Mobile GIS Data Collection Software Market Outlook

According to our latest research, the global Mobile GIS Data Collection Software market size reached USD 2.14 billion in 2024, and is anticipated to grow at a robust CAGR of 13.7% during the forecast period, reaching approximately USD 6.42 billion by 2033. This strong growth trajectory is primarily driven by the increasing demand for real-time geospatial data across multiple industries, the proliferation of mobile devices, and the integration of advanced technologies such as IoT and AI into GIS solutions. As organizations globally seek to enhance operational efficiency and decision-making capabilities, the adoption of mobile GIS data collection software continues to accelerate, reshaping the landscape of field data management and spatial analytics.

One of the pivotal growth factors for the Mobile GIS Data Collection Software market is the rapid digital transformation across industries such as utilities, transportation, agriculture, and government. Organizations are increasingly leveraging geospatial data to streamline field operations, optimize resource allocation, and improve asset management. The shift towards digitized workflows has created a surge in demand for mobile GIS solutions that enable real-time data capture, analysis, and sharing from remote locations. Furthermore, the growing emphasis on smart infrastructure and sustainable urban planning has amplified the need for accurate, up-to-date geographic information, positioning mobile GIS software as a critical tool in supporting these initiatives. The convergence of cloud computing, 5G connectivity, and mobile technologies is further enhancing the capabilities and accessibility of GIS platforms, making them indispensable for modern enterprises.

Another significant driver is the increasing adoption of IoT and sensor technologies, which are generating vast volumes of spatial data that require efficient collection, processing, and analysis. Mobile GIS data collection software enables seamless integration with IoT devices, allowing for automated data acquisition and real-time monitoring of assets, environmental conditions, and infrastructure. This capability is particularly valuable in sectors like environmental monitoring, utilities management, and agriculture, where timely and accurate geospatial data is essential for informed decision-making. Additionally, advancements in artificial intelligence and machine learning are empowering GIS software to deliver predictive analytics, anomaly detection, and advanced visualization, further expanding the application scope and value proposition of mobile GIS solutions.

The market is also benefiting from the increasing focus on regulatory compliance and safety standards, particularly in industries such as oil and gas, construction, and transportation. Mobile GIS data collection software facilitates compliance by providing accurate and auditable records of field activities, asset inspections, and environmental assessments. Moreover, the growing need for disaster management, emergency response, and public health surveillance is driving government agencies to invest in robust GIS platforms that support rapid data collection and situational awareness. As a result, vendors are continuously innovating to offer user-friendly, scalable, and secure solutions that cater to the evolving needs of diverse end-users, further fueling market expansion.

The integration of Mobile Mapping System technology into mobile GIS solutions is revolutionizing the way geospatial data is collected and analyzed. By utilizing vehicles equipped with advanced sensors and cameras, Mobile Mapping Systems enable the rapid and accurate capture of geospatial data across large areas. This technology is particularly beneficial for urban planning, infrastructure management, and environmental monitoring, where timely and precise data is crucial. As industries strive to enhance their operational capabilities, the adoption of Mobile Mapping Systems is becoming increasingly prevalent, providing a competitive edge through improved data accuracy and efficiency.

Regionally, North America currently dominates the Mobile GIS Data Collection Software market, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific. The presence of leading technology providers, high adoption rates of digital soluti

The booming field data collection software market is projected to reach $7.75 billion by 2033, growing at a 15% CAGR. This comprehensive analysis explores key market drivers, trends, and regional insights, featuring leading companies and crucial application segments. Discover the future of data collection in construction, environmental monitoring, and more!

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.

Utility GIS Field Data Collection Market Outlook

According to our latest research, the Utility GIS Field Data Collection market size reached USD 1.72 billion in 2024 globally, driven by rapid digital transformation across utility sectors and increasing demand for real-time data insights. The market is expected to expand at a robust CAGR of 13.1% from 2025 to 2033, reaching a forecasted value of USD 5.06 billion by 2033. This impressive growth trajectory is underpinned by the rising need for efficient asset management, regulatory compliance, and the integration of advanced geospatial technologies into utility operations, as per our comprehensive analysis.

The primary growth factor for the Utility GIS Field Data Collection market is the accelerating adoption of smart grid technologies and digital infrastructure upgrades by utility providers worldwide. Utilities are increasingly leveraging Geographic Information Systems (GIS) to streamline field operations, improve asset visibility, and enhance decision-making processes. The proliferation of IoT devices and mobile data collection platforms has further amplified the demand for GIS-based field data solutions, enabling utilities to capture, process, and analyze spatial data with unprecedented accuracy and speed. This shift is particularly pronounced in regions with aging infrastructure, where the need for modernization and operational efficiency is paramount. Additionally, regulatory mandates for accurate asset documentation and environmental compliance are compelling utilities to invest in advanced GIS field data collection tools.

Another significant growth catalyst is the surge in investments toward renewable energy integration and the modernization of water, gas, and electric utility networks. As utilities transition towards sustainable energy sources and smart distribution systems, the complexity of managing diverse assets and networks has increased substantially. GIS field data collection solutions provide utilities with the ability to map, monitor, and maintain these assets in real-time, reducing downtime and operational costs. The growing emphasis on predictive maintenance, driven by AI and machine learning algorithms, is also fueling the adoption of GIS-enabled field data collection for proactive infrastructure management. Furthermore, the ongoing urbanization and expansion of utility networks in emerging economies are creating new opportunities for market players.

The evolution of cloud computing and mobile technologies is reshaping how utilities approach field data collection. Cloud-based GIS platforms offer scalability, flexibility, and cost-effectiveness, making them attractive for both large and small utility providers. The integration of advanced analytics, real-time data synchronization, and remote collaboration features is enhancing the value proposition of GIS field data solutions. Moreover, the increasing focus on customer-centric services, such as outage management and service restoration, is driving utilities to adopt GIS-based field data collection for improved responsiveness and service quality. As digital transformation accelerates across the sector, the market is witnessing a surge in partnerships and collaborations between technology vendors and utility companies, further propelling growth.

Regionally, North America leads the Utility GIS Field Data Collection market owing to substantial investments in smart grid projects, stringent regulatory frameworks, and the presence of major technology providers. Europe follows closely, driven by ambitious sustainability targets and modernization initiatives in the utility sector. The Asia Pacific region is emerging as a high-growth market, supported by rapid urbanization, infrastructure expansion, and increasing adoption of digital technologies by utilities in countries such as China, India, and Japan. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as utilities in these regions embark on digital transformation journeys to enhance operational efficiency and service delivery.

Component Analysis

The component segment of the Utility GIS Field Data Collection market is broadly categorized into software, hardware, and services, each playing a pivotal role in supporting the digital transformation of utility operations. Software solutions form the backbone of GIS field data collection, offering advanced functionalities such a

Utility GIS Field Data Collection Market Outlook

According to our latest research, the Global Utility GIS Field Data Collection market size was valued at $1.4 billion in 2024 and is projected to reach $3.1 billion by 2033, expanding at a robust CAGR of 9.3% during the forecast period of 2025–2033. The significant growth in this market is primarily driven by the increasing adoption of advanced geospatial technologies by utility companies seeking to modernize their infrastructure and enhance operational efficiency. The proliferation of smart grids, the growing need for real-time asset monitoring, and the integration of IoT devices have collectively intensified the demand for precise, field-based GIS data collection solutions. This market is further propelled by regulatory mandates emphasizing infrastructure resilience and digital transformation initiatives across the utilities sector, making GIS field data collection systems indispensable for asset management, network mapping, and operational optimization.

Regional Outlook

North America holds the largest share of the global Utility GIS Field Data Collection market, accounting for nearly 38% of the total market value in 2024. This dominance is underpinned by the region’s mature utility infrastructure, widespread digitalization, and early adoption of GIS technologies. The United States, in particular, has invested heavily in upgrading aging utility networks and deploying smart grid solutions, which has necessitated sophisticated GIS field data collection tools. Additionally, favorable regulatory frameworks and a strong presence of leading GIS software providers have accelerated technology uptake. The emphasis on disaster management, grid reliability, and environmental compliance further amplifies the demand for advanced GIS field data collection systems in North America.

In contrast, Asia Pacific emerges as the fastest-growing region, projected to register an impressive CAGR of 12.1% over the forecast period. The rapid urbanization, expanding utility networks, and significant government investments in infrastructure modernization across China, India, and Southeast Asia are pivotal growth drivers. These economies are leveraging GIS field data collection to support mega infrastructure projects, rural electrification, and efficient resource management. The increasing penetration of cloud-based GIS solutions and mobile data collection apps is enabling utilities in Asia Pacific to overcome legacy system limitations, optimize field operations, and improve service delivery. As a result, the region is witnessing a surge in both public and private sector investments aimed at digitalizing utility asset management.

Meanwhile, emerging economies in Latin America and Middle East & Africa are gradually adopting Utility GIS Field Data Collection technologies, albeit at a slower pace due to budget constraints, skills shortages, and infrastructural challenges. These regions face unique hurdles such as fragmented utility networks, inconsistent regulatory support, and limited access to advanced geospatial tools. However, localized demand is rising as governments and utility operators recognize the value of GIS in reducing losses, improving maintenance cycles, and supporting sustainable resource management. International aid programs, technology transfer initiatives, and growing awareness of digital transformation benefits are expected to accelerate adoption in these regions over the next decade.

Report Scope

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

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.

The booming Field Data Collection Software market is projected for significant growth (CAGR of approximately 10%), driven by increased mobile adoption and demand for efficient data management. Explore market trends, key players (SafetyCulture, ArcGIS, Tableau), regional analysis (North America, Europe, Asia-Pacific), and future forecasts (2025-2033) in this comprehensive market report.

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

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.

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.

GIS Data Management Market Outlook

The global GIS Data Management market size is projected to grow from USD 12.5 billion in 2023 to USD 25.6 billion by 2032, exhibiting a CAGR of 8.4% during the forecast period. This impressive growth is driven by the increasing adoption of geographic information systems (GIS) across various sectors such as urban planning, disaster management, and agriculture. The rising need for effective data management systems to handle the vast amounts of spatial data generated daily also significantly contributes to the market's expansion.

One of the primary growth factors for the GIS Data Management market is the burgeoning demand for spatial data analytics. Businesses and governments are increasingly leveraging GIS data to make informed decisions and strategize operational efficiencies. With the rapid urbanization and industrialization worldwide, there's an unprecedented need to manage and analyze geographic data to plan infrastructure, monitor environmental changes, and optimize resource allocation. Consequently, the integration of GIS with advanced technologies like artificial intelligence and machine learning is becoming more prominent, further fueling market growth.

Another significant factor propelling the market is the advancement in GIS technology itself. The development of sophisticated software and hardware solutions for GIS data management is making it easier for organizations to capture, store, analyze, and visualize geographic data. Innovations such as 3D GIS, real-time data processing, and cloud-based GIS solutions are transforming the landscape of geographic data management. These advancements are not only enhancing the capabilities of GIS systems but also making them more accessible to a broader range of users, from small enterprises to large governmental agencies.

The growing implementation of GIS in disaster management and emergency response activities is also a critical factor driving market growth. GIS systems play a crucial role in disaster preparedness, response, and recovery by providing accurate and timely geographic data. This data helps in assessing risks, coordinating response activities, and planning resource deployment. With the increasing frequency and intensity of natural disasters, the reliance on GIS data management systems is expected to grow, resulting in higher demand for GIS solutions across the globe.

Geospatial Solutions are becoming increasingly integral to the GIS Data Management landscape, offering enhanced capabilities for spatial data analysis and visualization. These solutions provide a comprehensive framework for integrating various data sources, enabling users to gain deeper insights into geographic patterns and trends. As organizations strive to optimize their operations and decision-making processes, the demand for robust geospatial solutions is on the rise. These solutions not only facilitate the efficient management of spatial data but also support advanced analytics and real-time data processing. By leveraging geospatial solutions, businesses and governments can improve their strategic planning, resource allocation, and environmental monitoring efforts, thereby driving the overall growth of the GIS Data Management market.

Regionally, North America holds a significant share of the GIS Data Management market, driven by high technology adoption rates and substantial investments in GIS technologies by government and private sectors. However, Asia Pacific is anticipated to witness the highest growth rate during the forecast period. The rapid urbanization, economic development, and increasing adoption of advanced technologies in countries like China and India are major contributors to this growth. Governments in this region are also focusing on smart city projects and infrastructure development, which further boosts the demand for GIS data management solutions.

Component Analysis

The GIS Data Management market is segmented by component into software, hardware, and services. The software segment is the largest and fastest-growing segment, driven by the continuous advancements in GIS software capabilities. GIS software applications enable users to analyze spatial data, create maps, and manage geographic information efficiently. The integration of GIS software with other enterprise systems and the development of user-friendly interfaces are key factors propelling the growth of this segment. Furthermore, the rise of mobile GIS applications, which allow field data collectio

This collection contains Environmental Response Management Application (ERMA) GIS layers used as part of the Programmatic Damage Assessment and Restoration Plan (PDARP), including outputs from Synthetic Aperture Radar (SAR) imagery, helicopter flights surveys (observations) of marine mammal and turtles, Mississippi Canyon 252 wellhead location, wellhead buffers, and supporting bathymetric contour data, infrared and photographic images from EPA's airborne spectral photometric environmental collection technology (ASPECT) with geospatial, chemical and radiological information, boom-related response observations, nearshore tissue and sediment samples, forensic and Total Polycyclic Aromatic Hydrocarbon (TPAH) results, stranded oil forensic classification data, and other types of chemistry data, Submerged Aquatic Vegetation (SAV) classifications, seabed sampling and transect data, sample locations for workplan cruises, deep-sea area injury toxicity results and total polycyclic aromatic hydrocarbon (TPAH) results, habitat injury zones, footprint impacts on mesophotic reef resources and other types of benthic habitat data, overflight imagery of the flight path for the NOAA King Air flights taken in October of 2010 and contains post-oiling images collection in support of Natural Resource Damage Assessment (NRDA) marsh monitoring, turtle survey overflight observations, loggerhead sea turtle density grids, sea turtle capture observations and transect analysis, sea turtle strandings, as well as probabilities of oiling and other related datasets, trawl locations, Southeast Area Monitoring and Assessment Program (SEAMAP) plankton trawls, workplan cruise samples, and other related data, delineation of the areas impacted with additional fresh water due to the opening of the diversions in 2011 as part of the Deepwater Horizon oil spill response, surface shoreline oiling characteristics as observed by field surveys performed by Shoreline Cleanup Assessment Techniques (SCAT) teams, marine mammal surveys, observations, telemetry and abundance data including Cytochrome P450 (CYP) dolphin analysis, population and abundance datasets, telemetry, wildlife and aerial observations, bathymetry estimates, and other related Marine Mammal field observations and surveys, presence and spatial distribution of synthetic-based mud (SBM) in deep-sea sediments around the Macondo well, surface sediment, residual kriging, and other oiling analytical data, oyster recruitment and abundance sampling results, estimates of subtidal habitat, estimates of oyster resource, seafloor substrate mapping layers, percent cover, nearshore and subtidal quadrat abundance data, and other related datasets, shoreline exposure model for beach and marsh oiling, wave exposure, habitat classifications, wetland monitoring datasets, and related shoreline datasets, compilation of all the individual Texture Classifying Neural Network Algorithm (TCNNA) days from Synthetic Aperture Radar (SAR) satellite polygons, a variety of cumulative oiling datasets including the Texture Classifying Neural Network Algorithm (TCNNA) from Synthetic Aperture Radar (SAR) satellite polygon layers, burn locations, dispersant operation datasets including estimations of where aerial dispersants were applied via aerial flight paths, dispersant airport locations, daily flight tracks, and vessel dispersant tracks, as well as locations of subsurface dispersant data, marine mammal surveys, observations, telemetry and abundance data collected including synoptic surveys, helicopter surveys, Cytochrome P450 (CYP) dolphin analysis, population and abundance datasets, telemetry, wildlife and aerial observations, bathymetry estimates, other related marine mammal field observations and surveys, and sea turtle data, and other data related to the Deepwater Horizon oil spill in the Northern Gulf of Mexico. Some of these data were collected during the response to the Mississippi Canyon 252 Deepwater Horizon oil spill in the Northern Gulf of Mexico.

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/

GapMaps Live is an easy-to-use location intelligence platform available across 25 countries globally that allows you to visualise your own store data, combined with the latest demographic, economic and population movement intel right down to the micro level so you can make faster, smarter and surer decisions when planning your network growth strategy.

With one single login, you can access the latest estimates on resident and worker populations, census metrics (eg. age, income, ethnicity), consuming class, retail spend insights and point-of-interest data across a range of categories including fast food, cafe, fitness, supermarket/grocery and more.

Some of the world's biggest brands including McDonalds, Subway, Burger King, Anytime Fitness and Dominos use GapMaps Live as a vital strategic tool where business success relies on up-to-date, easy to understand, location intel that can power business case validation and drive rapid decision making.

Primary Use Cases for GapMaps Live includes:

1. Retail Site Selection - Identify optimal locations for future expansion and benchmark performance across existing locations.
2. Customer Profiling: get a detailed understanding of the demographic profile of your customers and where to find more of them.
3. Analyse your catchment areas at a granular grid levels using all the key metrics
4. Target Marketing: Develop effective marketing strategies to acquire more customers.
5. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)
6. Customer Profiling
7. Target Marketing
8. Market Share Analysis

Some of features our clients love about GapMaps Live include: - View business locations, competitor locations, demographic, economic and social data around your business or selected location - Understand consumer visitation patterns (“where from” and “where to”), frequency of visits, dwell time of visits, profiles of consumers and much more. - Save searched locations and drop pins - Turn on/off all location listings by category - View and filter data by metadata tags, for example hours of operation, contact details, services provided - Combine public data in GapMaps with views of private data Layers - View data in layers to understand impact of different data Sources - Share maps with teams - Generate demographic reports and comparative analyses on different locations based on drive time, walk time or radius. - Access multiple countries and brands with a single logon - Access multiple brands under a parent login - Capture field data such as photos, notes and documents using GapMaps Connect and integrate with GapMaps Live to get detailed insights on existing and proposed store locations.

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 Vegetation Technical Working Group (VTWG) of the Alaska Geospatial Council developed the Minimum Standards for Field Observation of Vegetation and Related Properties Version 1.1 (August 2022) to help ensure that vegetation data collected as part of independent vegetation survey, mapping, monitoring, and classification projects can support the production of a statewide vegetation map from quantitative data and the continued development of the U.S. National Vegetation Classification.

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.

Explore the booming Mobile Data Collection Software market, projected to reach USD 3.5 billion by 2025 with a 12.5% CAGR. Discover key drivers, trends, and leading companies revolutionizing data collection across healthcare, marketing, and logistics.

A Geographic Information System (GIS) shapefile and summary tables of irrigated agricultural land-use are provided for the 15 counties fully within the Northwest Florida Water Management District (Bay, Calhoun, Escambia, Franklin, Gadsden, Gulf, Holmes, Jackson, Leon, Liberty, Okaloosa, Santa Rosa, Wakulla, Walton, and Washington counties). These files were compiled through a cooperative project between the U.S. Geological Survey and the Florida Department of Agriculture and Consumer Services, Office of Agricultural Water Policy. Information provided in the shapefile includes the location of irrigated lands that were verified during field surveying that started in May 2021 and concluded in August 2021. Field data collected were crop type, irrigation system type, and primary water source used. A map image of the shapefile is also provided. Previously published estimates of irrigation acreage for years since 1982 are included in summary tables.