Providing user-friendly interfaces in your applications is crucial for increasing task completion and user satisfaction. This can be particularly important for geospatial applications, as these may include a high degree of complexity or may be used by the public or non-GIS professionals. This workshop will introduce you to ArcGIS Experience Builder, a new development platform from Esri that allows you to build custom Web apps for desktop and mobile devices using a drag-and-drop interface. You will learn how to develop an app using Experience Builder’s layout and widget options, as well as some best practices for integrating Experience Builder into a larger user-interface design process and optimizing your app’s interface for greater usability.

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

GIS Controller Market Outlook

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

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

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

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

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

Component Analysis

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

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

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

This collection grew out of a prototype case tracking and crime mapping application that was developed for the United States Attorney's Office (USAO), Southern District of New York (SDNY). The purpose of creating the application was to move from the traditionally episodic way of handling cases to a comprehensive and strategic method of collecting case information and linking it to specific geographic locations, and collecting information either not handled at all or not handled with sufficient enough detail by SDNY's existing case management system. The result was an end-user application designed to be run largely by SDNY's nontechnical staff. It consisted of two components, a database to capture case tracking information and a mapping component to link case and geographic data. The case tracking data were contained in a Microsoft Access database and the client application contained all of the forms, queries, reports, macros, table links, and code necessary to enter, navigate through, and query the data. The mapping application was developed using Environmental Systems Research Institute's (ESRI) ArcView 3.0a GIS. This collection shows how the user-interface of the database and the mapping component were customized to allow the staff to perform spatial queries without having to be geographic information systems (GIS) experts. Part 1 of this collection contains the Visual Basic script used to customize the user-interface of the Microsoft Access database. Part 2 contains the Avenue script used to customize ArcView to link the data maintained in the server databases, to automate the office's most common queries, and to run simple analyses.

(See USGS Digital Data Series DDS-69-E) A geographic information system focusing on the Cretaceous Travis Peak and Hosston Formations was developed for the U.S. Geological Survey's (USGS) 2002 assessment of undiscovered, technically recoverable oil and natural gas resources of the Gulf Coast Region. The USGS Energy Resources Science Center has developed map and metadata services to deliver the 2002 assessment results GIS data and services online. The Gulf Coast assessment is based on geologic elements of a total petroleum system (TPS) as described in Dyman and Condon (2005). The estimates of undiscovered oil and gas resources are within assessment units (AUs). The hydrocarbon assessment units include the assessment results as attributes within the AU polygon feature class (in geodatabase and shapefile format). Quarter-mile cells of the land surface that include single or multiple wells were created by the USGS to illustrate the degree of exploration and the type and distribution of production for each assessment unit. Other data that are available in the map documents and services include the TPS and USGS province boundaries. To easily distribute the Gulf Coast maps and GIS data, a web mapping application has been developed by the USGS, and customized ArcMap (by ESRI) projects are available for download at the Energy Resources Science Center Gulf Coast website. ArcGIS Publisher (by ESRI) was used to create a published map file (pmf) from each ArcMap document (.mxd). The basemap services being used in the GC map applications are from ArcGIS Online Services (by ESRI), and include the following layers: -- Satellite imagery -- Shaded relief -- Transportation -- States -- Counties -- Cities -- National Forests With the ESRI_StreetMap_World_2D service, detailed data, such as railroads and airports, appear as the user zooms in at larger scales. This map service shows the structural configuration of the top of the Travis Peak or Hosston Formations in feet below sea level. The map was produced by calculating the difference between a datum at the land surface (either the Kelly bushing elevation or the ground surface elevation) and the reported depth of the Travis Peak or Hosston. This map service also shows the thickness of the interval from the top of the Travis Peak or Hosston Formations to the top of the Cotton Valley Group.

(See USGS Digital Data Series DDS-69-E) A geographic information system focusing on the Jurassic-Cretaceous Cotton Valley Group was developed for the U.S. Geological Survey's (USGS) 2002 assessment of undiscovered, technically recoverable oil and natural gas resources of the Gulf Coast Region. The USGS Energy Resources Science Center has developed map and metadata services to deliver the 2002 assessment results GIS data and services online. The Gulf Coast assessment is based on geologic elements of a total petroleum system (TPS) as described in Dyman and Condon (2005). The estimates of undiscovered oil and gas resources are within assessment units (AUs). The hydrocarbon assessment units include the assessment results as attributes within the AU polygon feature class (in geodatabase and shapefile format). Quarter-mile cells of the land surface that include single or multiple wells were created by the USGS to illustrate the degree of exploration and the type and distribution of production for each assessment unit. Other data that are available in the map documents and services include the TPS and USGS province boundaries. To easily distribute the Gulf Coast maps and GIS data, a web mapping application has been developed by the USGS, and customized ArcMap (by ESRI) projects are available for download at the Energy Resources Science Center Gulf Coast website. ArcGIS Publisher (by ESRI) was used to create a published map file (pmf) from each ArcMap document (.mxd). The basemap services being used in the GC map applications are from ArcGIS Online Services (by ESRI), and include the following layers: -- Satellite imagery -- Shaded relief -- Transportation -- States -- Counties -- Cities -- National Forests With the ESRI_StreetMap_World_2D service, detailed data, such as railroads and airports, appear as the user zooms in at larger scales. This map service shows the structural configuration on the top of the Cotton Valley Group in feet below sea level. The map was produced by calculating the difference between a datum at the land surface (either the kelly bushing elevation or the ground surface elevation) and the reported depth of the Cotton Valley Group. This map service also shows the thickness of the interval from the top of the Cotton Valley Group to the top of the Smackover Formation.

(See USGS Digital Data Series DDS-69-H) A geographic information system focusing on the Upper Cretaceous Taylor and Navarro Groups was developed for the U.S. Geological Survey's (USGS) 2003 assessment of undiscovered, technically recoverable oil and natural gas resources of the Gulf Coast Region. The USGS Energy Resources Science Center has developed map and metadata services to deliver the 2003 assessment results GIS data and services online. The Gulf Coast assessment is based on geologic elements of a total petroleum system (TPS) as described in Condon and Dyman (2005). The estimates of undiscovered oil and gas resources are within assessment units (AUs). The hydrocarbon assessment units include the assessment results as attributes within the AU polygon feature class (in geodatabase and shapefile format). Quarter-mile cells of the land surface that include single or multiple wells were created by the USGS to illustrate the degree of exploration and the type and distribution of production for each assessment unit. Other data that are available in the map documents and services include the TPS and USGS province boundaries. To easily distribute the Gulf Coast maps and GIS data, a web mapping application has been developed by the USGS, and customized ArcMap (by ESRI) projects are available for download at the Energy Resources Science Center Gulf Coast website. ArcGIS Publisher (by ESRI) was used to create a published map file (pmf) from each ArcMap document (.mxd). The basemap services being used in the GC map applications are from ArcGIS Online Services (by ESRI), and include the following layers: -- Satellite imagery -- Shaded relief -- Transportation -- States -- Counties -- Cities -- National Forests With the ESRI_StreetMap_World_2D service, detailed data, such as railroads and airports, appear as the user zooms in at larger scales.

The City of Frederick GIS Department maintains a number of interactive web mapping applications for use by both City employees and citizens collectively known as SpiresGIS. These applications allow users to find specific information such as property zoning and plat information as well as print custom paper maps.These mapping applications were developed using ESRI's Javascript Application Programming Interface in conjunction with ESRI's ArcServer. More applications are under development, and will be listed on this page in the near future.

Extensive polygon dataset for marinas across the US and Canada. Includes custom-drawn boundaries and precise location information. Valuable for marine industry analysis, recreational planning, and coastal development projects.

The Watershed Health Assessment Framework (WHAF) mapping application was developed to support natural resource management through an approach that emphasizes ecosystem health.

The WHAF application delivers a suite of watershed health index scores, each index represents a unique facet of the ecosystem. The index scores are organized within the five components of ecosystem Health: Hydrology, Geomorphology, Biology, Connectivity, and Water Quality. This 5-Component model provides a consistent structure for delivering a balanced and repeatable exploration of watershed conditions.

In addition to health scores, the application provides access to:
- Watershed boundaries with tools to visualize patterns of water movement.
- 100+ GIS data layers that provide insights to support natural resource management decisions and planning efforts.
- The ability to chart land cover at each watershed scale.
- The ability to save and share interactive map layouts through custom links and bookmarks.

The WHAF application is accessible from any modern web browser, although we recommend using a recent version of Google Chrome or Mozilla Firefox.

The WHAF health scores are also available for use in desktop GIS software, the data can be downloaded from Watershed Health Assessment Scores.

Data delivered through the application varies in origin and date of publication. Every effort is made to use the most current data available, and keep resources in sync with updates made to the source data. For detailed information regarding the data used to derive health scores, consult the health score analysis documentation. For detailed information regarding the source and publication data of additional GIS layers, consult the GIS Data Sources list.

Alaska Regional Development Organizations (ARDORs), their contact information, and their Comprehensive Economic Development Strategies (CEDS).The mission of the ARDORs Program is to encourage the formation of regional development organizations to prepare and implement regional development strategies (Alaska Statute 44.33.896). Through regional development strategies, local knowledge, and coordinated implementation, ARDORs champion economic development planning for Alaska’s regions and communities by leveraging baseline support provided by the State of Alaska. ARDORs develop customized work plans that contain goals, objectives, and strategies for addressing regional economic development needs including:

Facilitating development of a healthy regional economy that results in sustainable business growth, new business investment, and economic diversification.Identifying and working to eliminate regional economic development barriers. Developing and implementing a comprehensive economic development strategy. Coordinating regional planning efforts that result in new employment and business opportunities. Working to enable multiple communities to collaborate and pool limited resources. Strengthening partnerships with public, private, and non-government organizations. Providing technical assistance to encourage business startup, retention, and expansion.Source: Alaska Department of Commerce, Community & Economic Development

This data provides location information for mainline railway stations operated by the Korea Railroad Corporation (KORAIL), including key information such as latitude and longitude and the number of entrances and exits for each station. It also includes information on the regional headquarters to which each station belongs, providing accurate geographical information and facility status for stations within the national mainline railway network. The data consists of 202 items and is available in various formats, including CSV, XML, and JSON. This data provides various insights, including the spatial distribution of major railway stations nationwide, accessibility (number of entrances and exits), and jurisdiction status by regional headquarters. For example, it can be used to understand station density in the metropolitan and non-metropolitan areas, accessibility to railway services in specific regions, and the size and convenience of stations based on the number of entrances and exits per station. Station location (latitude and longitude) information can be utilized for in-depth spatial data applications, such as map visualization, GIS analysis, and analysis of transportation connectivity around stations. This data can be utilized as reference material in various fields, including transportation policy formulation, station area development and regional infrastructure planning, public transportation service design, GIS-based research, regional rail service accessibility assessments, tourist information system development, emergency response system design (e.g., 119, 112), and location strategies for private and public institutions. Furthermore, it is useful for various groups, including citizens, travel agencies, and researchers, to develop customized travel plans and service strategies by utilizing the location information and number of entrances at each station.

This research paper presents the design and development of an indigenous low cost Mobile Mapping System (MMS) for urban surveying applications. The MMS is comprised of economical Hokuyo-30LX 2D laser scanners, vision sensors, Global Positioning System (GPS) and various odometric sensors that can be installed on car like moving platform. The run time sensorial data is interfaced, processed and recorded using Robot Operating System (ROS). The live laser scan is utilized for the pose estimation using Simultaneous Localization and Mapping (SLAM) technique. In absence of valid SLAM estimation and frequent GPS outages, a multimodal sensor fusion framework for the enhanced pose correction has been developed using Kalman Filter (KF) by incorporating the Inertial Measurement Unit (IMU) and wheel odometric data along with SLAM and GPS data. The corrected pose is utilized for the 3D point cloud mapping by incorporating laser scans perceived periodically from various 2D laser scanners mounted on the MMS. The custom-made installation scheme has been followed for mounting three 2D laser scanners at horizontal, vertical and inclined orientations. The efficacy of the developed map has employed for extraction of road edges and associated road assets by establishing the lucrative classification technique of the point cloud using Split and Merge segmentation and Hough transformation. The surveying to map development time has significantly reduced and the mapping results have found quite accurate when matched with the ground truths. Furthermore, the comparison of the developed maps with ground truths and GIS tools reveals the highly acceptable accuracy of the generated results which have found very nearly aligned with the actual urban environment features. In comparison to the existing global MMS variants, the presented MMS is quite affordable solution for limited financial resourced business entities.

Cartography Software Market Outlook

According to our latest research, the global cartography software market size reached USD 2.15 billion in 2024, driven by increasing demand for advanced mapping solutions across diverse sectors. The market is expected to expand at a CAGR of 9.2% between 2025 and 2033, with the market size forecasted to reach USD 4.79 billion by 2033. This robust growth is primarily attributed to rapid urbanization, the proliferation of geospatial data, and growing integration of GIS technologies in government and commercial applications.

The primary growth factor propelling the cartography software market is the accelerating adoption of geospatial intelligence and geographic information systems (GIS) across various sectors. Governments, urban planners, and commercial enterprises are increasingly leveraging cartography software for enhanced decision-making, spatial data visualization, and resource management. The surge in smart city initiatives and infrastructure development projects worldwide is further boosting demand for sophisticated mapping tools. These tools enable stakeholders to visualize complex datasets, analyze spatial relationships, and optimize planning processes, thereby improving efficiency and reducing operational costs.

Another significant driver is the technological evolution within the cartography software landscape. The integration of artificial intelligence, machine learning, and cloud computing has transformed traditional mapping solutions into dynamic, interactive, and real-time platforms. These advancements have broadened the application scope of cartography software, making it indispensable in fields such as disaster management, environmental monitoring, and business intelligence. The ability to process large volumes of geospatial data quickly and accurately has enhanced the value proposition of cartography solutions, attracting investments from both public and private sectors.

Furthermore, the growing need for disaster risk management and environmental monitoring is catalyzing the adoption of cartography software. Governments and humanitarian organizations are increasingly utilizing these tools to map vulnerable areas, monitor climate change impacts, and plan emergency response strategies. The software’s capability to provide real-time situational awareness and predictive analytics is critical in mitigating risks and enhancing preparedness. As climate-related challenges intensify, the reliance on advanced cartographic solutions is expected to deepen, further fueling market growth.

From a regional perspective, North America currently dominates the cartography software market, supported by substantial investments in geospatial infrastructure and a high concentration of technology-driven enterprises. However, Asia Pacific is poised for the fastest growth, driven by rapid urbanization, expanding infrastructure projects, and increasing government focus on smart city development. Europe also holds a significant share, benefiting from robust regulatory frameworks and widespread adoption of GIS technologies across various sectors. The Middle East & Africa and Latin America are emerging as promising markets, with growing awareness of the benefits of digital mapping in resource management and urban planning.

Component Analysis

The cartography software market by component is bifurcated into software and services. The software segment captures the largest market share, accounting for over 65% in 2024, owing to the widespread adoption of advanced mapping solutions across government, commercial, and utility sectors. Modern cartography software platforms offer comprehensive features such as data visualization, spatial analysis, and real-time collaboration, making them indispensable tools for urban planners, environmental agencies, and businesses. The proliferation of open-source platforms and the availability of customizable mapping solutions have further accelerated the adoption of cartography software globally.
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Aquatic environmental DNA (eDNA) sampling is the collection of DNA released by a target species into streams, rivers, ponds, lakes, and wetlands. Detection of stream fish with eDNA can be remarkably sensitive—100% detection efficiency of target species has been achieved despite order-of-magnitude changes in stream discharge. The eDNA samples in the eDNAtlas database describe species occurrence locations and were collected by the U.S. Forest Service and numerous agencies that have partnered with the National Genomics Center for Wildlife and Fish Conservation (NGC) throughout the United States. The data were collected for a variety of project-specific purposes that included: species status assessments, trend monitoring at one or many sites, development of predictive species distribution models, detection and tracking of non-native species invasions, and assessments of habitat restoration efforts. The eDNAtlas database consists of two feature classes. The first component (eDNAtlas_West_AGOL_ResultsOnly) is a database of georeferenced species occurrence locations based on eDNA field sampling results, which are downloadable by species through a dynamic ArcGIS Online (AGOL) mapping tool. The earliest eDNA samples in the database were collected in 2015 but new samples and results are added annually to the database, which houses thousands of species occurrence records. The second component (eDNAtlas_West_SampleGridAndResults) is a systematically-spaced 1-kilometer grid of potential sample points in streams and rivers throughout the western United States. Future versions will include the eastern United States as well. The points in the sampling grid are arrayed along the medium-resolution National Hydrography Dataset Version 2 (NHDPlusV2) and can be used to develop custom eDNA sampling strategies for many purposes. Each sample point has a unique identity code that enables efficient integration of processed eDNA sample results with the species occurrence database. The eDNAtlas is accessed via an interactive ArcGIS Online (AGOL) map that allows users to view and download sample site information and lab results of species occurrence for the U.S. The results are primarily based on samples analyzed at the National Genomics Center for Wildlife and Fish Conservation (NGC) and associated with geospatial attributes created by the Boise Spatial Streams Group (BSSG). The AGOL map displays results for all species sampled within an 8-digit USGS hydrologic unit or series of units. The map initially opens to the project extent, but allows users to zoom to areas of interest. Symbols indicate whether a field sample has been collected and processed at a specific location, and if the latter, whether the target species was present. Each flowing-water site is assigned a unique identification code in the database to ensure that it can be tracked and matched to geospatial habitat descriptors or other attributes for subsequent analyses and reports. Because no comparable database has been built for standing water, results for those sites lack this additional information but still provide data on the sample and species detected. Resources in this dataset:Resource Title: The Aquatic eDNAtlas Project: Lab Results Map - USFS RMRS. File Name: Web Page, url: https://usfs.maps.arcgis.com/apps/webappviewer/index.html?id=b496812d1a8847038687ff1328c481fa The eDNAtlas is accessed via an interactive ArcGIS Online (AGOL) map that allows users to view and download sample site information and lab results of species occurrence for the U.S. The results are primarily based on samples analyzed at the National Genomics Center for Wildlife and Fish Conservation (NGC) and associated with geospatial attributes created by the Boise Spatial Streams Group (BSSG). The AGOL map displays results for all species sampled within an 8-digit USGS hydrologic unit or series of units. The map initially opens to the project extent, but allows users to zoom to areas of interest. Symbols indicate whether a field sample has been collected and processed at a specific location, and if the latter, whether the target species was present. Each flowing-water site is assigned a unique identification code in the database to ensure that it can be tracked and matched to geospatial habitat descriptors or other attributes for subsequent analyses and reports. Because no comparable database has been built for standing water, results for those sites lack this additional information but still provide data on the sample and species detected. For details on using the map see the Aquatic eDNAtlas Project: Lab Results ArcGIS Online Map Guide.

In 2015, under contract to the Marin Municipal Water district (MMWD), Aerial Information Systems, Inc. (AIS) conducted the photo interpretation of sudden oak death (SOD) affected vegetation stands for the Mt. Tamalpais Watershed Forest and Woodlands Project. They looked at 2014 imagery and used the 2009 remap layer to reclassify stands which had changed over the past 5 years, these stands were changed mostly due to SOD. The mapping study area consists of approximately 18,986 acres of Marin county. Work was performed on the project in 2015 by using the 2014 imagery to mark changes in vegetation. The primary purpose of the project was to find areas where vegetation had changed because of SOD and to show where gaps were formed by fallen oak trees. There was a total of 99 mapping classes. Vegetation with field questions map class is not symbolized in the cartography.CNPS under separate contract and in collaboration with CDFW VegCAMP developed the floristic vegetation classification used for the project. The floristic classification follows protocols compliant with the Federal Geographic Data Committee (FGDC) and National Vegetation Classification Standards (NVCS).The 2009 vegetation map was updated applying heads-up digitizing techniques using a 2014 base of 6-inch resolution, natural color imagery provided by MMWD, in conjunction with custom ArcGIS tools that AIS developed to update the existing 2009 database. Mapped polygons were assessed for change in Vegetation Type as a result of SOD. More information can be found in the project report which is bundled with the BIOS vegetation map.

The Editor application for of a set (Editing/Printing) of STR-GIS developed Web-map applications that will enable the Traffic Services Investigator Area Administration staff to maintain in real-time, staff's attribute information in the STR-GIS database. In conjunction with the print applications a custom-built print template, Traffic Services Investigator Administration staff will be able to produce a PDF exhibit with any edited or new information on demand. AD GroupsSTRGR_GIS_AdministratorsSTRGR_GIS_TSInvstgtr_AdminArea_EDIT

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable. For a deep dive into the data model including every specific metric, see the ACS 2017-2021 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e21Estimate from 2017-21 ACS_m21Margin of Error from 2017-21 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_21Change, 2010-21 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLine (buffer)BeltLine Study (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Planning Unit STV (3 NPUs merged to a single geographic unit within City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)City of Atlanta Neighborhood Statistical Areas E02E06 (2 NSAs merged to single geographic unit within City of Atlanta)County (statewide)Georgia House (statewide)Georgia Senate (statewide)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)SPARCC = Strong, Prosperous And Resilient Communities ChallengeState of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)WFF = Westside Future Fund (subarea of City of Atlanta)ZIP Code Tabulation Areas (statewide)The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2017-2021). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2017-2021Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://garc.maps.arcgis.com/sharing/rest/content/items/34b9adfdcc294788ba9c70bf433bd4c1/data

Important Note

The Brownfield data was handed over from LDA to the Homes and Communities Agency so that HCA could maintain it as part of the National Land Use Database (NLUD). The HCA’s online mapping site displays a points only version of NLUD from 2010 (password protected):

<https://signet.hca-online.org.uk/live/custom/login/SIGnet.aspx>

The links to the files below will remain here as a matter of historical record.

Polygons showing the boundaries of Brownfield land in London along with their addresses.

This database of Brownfield land replaces in more detail and accuracy the EP National Land Use Database (NLUD) for London. The current NLUD assessment covers sites in excess of 0.25ha. This project validates, checks and updates this information for existing NLUD sites plus new sites down to a smaller threshold of 0.1 hectares and above.

The Database records over 2,000 Brownfield sites across London, equivalent to more than 2% of the land in Greater London (an increase of over 1,000 sites than recorded on the previous system). The Homes and Communities Agency will use the database as their preferred platform for boroughs to record brownfield sites.

The London Database uses Geographic Information Systems (GIS) mapping. It includes transport routes, deprivation, social infrastructure, as well as heritage and natural environment assets that can be overlaid over the dataset of brownfield land. Visitors to the Database website can identify sites suitable for development, and better explore and understand a site’s context.

For more information visit the HCA website

This layer provides generalized boundaries for the 50 States and the District of Columbia, developed by Esri from US Census Bureau public domain sources and updated as boundaries change.Attribute fields include 2020 total population from the US Census PL94 data.This ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, StoryMaps, custom apps, and mobile apps. The data can also be exported for offline workflows. Cite the 'U.S. Census Bureau' when using this data.