Mobile GIS Data Collection Software Market Outlook

According to our latest research, the global mobile GIS data collection software market size reached USD 1.64 billion in 2024. The market is experiencing robust expansion, driven by the increasing demand for real-time geospatial data across industries. The market is projected to grow at a CAGR of 14.2% from 2025 to 2033, reaching a forecasted value of USD 4.46 billion by 2033. This growth is primarily fueled by the widespread adoption of mobile GIS solutions for field data collection, asset management, and environmental monitoring, as organizations seek efficient, accurate, and scalable geospatial data collection tools to enhance operational decision-making.

One of the primary growth factors propelling the mobile GIS data collection software market is the rapid digital transformation occurring across multiple sectors, such as utilities, government, agriculture, and transportation. Organizations are increasingly recognizing the value of real-time geospatial data in optimizing workflows, improving resource allocation, and ensuring regulatory compliance. The integration of mobile GIS solutions with Internet of Things (IoT) devices and advanced sensors enables seamless data capture, transmission, and analysis, empowering field teams to make informed decisions on the go. Furthermore, advancements in mobile hardware and connectivity, such as the proliferation of 5G networks, have significantly enhanced the usability and effectiveness of mobile GIS platforms, making them indispensable tools for field operations.

Another significant driver is the growing emphasis on environmental monitoring and sustainability initiatives worldwide. Governments and private organizations are leveraging mobile GIS data collection software to track environmental parameters, monitor land use changes, and support conservation efforts. The ability to collect, visualize, and analyze spatial data in real time is critical for managing natural resources, assessing environmental risks, and responding to emergencies such as natural disasters or hazardous material spills. As climate change concerns intensify and regulatory frameworks become more stringent, the demand for robust and scalable mobile GIS solutions is expected to rise, further boosting market growth.

The market is also benefiting from the increasing adoption of cloud-based mobile GIS solutions, which offer unparalleled scalability, flexibility, and cost-effectiveness. Cloud deployment enables organizations to centralize data storage, streamline collaboration, and ensure data integrity across geographically dispersed teams. The shift towards Software-as-a-Service (SaaS) models is reducing the upfront costs associated with traditional GIS deployments and making advanced geospatial analytics accessible to small and medium-sized enterprises (SMEs) as well as large corporations. This democratization of GIS technology is expanding the addressable market and fostering innovation in application development, user experience, and integration capabilities.

Regionally, North America remains the dominant market, accounting for the largest revenue share in 2024, driven by high technology adoption, a mature IT infrastructure, and the presence of leading GIS software providers. However, Asia Pacific is emerging as the fastest-growing region, supported by rapid urbanization, infrastructure development, and government initiatives promoting digital transformation. Europe also holds a significant market share, particularly in sectors such as utilities management and environmental monitoring. Meanwhile, Latin America and the Middle East & Africa are witnessing increasing investments in GIS technologies, reflecting the global trend toward smarter, data-driven decision-making across industries.

Component Analysis

The mobile GIS data collection software market is segmented by component into software and services, each playing a pivotal role in driving the adoption and effectiveness of GIS solutions. The software segment encompasses a wide array of applications designed for data capture, visualization, editing, and analysis on mobile devices. These software solutions are increasingly equipped with advanced features such as offline data collection, real-time synchronization, customizable workflows, and integration with third-party systems. The evolution of user-friendly interfaces and mobile-first design principles has further acceler

The China Administrative Regions GIS Data: 1:1M, County Level, 1990 consists of geographic boundary data for the administrative regions of China as of 31 December 1990. The data includes the geographical location, area, administrative division code, and county and island name. The data are at a scale of one to one million (1:1M) at the national, provincial, and county level. This data set is produced in collaboration with the Center for International Earth Science Information Network (CIESIN), Chinese Academy of Surveying and Mapping (CASM), and the University of Washington as part of the China in Time and Space (CITAS) project.

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.

This dataset contains the West Siberian Lowland (WSL) peatland GIS data collection. The collection covers the entire West Siberian lowland and was compiled from a wide array of data under the auspices of the NSF-funded Sensitivity of the West Siberian Lowland to Past and Present Climate project (Smith et al., 2000; Smith et al., 2004). Detailed physical characteristics of 9,691 individual peatlands (patches) were obtained from previously unpublished Russian field and ancillary map data, previously published depth measurements, and field depth and core measurements taken throughout the region during field campaigns in 1999, 2000, and 2001. The data collection features eight layers containing the detailed peatland inventory, political, and hydrographic information. Point data consist of field and laboratory measurements of peat depth, ash content, and bulk density. This research was funded by the National Science Foundation (NSF) Office of Polar Programs (OPP), grant number OPP-9818496.

The Fundamental GIS: Digital Chart of China, 1:1M, Version 1 consists of vector maps of China and surrounding areas. The maps include roads, railroads, drainage systems, contours, populated places, and urbanized areas for China proper, as well as for China and neighboring countries. The maps are at a scale of one to one million (1:1M). This data set is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project and the Columbia University Center for International Earth Science Information Network (CIESIN).

Overview: This document is a reference guide for users of the SAR Field Data Collection Form User Guide. The purpose is to provide a better understanding of how to use the form in the field.

The underlying technology used with this form is likely to evolve and change over time, therefore technical user guides will be provided as appendices to this document.

Background: The SAR Field Data Collection Form was created by an interdisciplinary group of first responders, decision-makers and technology specialists from across Federal, State, and Local Urban Search and Rescue Teams – the NAPSG Foundation SAR Working Group. If you have any questions or concerns regarding this document and associated materials, please send a note to comments@publicsafetygis.org.

Purpose: The SAR Field Data Collection Form is intended to provide a standardized approach to the collection of information during disaster response alongside resource management and tracking of assets.The primary goal of this approach is to obtain situational awareness (where, when, what) for SAR Teams in the field across four relevant themes: Victims that may need assistance or have already been helped. Hazards that must be avoided or mitigated. Damage that have been rapidly assessed for damage, when time and the mission permits. Other mission critical intelligence that vary based on mission type. The secondary goal of this approach is to provide essential elements of information to those not currently on-scene of the disaster. Using the themes above, information and maps can be shared based on “need to know”. If you are a technology specialist looking to deploy this application on your own see the Deployment Kit.

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.

This study focuses on the use of citizen science and GIS tools for collecting and analyzing data on Rose Swanson Mountain in British Columbia, Canada. While several organizations collect data on wildlife habitats, trail mapping, and fire documentation on the mountain, there are few studies conducted on the area and citizen science is not being addressed. The study aims to aggregate various data sources and involve citizens in the data collection process using ArcGIS Dashboard and ArcGIS Survey 123. These GIS tools allow for the integration and analysis of different kinds of data, as well as the creation of interactive maps and surveys that can facilitate citizen engagement and data collection. The data used in the dashboard was sourced from BC Data Catalogue, Explore the Map, and iNaturalist. Results show effective citizen participation, with 1073 wildlife observations and 3043 plant observations. The dashboard provides a user-friendly interface for citizens to tailor their map extent and layers, access surveys, and obtain information on each attribute included in the pop-up by clicking. Analysis on classification of fuel types, ecological communities, endangered wildlife species presence and critical habitat, and scope of human activities can be conducted based on the distribution of data. The dashboard can provide direction for researchers to develop research or contribute to other projects in progress, as well as advocate for natural resource managers to use citizen science data. The study demonstrates the potential for GIS and citizen science to contribute to meaningful discoveries and advancements in areas.

As GIS and computing technologies advanced rapidly, many indoor space studies began to adopt GIS technology, data models, and analysis methods. However, even with a considerable amount of research on indoor GIS and various indoor systems developed for different applications, there has not been much attention devoted to adopting indoor GIS for the evaluation space usage. Applying indoor GIS for space usage assessment can not only provide a map-based interface for data collection, but also brings spatial analysis and reporting capabilities for this purpose. This study aims to explore best practice of using an indoor GIS platform to assess space usage and design a complete indoor GIS solution to facilitate and streamline the data collection, a management and reporting workflow. The design has a user-friendly interface for data collectors and an automated mechanism to aggregate and visualize the space usage statistics. A case study was carried out at the Purdue University Libraries to assess study space usage. The system is efficient and effective in collecting student counts and activities and generating reports to interested parties in a timely manner. The analysis results of the collected data provide insights into the user preferences in terms of space usage. This study demonstrates the advantages of applying an indoor GIS solution to evaluate space usage as well as providing a framework to design and implement such a system. The system can be easily extended and applied to other buildings for space usage assessment purposes with minimal development efforts.

Utility GIS Field Data Collection Market Outlook

As per our latest research, the global Utility GIS Field Data Collection market size in 2024 stands at USD 1.62 billion, reflecting the sector’s robust expansion driven by the digital transformation of utility infrastructure management. The market is experiencing a strong compound annual growth rate (CAGR) of 11.2% from 2025 to 2033. By 2033, the market is forecasted to reach USD 4.22 billion, underpinned by rising investments in smart grid technologies, increasing regulatory mandates for accurate geospatial data, and the growing need for efficient asset management across electric, water, gas, and telecommunication utilities.

The primary growth factor for the Utility GIS Field Data Collection market is the accelerating adoption of Geographic Information Systems (GIS) in field operations to enhance the accuracy, efficiency, and reliability of utility asset management. Utilities across the globe are increasingly leveraging advanced GIS-enabled field data collection tools to streamline processes such as asset mapping, network inspections, and maintenance scheduling. The integration of real-time data collection with cloud-based GIS platforms enables field workers to capture, update, and synchronize geospatial data instantaneously, reducing manual errors and operational downtime. This digital shift is further fueled by the proliferation of mobile devices and IoT sensors, which allow utilities to gather granular data from remote locations, supporting predictive maintenance and rapid response to outages or infrastructure issues.

Another critical driver is the mounting regulatory pressure and compliance requirements imposed by government agencies and industry bodies, particularly in regions with aging utility infrastructure. Utilities are mandated to maintain accurate, up-to-date geospatial records to ensure public safety, environmental protection, and efficient resource allocation. The deployment of GIS field data collection solutions facilitates compliance by providing comprehensive audit trails, real-time reporting, and seamless integration with enterprise asset management (EAM) systems. As governments worldwide invest in smart city initiatives and infrastructure modernization, the demand for advanced GIS capabilities in field data collection is expected to surge, creating new opportunities for software vendors, hardware providers, and service integrators.

Moreover, the growing complexity of utility networks, coupled with the increasing frequency of extreme weather events and natural disasters, necessitates robust field data collection systems for rapid damage assessment and recovery planning. GIS-based field data collection tools empower utilities to quickly map affected areas, prioritize restoration efforts, and communicate effectively with stakeholders. The ability to overlay real-time field data with historical geospatial information enhances situational awareness and supports data-driven decision-making. As utilities strive to enhance operational resilience and customer service, the adoption of advanced GIS field data collection solutions is poised to become a strategic imperative.

Regionally, North America leads the Utility GIS Field Data Collection market, accounting for over 38% of the global market share in 2024, followed by Europe and Asia Pacific. The United States and Canada are at the forefront of adoption, driven by significant investments in grid modernization and stringent regulatory frameworks. Europe is witnessing steady growth, propelled by the digital transformation of water and gas utilities and the implementation of the European Green Deal. Meanwhile, the Asia Pacific region is emerging as a high-growth market, fueled by rapid urbanization, expanding utility networks, and government-led smart infrastructure projects in countries such as China, India, and Australia. Latin America and the Middle East & Africa are also showing increasing interest in GIS field data collection solutions to address infrastructure challenges and improve service delivery.

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.

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.

The USDA Long-Term Agroecosystem Research was established to develop national strategies for sustainable intensification of agricultural production. As part of the Agricultural Research Service, the LTAR Network incorporates numerous geographies consisting of experimental areas and locations where data are being gathered. Starting in early 2019, two working groups of the LTAR Network (Remote Sensing and GIS, and Data Management) set a major goal to jointly develop a geodatabase of LTAR Standard GIS Data Layers. The purpose of the geodatabase was to enhance the Network's ability to utilize coordinated, harmonized datasets and reduce redundancy and potential errors associated with multiple copies of similar datasets. Project organizers met at least twice with each of the 18 LTAR sites from September 2019 through December 2020, compiling and editing a set of detailed geospatial data layers comprising a geodatabase, describing essential data collection areas within the LTAR Network. The LTAR Standard GIS Data Layers geodatabase consists of geospatial data that represent locations and areas associated with the LTAR Network as of late 2020, including LTAR site locations, addresses, experimental plots, fields and watersheds, eddy flux towers, and phenocams. There are six data layers in the geodatabase available to the public. This geodatabase was created in 2019-2020 by the LTAR network as a national collaborative effort among working groups and LTAR sites. The creation of the geodatabase began with initial requests to LTAR site leads and data managers for geospatial data, followed by meetings with each LTAR site to review the initial draft. Edits were documented, and the final draft was again reviewed and certified by LTAR site leads or their delegates. Revisions to this geodatabase will occur biennially, with the next revision scheduled to be published in 2023. Resources in this dataset:Resource Title: LTAR Standard GIS Data Layers, 2020 version, File Geodatabase. File Name: LTAR_Standard_GIS_Layers_v2020.zipResource Description: This file geodatabase consists of authoritative GIS data layers of the Long-Term Agroecosystem Research Network. Data layers include: LTAR site locations, LTAR site points of contact and street addresses, LTAR experimental boundaries, LTAR site "legacy region" boundaries, LTAR eddy flux tower locations, and LTAR phenocam locations.Resource Software Recommended: ArcGIS,url: esri.com Resource Title: LTAR Standard GIS Data Layers, 2020 version, GeoJSON files. File Name: LTAR_Standard_GIS_Layers_v2020_GeoJSON_ADC.zipResource Description: The contents of the LTAR Standard GIS Data Layers includes geospatial data that represent locations and areas associated with the LTAR Network as of late 2020. This collection of geojson files includes spatial data describing LTAR site locations, addresses, experimental plots, fields and watersheds, eddy flux towers, and phenocams. There are six data layers in the geodatabase available to the public. This dataset was created in 2019-2020 by the LTAR network as a national collaborative effort among working groups and LTAR sites. Resource Software Recommended: QGIS,url: https://qgis.org/en/site/

In this seminar, you will learn how to use Collector for ArcGIS to download maps, create new GIS features, as well as update existing ones when disconnected from the Internet, and then synchronize changes back to the office when you are connected. In addition, you will learn how to create maps and publish services for devices.This seminar was developed to support the following:Collector for ArcGIS (Android) 10.2Collector for ArcGIS (iOS) 10.2

The China County-Level Data on Population (Census) and Agriculture, Keyed To 1:1M GIS Map consists of census, agricultural economic, and boundary data for the administrative regions of China for 1990. The census data includes urban and rural residency, age and sex distribution, educational attainment, illiteracy, marital status, childbirth, mortality, immigration (since 1985), industrial/economic activity, occupation, and ethnicity. The agricultural economic data encompasses rural population, labor force, forestry, livestock and fishery, commodities, equipment, utilities, irrigation, and output value. The boundary data are at a scale of one to one million (1:1M) at the county level. This data set is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project, University of California-Davis China in Time and Space (CITAS) project, and the Center for International Earth Science Information Network (CIESIN).

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Geographic Information System Analytics Market Size 2024-2028

The geographic information system analytics market size is forecast to increase by USD 12 billion at a CAGR of 12.41% between 2023 and 2028.

The GIS Analytics Market analysis is experiencing significant growth, driven by the increasing need for efficient land management and emerging methods in data collection and generation. The defense industry's reliance on geospatial technology for situational awareness and real-time location monitoring is a major factor fueling market expansion. Additionally, the oil and gas industry's adoption of GIS for resource exploration and management is a key trend. Building Information Modeling (BIM) and smart city initiatives are also contributing to market growth, as they require multiple layered maps for effective planning and implementation. The Internet of Things (IoT) and Software as a Service (SaaS) are transforming GIS analytics by enabling real-time data processing and analysis.
Augmented reality is another emerging trend, as it enhances the user experience and provides valuable insights through visual overlays. Overall, heavy investments are required for setting up GIS stations and accessing data sources, making this a promising market for technology innovators and investors alike.

What will be the Size of the GIS Analytics Market during the forecast period?

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The geographic information system analytics market encompasses various industries, including government sectors, agriculture, and infrastructure development. Smart city projects, building information modeling, and infrastructure development are key areas driving market growth. Spatial data plays a crucial role in sectors such as transportation, mining, and oil and gas. Cloud technology is transforming GIS analytics by enabling real-time data access and analysis. Startups are disrupting traditional GIS markets with innovative location-based services and smart city planning solutions. Infrastructure development in sectors like construction and green buildings relies on modern GIS solutions for efficient planning and management. Smart utilities and telematics navigation are also leveraging GIS analytics for improved operational efficiency.
GIS technology is essential for zoning and land use management, enabling data-driven decision-making. Smart public works and urban planning projects utilize mapping and geospatial technology for effective implementation. Surveying is another sector that benefits from advanced GIS solutions. Overall, the GIS analytics market is evolving, with a focus on providing actionable insights to businesses and organizations.

How is this Geographic Information System Analytics Industry segmented?

The geographic information system analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Retail and Real Estate
  Government
  Utilities
  Telecom
  Manufacturing and Automotive
  Agriculture
  Construction
  Mining
  Transportation
  Healthcare
  Defense and Intelligence
  Energy
  Education and Research
  BFSI


Components

  Software
  Services


Deployment Modes

  On-Premises
  Cloud-Based


Applications

  Urban and Regional Planning
  Disaster Management
  Environmental Monitoring Asset Management
  Surveying and Mapping
  Location-Based Services
  Geospatial Business Intelligence
  Natural Resource Management


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    South Korea


  Middle East and Africa

    UAE


  South America

    Brazil


  Rest of World

By End-user Insights

The retail and real estate segment is estimated to witness significant growth during the forecast period.

The GIS analytics market analysis is witnessing significant growth due to the increasing demand for advanced technologies in various industries. In the retail sector, for instance, retailers are utilizing GIS analytics to gain a competitive edge by analyzing customer demographics and buying patterns through real-time location monitoring and multiple layered maps. The retail industry's success relies heavily on these insights for effective marketing strategies. Moreover, the defense industries are integrating GIS analytics into their operations for infrastructure development, permitting, and public safety. Building Information Modeling (BIM) and 4D GIS software are increasingly being adopted for construction project workflows, while urban planning and designing require geospatial data for smart city planning and site selection.

The oil and gas industry is leveraging satellite imaging and IoT devices for land acquisition and mining operations. In the public sector, gover

This database was prepared using a combination of materials that include aerial photographs, topographic maps (1:24,000 and 1:250,000), field notes, and a sample catalog. Our goal was to translate sample collection site locations at Yellowstone National Park and surrounding areas into a GIS database. This was achieved by transferring site locations from aerial photographs and topographic maps into layers in ArcMap. Each field site is located based on field notes describing where a sample was collected. Locations were marked on the photograph or topographic map by a pinhole or dot, respectively, with the corresponding station or site numbers. Station and site numbers were then referenced in the notes to determine the appropriate prefix for the station. Each point on the aerial photograph or topographic map was relocated on the screen in ArcMap, on a digital topographic map, or an aerial photograph. Several samples are present in the field notes and in the catalog but do not correspond to an aerial photograph or could not be found on the topographic maps. These samples are marked with “No” under the LocationFound field and do not have a corresponding point in the SampleSites feature class. Each point represents a field station or collection site with information that was entered into an attributes table (explained in detail in the entity and attribute metadata sections). Tabular information on hand samples, thin sections, and mineral separates were entered by hand. The Samples table includes everything transferred from the paper records and relates to the other tables using the SampleID and to the SampleSites feature class using the SampleSite field.

GapMaps GIS Data by Azira uses location data on mobile phones sourced by Azira which is collected from smartphone apps when the users have given their permission to track their location. It can shed light on consumer visitation patterns (“where from” and “where to”), frequency of visits, profiles of consumers and much more.

Businesses can utilise GIS data to answer key questions including: - What is the demographic profile of customers visiting my locations? - What is my primary catchment? And where within that catchment do most of my customers travel from to reach my locations? - What points of interest drive customers to my locations (ie. work, shopping, recreation, hotel or education facilities that are in the area) ? - How far do customers travel to visit my locations? - Where are the potential gaps in my store network for new developments?
- What is the sales impact on an existing store if a new store is opened nearby? - Is my marketing strategy targeted to the right audience? - Where are my competitor's customers coming from?

Mobile Location data provides a range of benefits that make it a valuable GIS Data source for location intelligence services including: - Real-time - Low-cost at high scale - Accurate - Flexible - Non-proprietary - Empirical

Azira have created robust screening methods to evaluate the quality of Mobile location data collected from multiple sources to ensure that their data lake contains only the highest-quality mobile location data.

This includes partnering with trusted location SDK providers that get proper end user consent to track their location when they download an application, can detect device movement/visits and use GPS to determine location co-ordinates.

Data received from partners is put through Azira's data quality algorithm discarding data points that receive a low quality score.

Use cases in Europe will be considered on a case to case basis.

There will be two different software applications for the collecting of data which will be explored in this lesson, Survey 123 and Field Maps. There are other uses of survey apps which will be discussed, such as the registrations for meetings.There are other field data collection software that might have been used in the past such as Esri Collector or an open source data collection app. These two applications, Survey 123 and Field Maps will provide the functional foundations required to do field data work.In addition to exploring these two applications, webhooks will be explored within Survey123. A webhook provides an interfaces between software packages and a way of transferring data and information. There are both free and commercial software that are designed to create webhooks. For example, when you fill out a registration form for an event, and you receive an email confirming your registration has been completed, this is done using a webhook.

This is a national data collection of data resources managed by the Bureau of Ocean Energy Management (BOEM) for the Outer Continental Shelf (OCS). The data collection is designated as a National Geospatial Data Asset (NGDA) and includes: OCS BOEM Offshore Boundary Lines (Submerged Lands Act Boundary, OCSLA Limit of “8(g) Zone,” and Continental Shelf Boundary), OCS Protraction Polygons - 1st Division, OCS Gulf of Mexico NAD27 Protraction Polygons - 1st Division, OCS Block Polygons - 2nd Division, OCS Gulf of Mexico NAD27 Block Polygons - 2nd Division, and Aliquot 16ths Polygons - 3rd Division.All polygons are clipped to the Submerged Land Act Boundary and Continental Shelf Boundaries reflecting federal jurisdiction. The NAD27 Gulf of Mexico Protractions and Blocks have a different protraction and block configuration when compared to the OCS Protraction Polygons - 1st Division and OCS Block Polygons - 2nd Division. The NAD27 Gulf of Mexico data is used for Oil and Gas leasing.These data were created in the applicable NAD83 UTM or NAD27 UTM/SPCS Projection and re-projected to GCS WGS84 (EPSG 4326) for management in BOEM"s enterprise GIS. However, the services in this collection have been published in WGS 1984 Web Mercator Auxiliary Sphere (EPSG 3857). Because GIS projection and topology functions can change or generalize coordinates,these data are NOT an OFFICIAL record for the exact boundaries. These data are to be used for Cartographic purposes only and should not be used to calculate area.Layers MetadataOCS BOEM Offshore Boundary LinesOCS Protraction Polygons - 1st DivisionOCS Gulf of Mexico NAD27 Protraction Polygons - 1st DivisionOCS Block Polygons - 2nd DivisionOCS Gulf of Mexico NAD27 Block Polygons - 2nd DivisionAliquot 16ths Polygons - 3rd Division