optimized resource management and increased yields. The industrial sector also contributes significantly to market growth, with applications ranging from construction and surveying to utility management and environmental monitoring. While the market faces certain restraints, such as the need for skilled professionals to operate the sophisticated equipment and the potential for data security concerns, these are outweighed by the overwhelming benefits of improved efficiency, accuracy, and cost savings provided by GIS data collectors. The market's regional landscape shows significant participation from North America and Europe, owing to established technological infrastructure and early adoption of advanced GIS technologies. However, rapid growth is expected in the Asia-Pacific region, especially in countries like China and India, fueled by infrastructure development and expanding agricultural activities. Leading players like Garmin, Trimble, and Hexagon are driving innovation and competition, while a growing number of regional players offer more cost-effective solutions. The competitive landscape is characterized by a mix of established global players and regional manufacturers. The established players leverage their technological expertise and extensive distribution networks to maintain market leadership. However, the increasing affordability and accessibility of GIS data collection technologies are attracting new entrants, creating a more dynamic market. Future growth will likely be shaped by the integration of artificial intelligence and machine learning into GIS data collection systems, further enhancing data processing capabilities and automation. The continued development of robust and user-friendly software applications will also contribute to market expansion. Furthermore, the adoption of cloud-based GIS platforms is expected to increase, facilitating greater data sharing and collaboration. This convergence of hardware and software innovations will drive market growth and broaden the applications of GIS data collectors across diverse sectors.

Discover the booming GIS Data Collector market, projected to reach $4.7 billion by 2033 with an 8% CAGR. This comprehensive analysis explores market drivers, trends, restraints, key players (Garmin, Trimble, Hexagon), and regional growth opportunities in agriculture, forestry, and industrial applications. Get insights into high-precision vs. general precision segments.

GIS Data Collector Market Outlook

According to our latest research, the global GIS Data Collector market size reached USD 6.8 billion in 2024, reflecting robust demand across multiple industries. The market is projected to grow at a healthy CAGR of 11.2% from 2025 to 2033, reaching an anticipated value of USD 19.7 billion by 2033. This significant expansion is driven by increasing adoption of geospatial technologies in urban planning, environmental monitoring, and the digital transformation strategies of enterprises worldwide. As per our findings, the surge in smart city initiatives and the proliferation of IoT-based mapping solutions are key contributors to the accelerating growth of the GIS Data Collector market globally.

The primary growth driver for the GIS Data Collector market is the escalating need for precise and real-time geospatial data across diverse sectors. Urbanization and the rapid expansion of metropolitan regions have intensified the demand for advanced mapping and surveying tools, enabling city planners and government agencies to make informed decisions. The integration of GIS data collectors with cutting-edge technologies such as artificial intelligence, machine learning, and cloud computing has further enhanced data accuracy and accessibility. As organizations seek to optimize resource allocation and improve operational efficiency, the utilization of GIS data collectors has become indispensable in applications ranging from infrastructure management to disaster response and land administration.

Another crucial factor propelling the market is the growing use of GIS data collectors in environmental monitoring and natural resource management. With the increasing frequency of climate-related events and the global emphasis on sustainability, accurate geospatial data is vital for tracking environmental changes, managing agricultural lands, and monitoring deforestation or water resources. Advanced GIS data collectors equipped with remote sensing and mobile mapping capabilities are enabling stakeholders to gather high-resolution data, analyze spatial patterns, and implement effective conservation strategies. The synergy between GIS and remote sensing technologies is empowering organizations to address environmental challenges more proactively and efficiently.

Technological advancements in data collection methods have also played a pivotal role in shaping the GIS Data Collector market landscape. The advent of unmanned aerial vehicles (UAVs), mobile mapping systems, and real-time kinematic (RTK) GPS has revolutionized the way geospatial data is captured and processed. These innovations have not only improved the accuracy and speed of data collection but have also reduced operational costs and enhanced safety in field surveys. The integration of GIS data collectors with cloud-based platforms allows seamless data sharing and collaboration, fostering a more connected and agile ecosystem for geospatial data management. As industries continue to digitize their operations, the demand for sophisticated and user-friendly GIS data collection solutions is expected to witness sustained growth.

Field Data Collection Software has become an integral component in the realm of GIS data collection, providing users with the capability to efficiently gather, process, and analyze geospatial data in real time. This software facilitates seamless integration with various data collection devices, such as GPS receivers and mobile mapping systems, enabling field operatives to capture high-precision data with ease. The adoption of Field Data Collection Software is particularly beneficial in sectors like urban planning and environmental monitoring, where timely and accurate data is crucial for decision-making. By leveraging cloud-based platforms, this software ensures that data collected in the field is instantly accessible to stakeholders, promoting collaboration and enhancing the overall efficiency of geospatial projects. As the demand for real-time data insights grows, the role of Field Data Collection Software in supporting dynamic and responsive GIS operations continues to expand.

From a regional perspective, North America currently dominates the GIS Data Collector market, followed closely by Europe and Asia Pacific. The strong presence of leading technology providers, substantial investments in smart infrastructure, and suppo

Summary: How to configure Esri Collector for ArcGIS with a Bad Elf GPS Receiver for High-Accuracy Field Data Collection Storymap metadata page: URL forthcoming Possible K-12 Next Generation Science standards addressed:Grade level(s) 1: Standard 1-LS3-1 - Heredity: Inheritance and Variation of Traits - Make observations to construct an evidence-based account that young plants and animals are like, but not exactly like, their parentsGrade level(s) 4: Standard 4-ESS2-2 - Earth’s Systems - Analyze and interpret data from maps to describe patterns of Earth’s featuresGrade level(s) 5: Standard 5-ESS1-2 - Earth’s Place in the Universe - Represent data in graphical displays to reveal patterns of daily changes in length and direction of shadows, day and night, and the seasonal appearance of some stars in the night skyGrade level(s) 6-8: Standard MS-LS4-5 - Biological Evolution: Unity and Diversity - Gather and synthesize information about technologies that have changed the way humans influence the inheritance of desired traits in organisms.Grade level(s) 6-8: Standard MS-LS4-6 - Biological Evolution: Unity and Diversity - Use mathematical representations to support explanations of how natural selection may lead to increases and decreases of specific traits in populations over timeGrade level(s) 6-8: Standard MS-ESS1-3 - Earth’s Place in the Universe - Analyze and interpret data to determine scale properties of objects in the solar systemGrade level(s) 6-8: Standard MS-ESS2-2 - Earth’s Systems - Construct an explanation based on evidence for how geoscience processes have changed Earth’s surface at varying time and spatial scalesGrade level(s) 9-12: Standard HS-LS4-4 - Biological Evolution: Unity and Diversity - Construct an explanation based on evidence for how natural selection leads to adaptation of populationsGrade level(s) 9-12: Standard HS-ESS2-1 - Earth’s Systems - Develop a model to illustrate how Earth’s internal and surface processes operate at different spatial and temporal scales to form continental and ocean-floor features.Most frequently used words:featurebadelfselectgpsApproximate Flesch-Kincaid reading grade level: 9.9. The FK reading grade level should be considered carefully against the grade level(s) in the NGSS content standards above.

It is about updating to GIS information database, Decision Support Tool (DST) in collaboration with IWMI. With the support of the Fish for Livelihoods field team and IPs (MFF, BRAC Myanmar, PACT Myanmar, and KMSS) staff, collection of Global Positioning System GPS location data for year-1 (2019-20) 1,167 SSA farmer ponds, and year-2 (2020-21) 1,485 SSA farmer ponds were completed with different GPS mobile applications: My GPS Coordinates, GPS Status & Toolbox, GPS Essentials, Smart GPS Coordinates Locator and GPS Coordinates. The Soil and Water Assessment Tool (SWAT) model that integrates climate change analysis with water availability will provide an important tool informing decisions on scaling pond adoption. It can also contribute to a Decision Support Tool to better target pond scaling. GIS Data also contribute to identify the location point of the F4L SSA farmers ponds on the Myanmar Map by fiscal year from 1 to 5.

The construction of this data model was adapted from the Telvent Miner & Miner ArcFM MultiSpeak data model to provide interface functionality with Milsoft Utility Solutions WindMil engineering analysis program. Database adaptations, GPS data collection, and all subsequent GIS processes were performed by Southern Geospatial Services for the Town of Apex Electric Utilities Division in accordance to the agreement set forth in the document "Town of Apex Electric Utilities GIS/GPS Project Proposal" dated March 10, 2008. Southern Geospatial Services disclaims all warranties with respect to data contained herein. Questions regarding data quality and accuracy should be directed to persons knowledgeable with the forementioned agreement.The data in this GIS with creation dates between March of 2008 and April of 2024 were generated by Southern Geospatial Services, PLLC (SGS). The original inventory was performed under the above detailed agreement with the Town of Apex (TOA). Following the original inventory, SGS performed maintenance projects to incorporate infrastructure expansion and modification into the GIS via annual service agreements with TOA. These maintenances continued through April of 2024.At the request of TOA, TOA initiated in house maintenance of the GIS following delivery of the final SGS maintenance project in April of 2024. GIS data created or modified after April of 2024 are not the product of SGS.With respect to SGS generated GIS data that are point features:GPS data collected after January 1, 2013 were surveyed using mapping grade or survey grade GPS equipment with real time differential correction undertaken via the NC Geodetic Surveys Real Time Network (VRS). GPS data collected prior to January 1, 2013 were surveyed using mapping grade GPS equipment without the use of VRS, with differential correction performed via post processing.With respect to SGS generated GIS data that are line features:Line data in the GIS for overhead conductors were digitized as straight lines between surveyed poles. Line data in the GIS for underground conductors were digitized between surveyed at grade electric utility equipment. The configurations and positions of the underground conductors are based on TOA provided plans. The underground conductors are diagrammatic and cannot be relied upon for the determination of the actual physical locations of underground conductors in the field.The Service Locations feature class was created by Southern Geospatial Services (SGS) from a shapefile of customer service locations generated by dataVoice International (DV) as part of their agreement with the Town of Apex (TOA) regarding the development and implemention of an Outage Management System (OMS).Point features in this feature class represent service locations (consumers of TOA electric services) by uniquely identifying the features with the same unique identifier as generated for a given service location in the TOA Customer Information System (CIS). This is also the mechanism by which the features are tied to the OMS. Features are physically located in the GIS based on CIS address in comparison to address information found in Wake County GIS property data (parcel data). Features are tied to the GIS electric connectivity model by identifying the parent feature (Upline Element) as the transformer that feeds a given service location.SGS was provided a shapefile of 17992 features from DV. Error potentially exists in this DV generated data for the service location features in terms of their assigned physical location, phase, and parent element.Regarding the physical location of the features, SGS had no part in physically locating the 17992 features as provided by DV and cannot ascertain the accuracy of the locations of the features without undertaking an analysis designed to verify or correct for error if it exists. SGS constructed the feature class and loaded the shapefile objects into the feature class and thus the features exist in the DV derived location. SGS understands that DV situated the features based on the address as found in the CIS. No features were verified as to the accuracy of their physical location when the data were originally loaded. It is the assumption of SGS that the locations of the vast majority of the service location features as provided by DV are in fact correct.SGS understands that as a general rule that DV situated residential features (individually or grouped) in the center of a parcel. SGS understands that for areas where multiple features may exist in a given parcel (such as commercial properties and mobile home parks) that DV situated features as either grouped in the center of the parcel or situated over buildings, structures, or other features identifiable in air photos. It appears that some features are also grouped in roads or other non addressed locations, likely near areas where they should physically be located, but that these features were not located in a final manner and are either grouped or strung out in a row in the general area of where DV may have expected they should exist.Regarding the parent and phase of the features, the potential for error is due to the "first order approximation" protocol employed by DV for assigning the attributes. With the features located as detailed above, SGS understands that DV identified the transformer closest to the service location (straight line distance) as its parent. Phase was assigned to the service location feature based on the phase of the parent transformer. SGS expects that this protocol correctly assigned parent (and phase) to a significant portion of the features, however this protocol will also obviously incorretly assign parent in many instances.To accurately identify parent for all 17992 service locations would require a significant GIS and field based project. SGS is willing to undertake a project of this magnitude at the discretion of TOA. In the meantime, SGS is maintaining (editing and adding to) this feature class as part of the ongoing GIS maintenance agreement that is in place between TOA and SGS. In lieu of a project designed to quality assess and correct for the data provided by DV, SGS will verify the locations of the features at the request of TOA via comparison of the unique identifier for a service location to the CIS address and Wake County parcel data address as issues arise with the OMS if SGS is directed to focus on select areas for verification by TOA. Additionally, as SGS adds features to this feature class, if error related to the phase and parent of an adjacent feature is uncovered during a maintenance, it will be corrected for as part of that maintenance.With respect to the additon of features moving forward, TOA will provide SGS with an export of CIS records for each SGS maintenance, SGS will tie new accounts to a physical location based on address, SGS will create a feature for the CIS account record in this feature class at the center of a parcel for a residential address or at the center of a parcel or over the correct (or approximately correct) location as determined via air photos or via TOA plans for commercial or other relevant areas, SGS will identify the parent of the service location as the actual transformer that feeds the service location, and SGS will identify the phase of the service address as the phase of it's parent.Service locations with an ObjectID of 1 through 17992 were originally physically located and attributed by DV.Service locations with an ObjectID of 17993 or higher were originally physically located and attributed by SGS.DV originated data are provided the Creation User attribute of DV, however if SGS has edited or verified any aspect of the feature, this attribute will be changed to SGS and a comment related to the edits will be provided in the SGS Edits Comments data field. SGS originated features will be provided the Creation User attribute of SGS. Reference the SGS Edits Comments attribute field Metadata for further information.

This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

GIS Controller Market Outlook

The GIS Controller market size was valued at $8.3 billion in 2023 and is projected to reach $15.6 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.2% during the forecast period. This significant growth factor can be attributed primarily to increasing urbanization, the rising need for efficient spatial data management, and technological advancements in geospatial analytics.

One of the prime growth factors driving the GIS Controller market is the escalating demand for smart city solutions. As urbanization continues to rise globally, governments and municipalities are increasingly investing in smart city initiatives to improve urban planning, public safety, and resource management. GIS controllers play a crucial role in these initiatives by providing accurate spatial data, which is essential for efficient infrastructure development, traffic management, and environmental monitoring. Furthermore, the integration of GIS with other technologies such as IoT and AI is opening new avenues for real-time data analysis and decision-making, further propelling market growth.

The agriculture sector is another significant contributor to the growth of the GIS Controller market. Precision farming techniques that leverage GIS technology are gaining traction for their ability to enhance crop yield and optimize resource usage. By providing detailed insights into soil conditions, weather patterns, and crop health, GIS controllers enable farmers to make data-driven decisions, thereby improving operational efficiency and reducing costs. Additionally, government initiatives aimed at promoting sustainable farming practices are further fueling the adoption of GIS technology in the agricultural sector.

Disaster management is another critical application area where GIS controllers are making a substantial impact. The increasing frequency of natural disasters such as hurricanes, floods, and earthquakes necessitates advanced planning and real-time response capabilities. GIS controllers help in mapping disaster-prone areas, predicting the impact of natural calamities, and coordinating emergency response efforts. This capability is invaluable for minimizing damage and saving lives. The growing focus on disaster preparedness and management is expected to drive the demand for GIS controllers in the coming years.

Regionally, North America holds a significant share of the GIS Controller market, driven by the high adoption rate of advanced technologies and substantial investments in smart city projects. The Asia Pacific region is expected to witness the highest growth rate, fueled by rapid urbanization, infrastructural development, and increasing government initiatives for digital transformation. Europe also presents substantial growth opportunities due to the rising focus on environmental sustainability and smart transportation systems.

Component Analysis

The GIS Controller market is segmented into three primary components: Hardware, Software, and Services. The hardware segment includes devices and equipment necessary for capturing and processing geospatial data, such as GPS units, sensors, and data collection devices. This segment is witnessing steady growth due to the increasing need for advanced and accurate data collection tools. The integration of AI and IoT with GIS hardware is further enhancing the capabilities of these devices, making them indispensable for various applications such as urban planning, agriculture, and disaster management.

In terms of software, GIS Controllers are equipped with specialized software for data analysis, mapping, and modeling. This segment is experiencing rapid growth due to the increasing demand for sophisticated analytical tools that can handle large datasets and provide real-time insights. Advanced GIS software solutions are being developed to offer more user-friendly interfaces and better integration with other enterprise systems, thereby enhancing their usability and effectiveness across different sectors. The rise of cloud-based GIS software is also contributing to the growth of this segment by offering scalable and cost-effective solutions.

The services segment comprises consultancy, implementation, and maintenance services essential for the effective deployment and utilization of GIS Controllers. As organizations increasingly adopt GIS technology, the demand for specialized services that can ensure smooth integration and optimal performance is rising. Professional services providers are offering customized solutions to meet the specific needs of different industries

The objective of this research project is to design, develop, and test a pilot collaborative environment between two Irrigation Districts and Reclamation within the Missouri Basin (MB Region). The collaborative environment will utilize ArcGIS Online, ArcGIS Pro, and Field Maps. Through robust testing, the design process, procedural standards, and lessons learned in the implementing stages will be documented and shared with all Regions. This catalog record contains the Final S&T Project Report describing the work done in the project, and two shapefiles with point and line geometry types depicting observation wells and canals obtained from field GPS data collection by Frenchman Cambridge Irrigation District.

The global handheld GNSS receiver market is experiencing robust growth, driven by increasing demand across diverse sectors. Applications like precision agriculture, surveying (GIS), and construction are key contributors, leveraging the technology for accurate positioning and data collection. The aviation industry also utilizes these receivers for navigation and surveying, while the electric power sector benefits from their use in infrastructure maintenance and asset management. Technological advancements, such as the integration of multi-frequency signals and improved accuracy, are further fueling market expansion. The market is segmented by the number of channels (≤1000 and >1000), with higher-channel receivers offering enhanced performance and attracting a premium price point. Leading players like Trimble, NovAtel, and others are investing in R&D to develop more efficient and cost-effective solutions, leading to increased competition and fostering innovation. The market is geographically diverse, with North America and Europe currently holding significant shares, but the Asia-Pacific region is predicted to witness substantial growth due to increasing infrastructure development and adoption in emerging economies. Considering a conservative CAGR estimate of 8% (based on typical growth in related tech sectors) and a 2025 market size of $500 million (a reasonable assumption given the scale of applications and players), the market is projected to expand significantly by 2033. Restraints include the high initial investment costs for some receivers and potential interference from atmospheric conditions. However, ongoing technological advancements and decreasing prices are expected to mitigate these challenges. The market’s future trajectory points towards a growing integration with other technologies like IoT and AI, enabling smarter and more efficient applications in various industries. The competition among established players and emerging companies will likely intensify, driving further innovation and potentially impacting pricing strategies in the coming years.

Geographic Information System (GIS) Market Outlook

The global Geographic Information System (GIS) market size was valued at approximately USD 8.1 billion in 2023 and is projected to reach around USD 16.3 billion by 2032, growing at a CAGR of 8.2% during the forecast period. One of the key growth factors driving this market is the increasing adoption of GIS technology across various industries such as agriculture, construction, and transportation, which is enhancing operational efficiencies and enabling better decision-making capabilities.

Several factors are contributing to the robust growth of the GIS market. Firstly, the increasing need for spatial data in urban planning, infrastructure development, and natural resource management is accelerating the demand for GIS solutions. For instance, governments and municipalities globally are increasingly relying on GIS for planning and managing urban sprawl, transportation systems, and utility networks. This growing reliance on spatial data for efficient resource allocation and policy-making is significantly propelling the GIS market.

Secondly, the advent of advanced technologies like the Internet of Things (IoT), Artificial Intelligence (AI), and machine learning is enhancing the capabilities of GIS systems. The integration of these technologies with GIS allows for real-time data analysis and predictive analytics, making GIS solutions more powerful and valuable. For example, AI-powered GIS can predict traffic patterns and help in effective city planning, while IoT-enabled GIS can monitor and manage utilities like water and electricity in real time, thus driving market growth.

Lastly, the rising focus on disaster management and environmental monitoring is further boosting the GIS market. Natural disasters like floods, hurricanes, and earthquakes necessitate the need for accurate and real-time spatial data to facilitate timely response and mitigation efforts. GIS technology plays a crucial role in disaster risk assessment, emergency response, and recovery planning, thereby increasing its adoption in disaster management agencies. Moreover, environmental monitoring for issues like deforestation, pollution, and climate change is becoming increasingly vital, and GIS is instrumental in tracking and addressing these challenges.

Regionally, the North American market is expected to hold a significant share due to the widespread adoption of advanced technologies and substantial investments in infrastructure development. Asia Pacific is anticipated to witness the fastest growth, driven by rapid urbanization, industrialization, and supportive government initiatives for smart city projects. Additionally, Europe is expected to show steady growth due to stringent regulations on environmental management and urban planning.

Component Analysis

The GIS market by component is segmented into hardware, software, and services. The hardware segment includes devices like GPS, imaging sensors, and other data capture devices. These tools are critical for collecting accurate spatial data, which forms the backbone of GIS solutions. The demand for advanced hardware components is rising, as organizations seek high-precision instruments for data collection. The advent of technologies such as LiDAR and drones has further enhanced the capabilities of GIS hardware, making data collection faster and more accurate.

In the software segment, GIS platforms and applications are used to store, analyze, and visualize spatial data. GIS software has seen significant advancements, with features like 3D mapping, real-time data integration, and cloud-based collaboration becoming increasingly prevalent. Companies are investing heavily in upgrading their GIS software to leverage these advanced features, thereby driving the growth of the software segment. Open-source GIS software is also gaining traction, providing cost-effective solutions for small and medium enterprises.

The services segment encompasses various professional services such as consulting, integration, maintenance, and training. As GIS solutions become more complex and sophisticated, the need for specialized services to implement and manage these systems is growing. Consulting services assist organizations in selecting the right GIS solutions and integrating them with existing systems. Maintenance and support services ensure that GIS systems operate efficiently and remain up-to-date with the latest technological advancements. Training services are also crucial, as they help users maximize the potential of GIS technologies.

This is a point data set representing monumented vertical geodetic survey control points (a.k.a. elevation benchmarks) established by the City of Boise. A benchmark is a physical marker, monument, or demarcation established by a surveyor for horizontal and/or vertical measurement control. This data set only contains benchmarks established by the City of Boise that are based on the North American Vertical Datum of 1988 (NAVD 88); benchmarks established under other vertical datums are not included. The elevation values in this data set are based on the vertical control from the National Geodetic Survey (NGS), the U. S. Geological Survey (USGS), and some state owned vertical control. The horizontal location is obtained by Global Positioning System (GPS) data collection of the surveyed benchmark. Attribute data including elevation, is transcribed from surveying field books supplied by Boise City Public Works survey personnel. Data Attributes:MARK TYPE: ALCAP - Aluminum Cap, BRASSCAP - Brass Cap, CHISELSQ - physically carved square, PK - A steel masonry nail manufactured by Parker Kaelon (PK nail), OTHER - Any other survey marker type.LOCATION: Nearest street or cross streets to the benchmark.ELEVATION: The vertical elevation in feet above sea level as established from survey calculations based on the 1988 NAVD Datum.GPS DATE: Date the benchmark was captured by GPS.COMMENTS: Pertinent notes on general description and location of the benchmark.BOOK: The City of Boise Public Works surveying field book number the benchmark was established under.PAGE: The City of Boise Public Works surveying field book page the benchmark was established under.The data set is maintained by the Boise City Public Works GIS staff. The data is updated continuously. It is current to the date it was published.For more information, please visit Ada County Control Information or City of Boise Public Works.

This layer is maintained by the public works department. It is an inventory of tree sites originally performed by the Davey Tree Company in 2011. Updates have been applied by the GIS Technician in the Public Works Department. Additions and changes are determined by Forestry Staff based on GPS data collection, maps, site visits, and staff knowledge.

Tree Data 2018 - metadata info:

Summer 2018 Tree Data collection, around 3,400 trees were tagged and GPS using a Trimble Geo 7x, sites include 11 parks in the City of Utica, and MVCC Utica Campus. Data from Mohawk Valley Community College. Data obtained from a NYSDEC Urban Forestry Grant. https://www.youtube.com/channel/UCi7dr2NSOHOQLDcHOERSl8g?app=desktop

Spatial Reference of Source Data: State Plane NAD83 Feet Central Zone. Spatial Reference of Web Service: WGS 1984 Web Mercator Auxiliary Sphere.

Data current as of 2018.

For full FGDC metadata record, please click here.These data represent Staging and Response Locations collected by GPS for Mississippi, Alabama, and the Florida Panhandle prior to the Deepwater Horizon Oil Spill. The locations for the Peninsular portion of Florida, Georgia, South Carolina, Puerto Rico, and the US Virgin Islands have been compiled from numerous sources into this database schema and will at some later date (after Nov. 2010) be verified and validated by GPS. Staging and response locations were identified first by defining the types of locations that fit these descriptions. The broad categories were defined as Boat Ramp, Marina, Staging Area, or any combination of these. A marina may contain a boat ramp as well as a large parking lot with a seawall suitable for deploying equipment into the water. A staging area may contain just a waterfront park with access to the water, but no boat ramp or marina, but perhaps a dock or pier. These categories and attributes were used to design a specific database schema to collect information on these geographic features that could be used on a GPS-enabled field data collection device. Once the categories of information to be collected and the specifics of what types of information to be collected within each category were determined (the database schema), mobile devices were programmed to accomplish this task and area committee volunteers were used to conduct the field surveys. Field crews were given training on the devices. Guided by base maps identifying potential locations, they then traveled into the field to validate and collect specific GPS and attribute data on those locations. This was a cooperative effort between many federal, state, and local entities guided by FWC-FWRI that resulted in detailed and location-specific information on 366 staging area locations within Sector Mobile and a comprehensive GIS data set that is available on the DVD ROM and website as well a being used in the Geographic Response Plan Map Atlas production. Cyber-Tracker was the software used for this field data collection. Cyber-Tracker is a "shareware" software package developed as a data-capture tool designed for use in Environmental Conservation, Wildlife Biology and Disaster Relief. The software runs on numerous types of mobile devices and designing custom data capture processes for these devices requires no programming experience. Funded in large part by the European Commission and patroned by Harvard University, Cyber-Tracker Software has been a very valuable tool in the data collection efforts of this project. Cyber-Tracker Software can be found on the Internet at: http://www.cybertracker.co.za/.

GIS (Geographic Information System) data, which includes spatial data such as maps, satellite imagery, and other geospatial data, is typically created using various techniques and methods to ensure its accuracy, completeness, and reliability. The process of creating GIS data for use in metadata involves several key steps, which may include: Data Collection: The first step in creating GIS data for metadata is data collection. This may involve gathering data from various sources, such as field surveys, remote sensing, aerial photography, or existing datasets. Data can be collected using GPS (Global Positioning System) receivers, satellite imagery, LiDAR (Light Detection and Ranging) technology, or other data acquisition methods.Data Validation and Quality Control: Once data is collected, it goes through validation and quality control processes to ensure its accuracy and reliability. This may involve comparing data against known standards or specifications, checking for data errors or inconsistencies, and validating data attributes to ensure they meet the desired accuracy requirements.Data Processing and Analysis: After validation and quality control, data may be processed and analyzed to create meaningful information. This may involve data integration, data transformation, spatial analysis, and other geoprocessing techniques to derive new datasets or generate metadata.Metadata Creation: Metadata, which is descriptive information about the GIS data, is created based on established standards or guidelines. This may include information such as data source, data quality, data format, spatial extent, projection information, and other relevant details that provide context and documentation about the GIS data.Metadata Documentation: Once metadata is created, it needs to be documented in a standardized format. This may involve using metadata standards such as ISO 19115, FGDC (Federal Geographic Data Committee), or other industry-specific standards. Metadata documentation typically includes information about the data source, data lineage, data quality, spatial reference system, attributes, and other relevant information that describes the GIS data and its characteristics.Data Publishing: Finally, GIS data and its associated metadata may be published or made accessible to users through various means, such as online data portals, web services, or other data dissemination methods. Metadata is often used to facilitate data discovery, evaluation, and use, providing users with the necessary information to understand and utilize the GIS data effectively.Overall, the process of creating GIS data for use in metadata involves data collection, validation, processing, analysis, metadata creation, documentation, and data publishing, following established standards or guidelines to ensure accuracy, reliability, and interoperability of the GIS data.

Trails data compiled by NH Office of Strategic Initiatives and NH Fish and Game Department from multiple public information sources including online maps and spatial data, orthophotos, and field data collection with handheld GPS units. Positional accuracy will vary. Coverage and attributes should not be construed as complete.

Digital Data from VG09-4 (Digitized draft of VG97-5): Kim, J., 2009, Bedrock geologic map of parts of the Eden, Albany, Lowell, and Irasburg quadrangles, VGS Open-File Report VG09-4, 1 plate, scale 1:24000. The bedrock geologic map data at a scale of 1:24,000 depicts types of bedrock underlying unconsolidated materials in Vermont. Data is created by mapping on the ground using standard geologic pace and compass techniques and/or GPS on a USGS 1:24000 topographic base map. Data may be organized by town, quadrangle or watershed. Each data bundle may includes point, line and polygon data and some or all of the following: 1) contacts (lithogic contacts), 2) fault_brittle, 3) fault_ductile, 4) fault_thrust, 5) fault_bed_plane (bedding plane thrust), 6) bedding, 7) bedding_graded (graded bedding) 8) bedding_overturn (overturned bedding), 9) bedding_select (selected points for published map), 10) foliation_n1, n2, n3 etc (foliation data), 11) outcrop (exposed outcrops), 12) field_station (outcrop and data collection point), 13) fold_axis, 14) axial_plane, 15) lamprophyre, 16) water_well_log (water well driller information), 16) linear_int (intersection lineation), 17) linear_str (stretching lineation) 18) x_section_line (line of cross-section), and photolinear (lineaments identified from air photos). Other feature classes may be included with each data bundle. (https://dec.vermont.gov/geological-survey/publication-gis/ofr).

Geospatial Analytics Market Outlook

In 2023, the global geospatial analytics market size was valued at approximately USD 55 billion and is projected to reach around USD 165 billion by 2032, growing at a CAGR of 12.5% during the forecast period. The market is driven by technological advancements and the increasing need for geospatial data across various industries.

One of the key growth factors of the geospatial analytics market is the rapid advancement in geospatial technologies such as Geographic Information Systems (GIS), remote sensing, and global positioning systems (GPS). These technologies have significantly enhanced the accuracy and efficiency of data collection, analysis, and interpretation. Additionally, the integration of artificial intelligence (AI) and machine learning (ML) algorithms with geospatial analytics has further augmented its capabilities, making it an indispensable tool for decision-making across diverse sectors.

Another significant driver of the geospatial analytics market is the growing adoption of location-based services and real-time data analysis. With the proliferation of smartphones and IoT devices, there is an increasing demand for applications that provide real-time location data. This has led to a surge in the use of geospatial analytics in urban planning, transportation and logistics, and disaster management. Companies and governments are leveraging geospatial data to optimize routes, manage resources efficiently, and respond swiftly to emergencies.

Furthermore, the rising awareness about climate change and environmental sustainability has propelled the use of geospatial analytics in climate change adaptation and environmental monitoring. Governments and organizations are increasingly relying on geospatial data to understand environmental changes, assess risks, and devise strategies for climate resilience. This trend is particularly significant in regions prone to natural disasters, where timely and accurate geospatial data can save lives and minimize damages.

From a regional perspective, North America holds a significant share of the geospatial analytics market, driven by the presence of major technology companies and extensive government initiatives focused on smart city development and environmental conservation. Europe follows closely, with substantial investments in geospatial technologies for urban planning and infrastructure development. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by rapid urbanization, industrialization, and government initiatives to enhance geospatial infrastructure.

Component Analysis

The geospatial analytics market is segmented into three main components: software, hardware, and services. Each of these components plays a pivotal role in the functioning and advancement of geospatial analytics. Starting with software, which encompasses a wide array of applications such as Geographic Information Systems (GIS), remote sensing software, and enterprise geospatial solutions. GIS software, in particular, is integral to the collection, storage, analysis, and visualization of geospatial data, enabling organizations to make informed decisions based on spatial patterns and relationships.

Hardware components in the geospatial analytics market include devices and equipment used for data collection and processing, such as GPS devices, drones, LiDAR sensors, and remote sensing satellites. These hardware components are essential for acquiring high-resolution geospatial data from various sources, providing a comprehensive view of geographical areas. The evolution of drone technology and advancements in satellite imaging have significantly enhanced the capability to capture accurate and detailed geospatial information, driving the demand for advanced hardware solutions.

Services in the geospatial analytics market encompass a range of offerings, including consulting, integration, maintenance, and support services. These services are crucial for the successful implementation and operation of geospatial analytics solutions. Consulting services help organizations identify the most suitable geospatial technologies and strategies to meet their specific needs. Integration services ensure seamless deployment of geospatial solutions within existing IT infrastructures, while maintenance and support services provide ongoing technical assistance and updates to keep the systems running smoothly.

The interplay between software, hardware, and services is critical for the effective utilization