The Geographic Management Information System (GeoMIS) is a FISMA Moderate minor application built using ArcGIS Server and portal, Microsoft SQL, and a web-facing front-end. The system can be accessed over the internet via https://www.usaidgiswbg.com using a web browser. GeoMIS is based on a commercial off-the-shelf product developed by Esri. Esri is creates geographic information system (GIS) software, web GIS and geodatabase management applications and is based in California. GeoMISIt is maintained by an Israeli company, Systematics (see Attachment 3) which is EsriI's agent in Israel. The mission has an annual maintenance contract with Systematics for GeoMIS. GeoMIS has 100 users from USAID staff (USA Direct Hire and Foreign Service Nationals) and 200 users from USAID contractors and grantees. The system is installed at USAID WBG office in Tel Aviv/Israel inside the computer room in the DMZ. It has no interconnections with any other system.

Geographic Information System (GIS) Market Outlook

The Geographic Information System (GIS) market is witnessing robust growth with its global market size projected to reach USD 25.7 billion by 2032, up from USD 8.7 billion in 2023, at a compound annual growth rate (CAGR) of 12.4% during the forecast period. This growth is primarily driven by the increasing integration of GIS technology across various industries to improve spatial data visualization, enhance decision-making, and optimize operations. The benefits offered by GIS in terms of accuracy, efficiency, and cost-effectiveness are convincing more sectors to adopt these systems, thereby expanding the market size significantly.

A major growth factor contributing to the GIS market expansion is the escalating demand for location-based services. As businesses across different sectors recognize the importance of spatial data analytics in driving strategic decisions, the reliance on GIS applications is becoming increasingly pronounced. The rise in IoT devices, coupled with the enhanced capabilities of AI and machine learning, has further fueled the demand for GIS solutions. These technologies enable the processing and analysis of large volumes of spatial data, thereby providing valuable insights that businesses can leverage for competitive advantage. In addition, government initiatives promoting the adoption of digital infrastructure and smart city projects are playing a crucial role in the growth of the GIS market.

The advancement in satellite imaging and remote sensing technologies is another key driver of the GIS market growth. With enhanced satellite capabilities, the precision and quality of geospatial data have significantly improved, making GIS applications more reliable and effective. The availability of high-resolution satellite imagery has opened new avenues in various sectors including agriculture, urban planning, and disaster management. Moreover, the decreasing costs of satellite data acquisition and the proliferation of drone technology are making GIS more accessible to small and medium enterprises, further expanding the market potential.

The advent of 3D Geospatial Technologies is revolutionizing the way industries utilize GIS data. By providing a three-dimensional perspective, these technologies enhance spatial analysis and visualization, offering more detailed and accurate representations of geographical areas. This advancement is particularly beneficial in urban planning, where 3D models can simulate cityscapes and infrastructure, allowing planners to visualize potential developments and assess their impact on the environment. Moreover, 3D geospatial data is proving invaluable in sectors such as construction and real estate, where it aids in site analysis and project planning. As these technologies continue to evolve, they are expected to play a pivotal role in the future of GIS, expanding its applications and driving further market growth.

Furthermore, the increasing application of GIS in environmental monitoring and management is bolstering market growth. With growing concerns over climate change and environmental degradation, GIS is being extensively used for resource management, biodiversity conservation, and natural disaster risk management. This trend is expected to continue as more organizations and governments prioritize sustainability, thereby driving the demand for advanced GIS solutions. The integration of GIS with other technologies such as big data analytics, and cloud computing is also expected to enhance its capabilities, making it an indispensable tool for environmental management.

Regionally, North America is currently leading the GIS market, driven by the widespread adoption of advanced technologies and the presence of major GIS vendors. The regionÂ’s focus on infrastructure development and smart city projects is further propelling the market growth. Europe is also witnessing significant growth owing to the increasing adoption of GIS in various industries such as agriculture and transportation. The Asia Pacific region is anticipated to exhibit the highest CAGR during the forecast period, attributed to rapid urbanization, government initiatives for digital transformation, and increasing investments in infrastructure development. In contrast, the markets in Latin America and the Middle East & Africa are growing steadily as these regions continue to explore and adopt GIS technologies.

<a href="https://dataintelo.com/report/geospatial-data-fusion-market" target="_blank&quo

Geographic Information System Market Outlook

As per our latest research, the global Geographic Information System (GIS) market size reached USD 12.3 billion in 2024. The industry is experiencing robust expansion, driven by a surging demand for spatial data analytics across diverse sectors. The market is projected to grow at a CAGR of 11.2% from 2025 to 2033, reaching an estimated USD 31.9 billion by 2033. This accelerated growth is primarily attributed to the integration of advanced technologies such as artificial intelligence, IoT, and cloud computing with GIS solutions, as well as the increasing adoption of location-based services and smart city initiatives worldwide.

One of the primary growth factors fueling the GIS market is the rapid adoption of geospatial analytics in urban planning and infrastructure development. Governments and private enterprises are leveraging GIS to optimize land use, manage resources efficiently, and enhance public services. Urban planners utilize GIS to analyze demographic trends, plan transportation networks, and ensure sustainable development. The integration of GIS with Building Information Modeling (BIM) and real-time data feeds has further amplified its utility in smart city projects, driving demand for sophisticated GIS platforms. The proliferation of IoT devices and sensors has also enabled the collection of high-resolution geospatial data, which is instrumental in developing predictive models for urban growth and disaster management.

Another significant driver of the GIS market is the increasing need for disaster management and risk mitigation. GIS technology plays a pivotal role in monitoring natural disasters such as floods, earthquakes, and wildfires. By providing real-time spatial data, GIS enables authorities to make informed decisions, coordinate response efforts, and allocate resources effectively. The growing frequency and intensity of natural disasters, coupled with heightened awareness about climate change, have compelled governments and humanitarian organizations to invest heavily in advanced GIS solutions. These investments are not only aimed at disaster response but also at long-term resilience planning, thereby expanding the scope and scale of GIS applications.

The expanding application of GIS in the agriculture and utilities sectors is another crucial growth factor. Precision agriculture relies on GIS to analyze soil conditions, monitor crop health, and optimize irrigation practices, ultimately boosting productivity and sustainability. In the utilities sector, GIS is indispensable for asset management, network optimization, and outage response. The integration of GIS with remote sensing technologies and drones has revolutionized data collection and analysis, enabling more accurate and timely decision-making. Moreover, the emergence of cloud-based GIS platforms has democratized access to geospatial data and analytics, empowering small and medium enterprises to harness the power of GIS for operational efficiency and strategic planning.

From a regional perspective, North America continues to dominate the GIS market, supported by substantial investments in smart infrastructure, advanced research capabilities, and a strong presence of leading technology providers. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid urbanization, government initiatives for digital transformation, and increasing adoption of GIS in agriculture and disaster management. Europe is also witnessing significant growth, particularly in transportation, environmental monitoring, and public safety applications. The Middle East & Africa and Latin America are gradually catching up, with growing investments in infrastructure development and resource management. This regional diversification is expected to drive innovation and competition in the global GIS market over the forecast period.

Component Analysis

The Geographic Information System market is segmented by component into hardware, software, and services, each playing a unique role

The Department of Fish and Game - Division of Wildlife Conservation's game management units and subunits are the most requested of the Division's GIS data. Hunting and trapping regulations and other wildlife management issues often refer geographically to the effected Game Management Unit (GMU). This file gives the user access to the currently available digital representation of the GMU/UCUs. The purpose of the GMU and associated Subunits and Uniform coding units is to give a uniform, geographic based coding system for all State of Alaska wildlife population and habitat management and regulations. This data can be used for mapping or analysis purposes assuming it is used with comparable data.Uniform Coding UnitsPrior to 1982, Alaska Department of Fish and Game - Division of Wildlife Conservation (ADFG-DWC) had a variety of coding schemes (18) relating harvest and management information to geographical areas. This made it difficult when comparing statewide wildlife information gathered across the state. In 1982, a new standardized statewide, geographically-based, hierarchy system of coding was created called the Uniform Coding Unit or UCU system. Game management units (GMUs), Subunits, and uniform coding units (UCUs) are the underlying geographic foundation of the wildlife and habitat management and regulations for ADFG-DWC. The GMU/UCU system consists of five Regions which are divided into twenty-six (26) Game Management Units (GMUs). Many of the GMUs are divided into Subunits (e.g. GMU 15 has three (3) Subunits, 15A, 15B, and 15C). GMUs that are not divided into subunits have a "Z" designation for the subunit. GMUs and Subunits are further divided into Major Drainages, Minor Drainages and Specific Areas. The smallest of these areas (down to the "specific area") is referred to as a Uniform Coding Unit (UCU) and has a unique 10 character code associated with it. (NOTE: UCU layer is for internal and official use only, not for public use or distribution). The UCU code is used for geographically classifying harvest and management information. Data that cannot be tied to a specific code can be generalized to the next higher level of the hierarchy. For example:a location description that is within multiple "specific areas" within a "minor drainage" can be coded to the minor code with a "00" for the specific area. Unknown "minor drainages" can be coded to the "major drainage" level, etc. If the subunit is unknown or the area covers multiple subunits within a unit, the subunit can be specified as a "Z" code (e.g. an area within subunits 15A and 15B could be recorded as 15Z). If a geographic location covers multiple units or the unit is unknown, the most general code (statewide code) is recorded as 27Z-Z00. The original hardcopy master maps were drawn to portray the UCUs fairly accurately geographically, but were not necessarily precisely drawn (i.e. left vs. right bank of a river, or exact ridge line). Each UCU was represented by drawing boundaries on USGS 1:250,000 scale quadrangle maps with a thick magic marker. A list (database) of place-names and their corresponding UCU codes was created and is still used today to assign permit, harvest, and sealing information to one of these geographic areas. In 1988, the UCU boundaries were digitized (traced) from the original maps into a computerized Geographic Information System (ArcInfo). Minor changes were made in 1989. Effective July 1, 2006 - GMU 24 is now divided up into four subunit 24A, 24B, 24C, 24D. - GMU 21A and 21B - - boundary has been modified. Phase I2006-2008 - initial clean-up of boundaries for GMU 6, 9, 10, 12, 16, 19, 20, 25. These modifications have NOT been verified against the UCU master list or by area biologists. -ras Jan 2009 - Priority has shifted to getting the bulk of the updates into the master. Verification and modifications based on the UCU list and the AB corrections will come at a later date. This shift is to attempt to get the master into a permanent SDE GDB, set it up with the GDB topology, make additional clean-up/edits using the GDB tools, set up versioning, make it easier to replicate to area offices, and to take advantage of the tools/features available thru ArcGIS Server with versioned GDBs. June 2009 - initial clean-up of boundaries for Southeast (GMU 1-5), GMU 17, and GMU 18. These have NOT been verified against the UCU master list or by area biologists. -ras July 1 2009 - initial clean-up of boundaries for GMU 7 and 8. Also some adjustments for 25D based on the NHD 2008 version and ArcHydro Tools "raindrop" feature. These have NOT been verified against the UCU master list or by area biologists. -ras Sept 17, 2009 - initial clean-up of boundaries for GMU 13. These modifications have NOT been verified against the UCU master list or by area biologists. -ras Oct 21, 2009 - initial clean-up of boundaries for GMU 14 These modification have NOT been verified against the UCU master list or by area biologists. -rasNov 19, 2009 - initial clean-up of boundaries for GMU 15. These modifications have NOT been verified against the UCU master list or by area biologists. -ras Dec 7, 2009 - initial clean-up of boundaries for GMU 22. These modification have NOT been verified against the UCU master list or by area biologists. -ras March 3, 2010 - initial clean-up of boundaries for GMU 23. These modification have NOT been verified against the UCU master list or by area biologists. -rasApril 10, 2010 - initial clean-up of boundaries for GMU 26. These modification have NOT been verified against the UCU master list or by area biologists. -ras May 2010 - This completes Phase I of refining the UCUs - bulk heads-up re-digitizing of all arcs. Phase II - Converting and establishing procedures for maintaining the master in an Enterprise GDB is underway. Effective July 1, 2010, Region II was split into Region 2 (GMU's 6, 7, 8, 14C, 15) and Region 4 (GMU's 9, 10, 11, 13, 14AB, 16, 17. This version was updated to reflect the change. An archive of the previous version (with Regions I, II, III, and V) is available on request as GMUMaster_063010. -ras2012-present - minor updates continue as needed and time allows, and as newer base maps are used.2014 minor updates continue as needed, including updates to domain listings (not affecting GIS geometry)Effective July 1, 2014- revision to GMU 18/19/21 boundary to clarify/correct previous insufficient boundary description. Passed during Spring 2014 Board of Game.2015 minor changes as needed

Geographic Information System (GIS) Software Market Outlook

According to our latest research, the global Geographic Information System (GIS) Software market size reached USD 11.6 billion in 2024, reflecting a robust demand for spatial data analytics and location-based services across various industries. The market is experiencing a significant growth trajectory, driven by a CAGR of 12.4% from 2025 to 2033. By the end of 2033, the GIS Software market is forecasted to attain a value of USD 33.5 billion. This remarkable expansion is primarily attributed to the integration of advanced technologies such as artificial intelligence, IoT, and cloud computing, which are enhancing the capabilities and accessibility of GIS platforms.

One of the major growth factors propelling the GIS Software market is the increasing adoption of location-based services across urban planning, transportation, and utilities management. Governments and private organizations are leveraging GIS solutions to optimize infrastructure development, streamline resource allocation, and improve emergency response times. The proliferation of smart city initiatives worldwide has further fueled the demand for GIS tools, as urban planners and municipal authorities require accurate spatial data for effective decision-making. Additionally, the evolution of 3D GIS and real-time mapping technologies is enabling more sophisticated modeling and simulation, expanding the scope of GIS applications beyond traditional mapping to include predictive analytics and scenario planning.

Another significant driver for the GIS Software market is the rapid digitization of industries such as agriculture, mining, and oil & gas. Precision agriculture, for example, relies heavily on GIS platforms to monitor crop health, manage irrigation, and enhance yield forecasting. Similarly, the mining sector uses GIS for exploration, environmental impact assessment, and asset management. The integration of remote sensing data with GIS software is providing stakeholders with actionable insights, leading to higher efficiency and reduced operational risks. Furthermore, the growing emphasis on environmental sustainability and regulatory compliance is prompting organizations to invest in advanced GIS solutions for monitoring land use, tracking deforestation, and managing natural resources.

The expanding use of cloud-based GIS solutions is also a key factor driving market growth. Cloud deployment offers scalability, cost-effectiveness, and remote accessibility, making GIS tools more accessible to small and medium enterprises as well as large organizations. The cloud model supports real-time data sharing and collaboration, which is particularly valuable for disaster management and emergency response teams. As organizations increasingly prioritize digital transformation, the demand for cloud-native GIS platforms is expected to rise, supported by advancements in data security, interoperability, and integration with other enterprise systems.

Regionally, North America remains the largest market for GIS Software, accounting for a significant share of global revenues. This leadership is underpinned by substantial investments in smart infrastructure, advanced transportation systems, and environmental monitoring programs. The Asia Pacific region, however, is witnessing the fastest growth, driven by rapid urbanization, government-led digital initiatives, and the expansion of the utility and agriculture sectors. Europe continues to demonstrate steady adoption, particularly in environmental management and urban planning, while Latin America and the Middle East & Africa are emerging as promising markets due to increasing investments in infrastructure and resource management.

Component Analysis

The GIS Software market is segmented by component into Software and Services, each playing a pivotal role in the overall value chain. The software segment includes comprehensive GIS platforms, spatial analytics tools, and specialized applications

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

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

Abstract: The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee. Use the metadata link, http://nhdgeo.usgs.gov/metadata/nhd_high.htm, for additional information. Purpose: The NHD is a national framework for assigning reach addresses to water-related entities, such as industrial discharges, drinking water supplies, fish habitat areas, wild and scenic rivers. Reach addresses establish the locations of these entities relative to one another within the NHD surface water drainage network, much like addresses on streets. Once linked to the NHD by their reach addresses, the upstream/downstream relationships of these water-related entities--and any associated information about them--can be analyzed using software tools ranging from spreadsheets to geographic information systems (GIS). GIS can also be used to combine NHD-based network analysis with other data layers, such as soils, land use and population, to help understand and display their respective effects upon one another. Furthermore, because the NHD provides a nationally consistent framework for addressing and analysis, water-related information linked to reach addresses by one organization (national, state, local) can be shared with other organizations and easily integrated into many different types of applications to the benefit of all.

Geographic Information System (GIS) Market Outlook

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

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

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

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

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

Component Analysis

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

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

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

This data set contains small-scale base GIS data layers compiled by the National Park Service Servicewide Inventory and Monitoring Program and Water Resources Division for use in a Baseline Water Quality Data Inventory and Analysis Report that was prepared for the park. The report presents the results of surface water quality data retrievals for the park from six of the United States Environmental Protection Agency's (EPA) national databases: (1) Storage and Retrieval (STORET) water quality database management system; (2) River Reach File (RF3) Hydrography; (3) Industrial Facilities Discharges; (4) Drinking Water Supplies; (5) Water Gages; and (6) Water Impoundments. The small-scale GIS data layers were used to prepare the maps included in the report that depict the locations of water quality monitoring stations, industrial discharges, drinking intakes, water gages, and water impoundments. The data layers included in the maps (and this dataset) vary depending on availability, but generally include roads, hydrography, political boundaries, USGS 7.5' minute quadrangle outlines, hydrologic units, trails, and others as appropriate. The scales of each layer vary depending on data source but are generally 1:100,000.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Our final map product is a geographic information system (GIS) database of vegetation structure and composition across the Crater Lake National Park terrestrial landscape, including wetlands. The database includes photos we took at all relevé, validation, and accuracy assessment plots, as well as the plots that were done in the previous wetlands inventory. We conducted an accuracy assessment of the map by evaluating 698 stratified random accuracy assessment plots throughout the project area. We intersected these field data with the vegetation map, resulting in an overall thematic accuracy of 86.2 %. The accuracy of the Cliff, Scree & Rock Vegetation map unit was difficult to assess, as only 9% of this vegetation type was available for sampling due to lack of access. In addition, fires that occurred during the 2017 accuracy assessment field season affected our sample design and may have had a small influence on the accuracy. Our geodatabase contains the locations where particular associations are found at 600 relevé plots, 698 accuracy assessment plots, and 803 validation plots.

ArcGIS Map Packages and GIS Data for Gillreath-Brown, Nagaoka, and Wolverton (2019)

**When using the GIS data included in these map packages, please cite all of the following:

Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, 2019. PLoSONE 14(8):e0220457. http://doi.org/10.1371/journal.pone.0220457

Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. ArcGIS Map Packages for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al., 2019. Version 1. Zenodo. https://doi.org/10.5281/zenodo.2572018

OVERVIEW OF CONTENTS

This repository contains map packages for Gillreath-Brown, Nagaoka, and Wolverton (2019), as well as the raw digital elevation model (DEM) and soils data, of which the analyses was based on. The map packages contain all GIS data associated with the analyses described and presented in the publication. The map packages were created in ArcGIS 10.2.2; however, the packages will work in recent versions of ArcGIS. (Note: I was able to open the packages in ArcGIS 10.6.1, when tested on February 17, 2019). The primary files contained in this repository are:

• Raw DEM and Soils data
  • Digital Elevation Model Data (Map services and data available from U.S. Geological Survey, National Geospatial Program, and can be downloaded from the National Elevation Dataset)
    • DEM_Individual_Tiles: Individual DEM tiles prior to being merged (1/3 arc second) from USGS National Elevation Dataset.
    • DEMs_Merged: DEMs were combined into one layer. Individual watersheds (i.e., Goodman, Coffey, and Crow Canyon) were clipped from this combined DEM.
  • Soils Data (Map services and data available from Natural Resources Conservation Service Web Soil Survey, U.S. Department of Agriculture)
    • Animas-Dolores_Area_Soils: Small portion of the soil mapunits cover the northeastern corner of the Coffey Watershed (CW).
    • Cortez_Area_Soils: Soils for Montezuma County, encompasses all of Goodman (GW) and Crow Canyon (CCW) watersheds, and a large portion of the Coffey watershed (CW).
• ArcGIS Map Packages
  • Goodman_Watershed_Full_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the full Goodman Watershed (GW).
  • Goodman_Watershed_Mesa-Only_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the mesa-only Goodman Watershed.
  • Crow_Canyon_Watershed_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the Crow Canyon Watershed (CCW).
  • Coffey_Watershed_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the Coffey Watershed (CW).

For additional information on contents of the map packages, please see see "Map Packages Descriptions" or open a map package in ArcGIS and go to "properties" or "map document properties."

LICENSES

Code: MIT year: 2019
Copyright holders: Andrew Gillreath-Brown, Lisa Nagaoka, and Steve Wolverton

CONTACT

Andrew Gillreath-Brown, PhD Candidate, RPA
Department of Anthropology, Washington State University
andrew.brown1234@gmail.com – Email
andrewgillreathbrown.wordpress.com – Web

Download high-quality, up-to-date Minor Outlying Islands shapefile boundaries (SHP, projection system SRID 4326). Our Minor Outlying Islands Shapefile Database offers comprehensive boundary data for spatial analysis, including administrative areas and geographic boundaries. This dataset contains accurate and up-to-date information on all administrative divisions, zip codes, cities, and geographic boundaries, making it an invaluable resource for various applications such as geographic analysis, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including Shapefile, GeoJSON, KML, ASC, DAT, CSV, and GML, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

This dataset was created for the Ceres2030 Team 6, who conducted a semi-automatic systematic review of over 18,000 articles. The goal was to identify solutions for small farms to adapt to climate change - we focused on water scarce environments. This analysis is under review at the academic journal Nature Sustainability. A link to the article will be provided once published. The manuscript's current citation is:Ricciardi, V., Wane, A., Singh Sidhu, B., Goode, C., Solomon, D., McCullough, E., Diekmann, F., Porciello, J., Jain, M., Randall, N., Mehrabi, Z.Evidence synthesis: Funding research for small-scale farmers in water scarce regions to achieve SDG 2.3. In Review. Nature Sustainability.We use manually coded abstracts to build a natural language processing (NLP) model to include or exclude abstracts to our study. A deep learning model was developed using Bidirectional Encoder Representations from Transformers (BERT) and a cross entropy function to classify abstracts. This model performed better than other classifiers (e.g., optimized support vector machine (SVM) and naive bayes) across accuracy, precision, and recall metrics. This dataset is the predicted results.This dataset is an input file for use in:https://github.com/vinnyricciardi/Ricciardi_etal_2020_ceres/blob/master/full_text_analysis.RThe data filename referred to in the code is: Validation Survey_February 17, 2020_12.13.csv

Uniform Coding UnitsPrior to 1982, Alaska Department of Fish and Game - Division of Wildlife Conservation (ADFG-DWC) had a variety of coding schemes (18) relating harvest and management information to geographical areas. This made it difficult when comparing statewide wildlife information gathered across the state. In 1982, a new standardized statewide, geographically-based, hierarchy system of coding was created called the Uniform Coding Unit or UCU system. Game management units (GMUs), Subunits, and uniform coding units (UCUs) are the underlying geographic foundation of the wildlife and habitat management and regulations for ADFG-DWC. The GMU/UCU system consists of five Regions which are divided into twenty-six (26) Game Management Units (GMUs). Many of the GMUs are divided into Subunits (e.g. GMU 15 has three (3) Subunits, 15A, 15B, and 15C). GMUs that are not divided into subunits have a "Z" designation for the subunit. GMUs and Subunits are further divided into Major Drainages, Minor Drainages and Specific Areas. The smallest of these areas (down to the "specific area") is referred to as a Uniform Coding Unit (UCU) and has a unique 10 character code associated with it. (NOTE: UCU layer is for internal and official use only, not for public use or distribution). The UCU code is used for geographically classifying harvest and management information. Data that cannot be tied to a specific code can be generalized to the next higher level of the hierarchy. For example:a location description that is within multiple "specific areas" within a "minor drainage" can be coded to the minor code with a "00" for the specific area. Unknown "minor drainages" can be coded to the "major drainage" level, etc. If the subunit is unknown or the area covers multiple subunits within a unit, the subunit can be specified as a "Z" code (e.g. an area within subunits 15A and 15B could be recorded as 15Z). If a geographic location covers multiple units or the unit is unknown, the most general code (statewide code) is recorded as 27Z-Z00. The original hardcopy master maps were drawn to portray the UCUs fairly accurately geographically, but were not necessarily precisely drawn (i.e. left vs. right bank of a river, or exact ridge line). Each UCU was represented by drawing boundaries on USGS 1:250,000 scale quadrangle maps with a thick magic marker. A list (database) of place-names and their corresponding UCU codes was created and is still used today to assign permit, harvest, and sealing information to one of these geographic areas. In 1988, the UCU boundaries were digitized (traced) from the original maps into a computerized Geographic Information System (ArcInfo). Minor changes were made in 1989. Phase I2006-2008 - initial clean-up of boundaries for GMU 6, 9, 10, 12, 16, 19, 20, 25. These modifications have NOT been verified against the UCU master list or by area biologists. -ras Jan 2009 - Priority has shifted to getting the bulk of the updates into the master. Verification and modifications based on the UCU list and the AB corrections will come at a later date. This shift is to attempt to get the master into a permanent SDE GDB, set it up with the GDB topology, make additional clean-up/edits using the GDB tools, set up versioning, make it easier to replicate to area offices, and to take advantage of the tools/features available thru ArcGIS Server with versioned GDBs. June 2009 - initial clean-up of boundaries for Southeast (GMU 1-5), GMU 17, and GMU 18. These have NOT been verified against the UCU master list or by area biologists. -ras July 1 2009 - initial clean-up of boundaries for GMU 7 and 8. Also some adjustments for 25D based on the NHD 2008 version and ArcHydro Tools "raindrop" feature. These have NOT been verified against the UCU master list or by area biologists. -ras Sept 17, 2009 - initial clean-up of boundaries for GMU 13. These modifications have NOT been verified against the UCU master list or by area biologists. -ras Oct 21, 2009 - initial clean-up of boundaries for GMU 14 These modification have NOT been verified against the UCU master list or by area biologists. -rasNov 19, 2009 - initial clean-up of boundaries for GMU 15. These modifications have NOT been verified against the UCU master list or by area biologists. -ras Dec 7, 2009 - initial clean-up of boundaries for GMU 22. These modification have NOT been verified against the UCU master list or by area biologists. -ras March 3, 2010 - initial clean-up of boundaries for GMU 23. These modification have NOT been verified against the UCU master list or by area biologists. -rasApril 10, 2010 - initial clean-up of boundaries for GMU 26. These modification have NOT been verified against the UCU master list or by area biologists. -ras May 2010 - This completes Phase I of refining the UCUs - bulk heads-up re-digitizing of all arcs. Phase II - Converting and establishing procedures for maintaining the master in an Enterprise GDB is underway. Complete.Phase III- Continued maintenance as needed or as modified via the Board of Game process:Game Management changes:Effective July 1, 2006 - GMU 24 is now divided up into four subunit 24A, 24B, 24C, 24D. - GMU 21A and 21B - - boundary has been modifiedEffective July 1, 2010, Region II was split into Region 2 (GMU's 6, 7, 8, 14C, 15) and Region 4 (GMU's 9, 10, 11, 13, 14AB, 16, 17. This version was updated to reflect the change. An archive of the previous version (with Regions I, II, III, and V) is available on request as GMUMaster_063010. 2012 - minor updates continue as needed and time allows, and as newer base maps are used2014 - minor updates continue as needed, including updates to domain listings (not affecting GIS geometry)Effective July 1, 2014- revision to GMU 18/19/21 boundary to clarify/correct previous insufficient boundary description. Passed during Spring 2014 Board of Game.Effective July 1, 2016- revision to GMU 15 (Region II) and GMU 16 (Region IV). Kalgin Island (and surrounding waters) has been recoded from 16B-O00-1901 to 15B-O00-1901 This change will modify the area caluculations for Regions II and IV, Units 15 and 16, Subunits 16B and 15B, etc. GIS domains have been updated.Historical harvest information may not reflect these (or any) GMU modifications.

The Canadian major and minor crop field trial regions were developed following extensive stakeholder consultation and have been harmonized between the Pest Management Regulatory Agency (PMRA) and the Environmental Protection Agency of the USA. The Canadian major and minor crop field trial regions were delineated, using the geographic information system (GIS) data processing hardware and software facilities in Spatial Analysis and Geomatics Applications (SAGA), Agriculture Division, Statistics Canada. In general, the delineation process involved integration, evaluation and reference to numerous geographic data sources in a GIS to determine the best sources for the delineation. There are seven major and four minor field trial regions. Each of these regions recognizes physical characteristics, such as soils, and crops and climate, that make the region unique within the Canadian agricultural landscape. The subzones address differences within a region, generally reflected in the types of crops grown in that region. The Canadian regions, as much as possible, correspond to the U.S. regions

Today, deep neural networks are widely used in many computer vision problems, also for geographic information systems (GIS) data. This type of data is commonly used for urban analyzes and spatial planning. We used orthophotographic images of two residential districts from Kielce, Poland for research including urban sprawl automatic analysis with Transformer-based neural network application.Orthophotomaps were obtained from Kielce GIS portal. Then, the map was manually masked into building and building surroundings classes. Finally, the ortophotomap and corresponding classification mask were simultaneously divided into small tiles. This approach is common in image data preprocessing for machine learning algorithms learning phase. Data contains two original orthophotomaps from Wietrznia and Pod Telegrafem residential districts with corresponding masks and also their tiled version, ready to provide as a training data for machine learning models.Transformed-based neural network has undergone a training process on the Wietrznia dataset, targeted for semantic segmentation of the tiles into buildings and surroundings classes. After that, inference of the models was used to test model's generalization ability on the Pod Telegrafem dataset. The efficiency of the model was satisfying, so it can be used in automatic semantic building segmentation. Then, the process of dividing the images can be reversed and complete classification mask retrieved. This mask can be used for area of the buildings calculations and urban sprawl monitoring, if the research would be repeated for GIS data from wider time horizon.Since the dataset was collected from Kielce GIS portal, as the part of the Polish Main Office of Geodesy and Cartography data resource, it may be used only for non-profit and non-commertial purposes, in private or scientific applications, under the law "Ustawa z dnia 4 lutego 1994 r. o prawie autorskim i prawach pokrewnych (Dz.U. z 2006 r. nr 90 poz 631 z późn. zm.)". There are no other legal or ethical considerations in reuse potential.Data information is presented below.wietrznia_2019.jpg - orthophotomap of Wietrznia districtmodel's - used for training, as an explanatory imagewietrznia_2019.png - classification mask of Wietrznia district - used for model's training, as a target imagewietrznia_2019_validation.jpg - one image from Wietrznia district - used for model's validation during training phasepod_telegrafem_2019.jpg - orthophotomap of Pod Telegrafem district - used for model's evaluation after training phasewietrznia_2019 - folder with wietrznia_2019.jpg (image) and wietrznia_2019.png (annotation) images, divided into 810 tiles (512 x 512 pixels each), tiles with no information were manually removed, so the training data would contain only informative tilestiles presented - used for the model during training (images and annotations for fitting the model to the data)wietrznia_2019_vaidation - folder with wietrznia_2019_validation.jpg image divided into 16 tiles (256 x 256 pixels each) - tiles were presented to the model during training (images for validation model's efficiency); it was not the part of the training datapod_telegrafem_2019 - folder with pod_telegrafem.jpg image divided into 196 tiles (256 x 265 pixels each) - tiles were presented to the model during inference (images for evaluation model's robustness)Dataset was created as described below.Firstly, the orthophotomaps were collected from Kielce Geoportal (https://gis.kielce.eu). Kielce Geoportal offers a .pst recent map from April 2019. It is an orthophotomap with a resolution of 5 x 5 pixels, constructed from a plane flight at 700 meters over ground height, taken with a camera for vertical photos. Downloading was done by WMS in open-source QGIS software (https://www.qgis.org), as a 1:500 scale map, then converted to a 1200 dpi PNG image.Secondly, the map from Wietrznia residential district was manually labelled, also in QGIS, in the same scope, as the orthophotomap. Annotation based on land cover map information was also obtained from Kielce Geoportal. There are two classes - residential building and surrounding. Second map, from Pod Telegrafem district was not annotated, since it was used in the testing phase and imitates situation, where there is no annotation for the new data presented to the model.Next, the images was converted to an RGB JPG images, and the annotation map was converted to 8-bit GRAY PNG image.Finally, Wietrznia data files were tiled to 512 x 512 pixels tiles, in Python PIL library. Tiles with no information or a relatively small amount of information (only white background or mostly white background) were manually removed. So, from the 29113 x 15938 pixels orthophotomap, only 810 tiles with corresponding annotations were left, ready to train the machine learning model for the semantic segmentation task. Pod Telegrafem orthophotomap was tiled with no manual removing, so from the 7168 x 7168 pixels ortophotomap were created 197 tiles with 256 x 256 pixels resolution. There was also image of one residential building, used for model's validation during training phase, it was not the part of the training data, but was a part of Wietrznia residential area. It was 2048 x 2048 pixel ortophotomap, tiled to 16 tiles 256 x 265 pixels each.

Abstract: The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee. Use the metadata link, http://nhdgeo.usgs.gov/metadata/nhd_high.htm, for additional information. Purpose: The NHD is a national framework for assigning reach addresses to water-related entities, such as industrial discharges, drinking water supplies, fish habitat areas, wild and scenic rivers. Reach addresses establish the locations of these entities relative to one another within the NHD surface water drainage network, much like addresses on streets. Once linked to the NHD by their reach addresses, the upstream/downstream relationships of these water-related entities--and any associated information about them--can be analyzed using software tools ranging from spreadsheets to geographic information systems (GIS). GIS can also be used to combine NHD-based network analysis with other data layers, such as soils, land use and population, to help understand and display their respective effects upon one another. Furthermore, because the NHD provides a nationally consistent framework for addressing and analysis, water-related information linked to reach addresses by one organization (national, state, local) can be shared with other organizations and easily integrated into many different types of applications to the benefit of all.

Description Overseas economic and trade cooperation zones developed by Chinese enterprises and endorsed by the Ministry of Commerce of the People’s Republic of China under the Belt and Road Initiative. An interactive view of this dataset: Link Source All data were collected from HKTDC. For metadata, such as data description and available methods for geospatial data processing, please read the readme.pdf. Terms of use This dataset features in a collection of geospatial data "Geo-mapping databases for the Belt and Road Initiative". To cite this work, available citation styles can be found here: https://doi.org/10.6084/m9.figshare.c.6076193

Stormwater Action Monitoring (SAM) is a collaborative monitoring program between western Washington municipal stormwater permittees, state and federal agencies. SAM’s role is to use the results of regional monitoring and focused studies to inform policy decisions and identify effective strategies to improve stormwater management in the Puget Sound region. The SAM program includes status and trends monitoring of water quality, stream biota (macroinvertebrates, algae), and stream habitat to measure whether conditions are getting better or worse and identify patterns in healthy and impaired Puget Lowland streams. To meet this objective, a framework of fundamental geospatial data was required to develop physical and anthropogenic characteristics of the study region, sampled sites and corresponding watersheds, and riparian zones. The riparian-zone boundaries were created from stream centerlines digitized from imagery (hereinafter the "digitized riparian reach") that were buffered by 50 meters on each side of the stream centerline. The length of the digitized riparian reach was calculated as the distance in kilometers equal to the base-10 logarithm of the geospatially-derived watershed area, in kilometers squared (Johnson and Zelt, 2005). This dataset represents the riparian zone boundaries from 105 sites sampled for the SAM small stream study, and is one of the four fundamental geospatial data layers that were developed for this study. In addition, riparian zone boundaries for 16 reference sites in the Puget lowlands sampled by the Washington State Department of Ecology’s Ambient Biological Monitoring program from 2010 to 2015 were also digitized for this analysis to provide a regional context for the SAM study. In total, riparian zone boundaries for 121 total sites are provided in this data release.