About this itemStory Maps are a powerful platform that integrate spatial thinking with storytelling to present information in a compelling, interactive and easy to understand format. The University of Minnesota StoryMaps team provides support and resources for faculty looking to incorporate spatial tools such as StoryMaps, Survey 123 and other web-based GIS applications into their classrooms. The UMN StoryMaps site has examples of student projects, samples of project ideas/assignments/rubrics and user guides for students. This team’s work has received national recognition for promoting the role of spatial thinking and StoryMaps in higher education, K12 and informal learning spaces.Author/ContributorU-SpatialOrganizationUniversity of MinnesotaOrg Websitesystem.umn.edu

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

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.

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This feature collection, UCR Timber Harvest & Veg Management Activities_2022, provides the proposed timber harvest and vegetation management data within the ongoing Upper Cheat River project by the U.S. Forest Service for Monongahela National Forest, West Virginia.Purpose:This data was created by the U.S. Forest Service staff for use in analysis of the project’s likely environmental impacts.Source & Date:The source data was created in 2020 and downloaded in zipped ESRI shapefile format (GIS Shapefiles.zip) from the USFS project page (Analysis folder). The data was downloaded on July 1, 2021, and subsequently updated. The data is current as of March 29, 2022. Processing:ABRA published the source shapefiles from ArcMap as a feature layer. That feature layer was published as a feature collection to allow grouping in Map Viewer Classic. The sub-layers were symbolized using the provided map document as an example (Scoping Information and Maps.pdf).UCR Timber Harvest & Veg Management Activities_2022 contains the following data layers:UCR_TSIUCR_WildlifeHabitatEnhancementsUCR_FireBlocksUCR_ExWLOMaintenanceUCR_PotentialCommericalHarvestUnitsUCR_FireLines Symbology:The list below refers to the data layers above, named as shown in the Upper Cheat Project map provided by ABRA.Timber Stand Improvement Units: Light blue polygonWildlife Habitat Enhancements:Cutback Borders: a purple polygonDaylighting: green polygonWildlife Opening Expansion: yellow polygonBurn Blocks: brown polygonExisting Wildlife Opening Maintenance: red polygonPotential Commercial Harvest Units: Cable Timber Units: green polygonConventional Timber Units: green polygon with dark green outlineHelicopter Timber Units: clear polygon with red outlineFire Handlines: purple polylineMore information can be found on ABRA’s project description page, hosted by the National Forest Integrity Project. Additional detailed information is available on the USFS project page.

This Innovative Technology Experiences for Students and Teachers (ITEST) project has developed, implemented, and evaluated a series of innovative Socio-Environmental Science Investigations (SESI) using a geospatial curriculum approach. It is targeted for economically disadvantaged 9th grade high school students in Allentown, PA, and involves hands-on geospatial technology to help develop STEM-related skills. SESI focuses on societal issues related to environmental science. These issues are multi-disciplinary, involve decision-making that is based on the analysis of merged scientific and sociological data, and have direct implications for the social agency and equity milieu faced by these and other school students. This project employed a design partnership between Lehigh University natural science, social science, and education professors, high school science and social studies teachers, and STEM professionals in the local community to develop geospatial investigations with Web-based Geographic Information Systems (GIS). These were designed to provide students with geospatial skills, career awareness, and motivation to pursue appropriate education pathways for STEM-related occupations, in addition to building a more geographically and scientifically literate citizenry. The learning activities provide opportunities for students to collaborate, seek evidence, problem-solve, master technology, develop geospatial thinking and reasoning skills, and practice communication skills that are essential for the STEM workplace and beyond. Despite the accelerating growth in geospatial industries and congruence across STEM, few school-based programs integrate geospatial technology within their curricula, and even fewer are designed to promote interest and aspiration in the STEM-related occupations that will maintain American prominence in science and technology. The SESI project is based on a transformative curriculum approach for geospatial learning using Web GIS to develop STEM-related skills and promote STEM-related career interest in students who are traditionally underrepresented in STEM-related fields. This project attends to a significant challenge in STEM education: the recognized deficiency in quality locally-based and relevant high school curriculum for under-represented students that focuses on local social issues related to the environment. Environmental issues have great societal relevance, and because many environmental problems have a disproportionate impact on underrepresented and disadvantaged groups, they provide a compelling subject of study for students from these groups in developing STEM-related skills. Once piloted in the relatively challenging environment of an urban school with many unengaged learners, the results will be readily transferable to any school district to enhance geospatial reasoning skills nationally.

This dataset identifies the project boundaries for registered Emissions Reduction Fund (ERF) area based projects. Area based projects are generally savanna burning and sequestration activities. These ERF Projects are registered across a declared project area identified by project proponents at the time of registration. These project areas generally encompass the entire cadastral boundaries for the properties for which the participants intend to conduct their project activities and for which they hold the legal rights. For sequestration projects the project area does not generally represent the actual extent of a project activity which is generally a subset of the project area. These subset areas are known as Carbon Estimation Areas (CEA) which are defined by rules set out in the individual ERF methods. A project can contain one or many CEAs. The dataset includes basic attribution including: Scheme Participant; Project Name; Project ID; Method; Method Type; Project Description; Date Project Registered; Project location (State); Project location (Postcode); Permanence Period; and, Project Status (Active or Revoked) The Clean Energy Regulator publishes and maintains a project register which contains further details about projects registered under the Emissions Reduction Fund. The project register is published on the Clean Energy Regulator website at http://www.cleanenergyregulator.gov.au/DocumentAssets/Pages/Emissions-Reduction-Fund-Register.aspx and is the point of truth for information about ERF projects. The project register contains attributes not in the spatial dataset, such as, the number of Australian carbon credit units (ACCUs) issued, whether any units have been relinquished, or if that land has a carbon maintenance obligation in place. However, the Project Id attribute (PROJ_ID) can be used to link the mapping data with the project register if analysis of those attributes is required. Notes: 1. Users should be aware that the project register is updated on a weekly basis. 2. The dataset does not contain the boundaries of ten projects which have had their location suppressed or partially suppressed. 3. The dataset contains revoked projects. These are identified as being revoked in the status column

Clean Transportation Program Data 2022. The Clean Transportation Program (also known as Alternative and Renewable Fuel and Vehicle Technology Program) invests up to $100 million annually in a broad portfolio of transportation and fuel transportation projects throughout the state. The Energy Commission leverages public and private investments to support adoption of cleaner transportation powered by alternative and renewable fuels. The program plays an important role in achieving California’s ambitious goals on climate change, petroleum reduction, and adoption of zero-emission vehicles, as well as efforts to reach air quality standards. The program also supports the state’s sustainable, long-term economic development.Data within this application was last updated August 2024.For more information on the Clean Transportation Program, visit:https://www.energy.ca.gov/programs-and-topics/programs/clean-transportation-program

This dataset identifies the project boundaries for registered Australian Carbon Credit Unit (ACCU) Scheme area based projects. Area based projects are generally savanna burning and sequestration activities. These ACCU Scheme projects are registered across a declared project area identified by project proponents at the time of registration. These project areas generally encompass the entire cadastral boundaries for the properties for which the participants intend to conduct their project activities and for which they hold the legal rights. For sequestration projects the project area does not generally represent the actual extent of a project activity which is generally a subset of the project area. These subset areas are known as Carbon Estimation Areas (CEA) which are defined by rules set out in the individual ACCU Scheme methods. A project can contain one or many CEAs. The dataset includes basic attribution including: Scheme Participant; Project Name; Project ID; Method; Method Type; Project Description; Date Project Registered; Project location (State); Project location (Postcode); Permanence Period; Project Status (Active or Revoked); and, Area Ha The Clean Energy Regulator publishes and maintains a project register which contains further details about projects registered under the Emissions Reduction Fund. The project register is published on the Clean Energy Regulator website at https://cer.gov.au/markets/reports-and-data/accu-project-and-contract-register.aspx and is the point of truth for information about ACCU Scheme projects. The project register contains attributes not in the spatial dataset, such as, the number of Australian carbon credit units (ACCUs) issued, whether any units have been relinquished, or if that land has a carbon maintenance obligation in place. However, the Project Id attribute (PROJ_ID) can be used to link the mapping data with the project register if analysis of those attributes is required. Notes: 1. Users should be aware that the project register is updated on a monthly basis. 2. The dataset does not contain the boundaries of ten projects which have had their location suppressed or partially suppressed. 3. The dataset contains revoked projects. These are identified as being revoked in the status column

Beginning with the discovery of a "curious valley" in 1903 by Captain Scott, the McMurdo Dry Valleys (MDV) in Antarctica have been impacted by humans, although there were only three brief visits prior to 1950. Since the late 1950's, human activity in the MDV has become commonplace in summer, putting pressure on the region's fragile ecosystems through camp construction and inhabitation, cross-valley transport on foot and via vehicles, and scientific research that involves sampling and deployment of instruments. Historical photographs, put alongside information from written documentation, offer an invaluable record of the changing patterns of human activity in the MDV. Photographic images often show the physical extent of field camps and research sites, the activities that were taking place, and the environmental protection measures that were being followed. Historical photographs of the MDV, however, are scattered in different places around the world, often in private collections, and there is a real danger that many of these photos may be lost, along with the information they contain. This project will collect and digitize historical photographs of sites of human activity in the MDV from archives and private collections in the United States, New Zealand, and organize them both chronologically and spatially in a GIS database. Sites of past human activities will be re-photographed to provide comparisons with the present, and re-photography will assist in providing spatial data for historical photographs without obvious location information. The results of this analysis will support effective environmental management into the future. The digital photo archive will be openly available through the McMurdo Dry Valleys Long Term Ecological Research (MCM LTER) website (www.mcmlter.org), where it can be used by scientists, environmental managers, and others interested in the region.

The central question of this project can be reformulated as a hypothesis: Despite an overall increase in human activities in the MDV, the spatial range of these activities has become more confined over time as a result of an increased awareness of ecosystem fragility and efforts to manage the region. To address this hypothesis, the project will define the spatial distribution and temporal frequency of human activity in the MDV. Photographs and reports will be collected from archives with polar collections such as the National Archives of New Zealand in Wellington and Christchurch and the Byrd Polar Research Center in Ohio. Private photograph collections will be accessed through personal connections, social media, advertisements in periodicals such as The Polar Times, and other means. Re-photography in the field will follow established techniques and will create benchmarks for future research projects. The spatial data will be stored in an ArcGIS database for analysis and quantification of the human footprint over time in the MDV. The improved understanding of changing patterns of human activity in the MDV provided by this historical photo archive will provide three major contributions: 1) a fundamentally important historic accounting of human activity to support current environmental management of the MDV; 2) defining the location and type of human activity will be of immediate benefit in two important ways: a) places to avoid for scientists interested in sampling pristine landscapes, and, b) targets of opportunity for scientists investigating the long-term environmental legacy of human activity; and 3) this research will make an innovative contribution to knowledge of the environmental history of the MDV.

This data set was developed to provide geologic map GIS of the Coeur d'Alene 1:100,000 quadrangle for use in future spatial analysis by a variety of users. These data can be printed in a variety of ways to display various geologic features or used for digital analysis and modeling. This database is not meant to be used or displayed at any scale larger than 1:100,000 (e.g. 1:62,500 or 1:24,000).

The digital geologic map of the Coeur d'Alene 1:100,000 quadrangle was compiled from preliminary digital datasets [Athol, Coeur d'Alene, Kellogg, Kingston, Lakeview, Lane, and Spirit Lake 15-minute quadrangles] prepared by the Idaho Geological Survey from A. B. Griggs (unpublished field maps), supplemented by Griggs (1973) and by digital data from Bookstrom and others (1999) and Derkey and others (1996). The digital geologic map database can be queried in many ways to produce a variety of derivative geologic maps.

This GIS consists of two major Arc/Info data sets: one line and polygon file (cda100k) containing geologic contacts and structures (lines) and geologic map rock units (polygons), and one point file (cda100kp) containing structural data.

This dataset contains 63 shapefiles that represent the areas of relevance for each research project under the National Environmental Science Program Marine and Coastal Hub, northern and southern node projects for Rounds 1, 2 & 3.

Methods:
Each project map is developed using the following steps:
1. The project map was drawn based on the information provided in the research project proposals.
2. The map was refined based on feedback during the first data discussions with the project leader.
3. Where projects are finished most maps were updated based on the extents of datasets generated by the project and followup checks with the project leader.

The area mapped includes on-ground activities of the project, but also where the outputs of the project are likely to be relevant. The maps were refined by project leads, by showing them the initial map developed from the proposal, then asking them "How would you change this map to better represent the area where your project is relevant?". In general, this would result in changes such as removing areas where they were no longer intending research to be, or trimming of the extents to better represent the habitats that are relevant.

The project extent maps are intentionally low resolution (low number of polygon vertices), limiting the number of vertices 100s of points. This is to allow their easy integration into project metadata records and for presenting via interactive web maps and spatial searching. The goal of the maps was to define the project extent in a manner that was significantly more accurate than a bounding box, reducing the number of false positives generated from a spatial search. The geometry was intended to be simple enough that projects leaders could describe the locations verbally and the rough nature of the mapping made it clear that the regions of relevance are approximate.

In some cases, boundaries were drawn manually using a low number of vertices, in the process adjusting them to be more relevant to the project. In others, high resolution GIS datasets (such as the EEZ, or the Australian coastline) were used, but simplified at a resolution of 5-10km to ensure an appopriate vertices count for the final polygon extent. Reference datasets were frequently used to make adjustments to the maps, for example maps of wetlands and rivers were used to better represent the inner boundary of projects that were relevant for wetlands.

In general, the areas represented in the maps tend to show an area larger then the actual project activities, for example a project focusing on coastal restoration might include marine areas up to 50 km offshore and 50 km inshore. This buffering allows the coastline to be represented with a low number of verticies without leading to false negatives, where a project doesn't come up in a search because the area being searched is just outside the core area of a project.


Limitations of the data:
The areas represented in this data are intentionally low resolution. The polygon features from the various projects overlap significantly and thus many boundaries are hidden with default styling. This dataset is not a complete representation of the work being done by the NESP MaC projects as it was collected only 3 years into a 7 year program.

Format of the data:
The maps were drawn in QGIS using relevant reference layers and saved as shapefiles. These are then converted to GeoJSON or WKT (Well-known Text) and incorporated into the ISO19115-3 project metadata records in GeoNetwork. Updates to the map are made to the original shapefiles, and the metadata record subsequently updated.

All projects are represented as a single multi-polygon. The multiple polygons was developed by merging of separate areas into a single multi-polygon. This was done to improve compatibility with web platforms, allowing easy conversion to GeoJSON and WKT.

This dataset will be updated periodically as new NESP MaC projects are developed and as project progress and the map layers are improved. These updates will typically be annual.


Data dictionary:
NAME - Title of the layer
PROJ - Project code of the project relating to the layer
NODE - Whether the project is part of the Northern or Southern Nodes
TITLE - Title of the project
P_LEADER - Name of the Project leader and institution managing the project
PROJ_LINK - Link to the project metadata
MAP_DESC - Brief text description of the map area
MAP_TYPE - Describes whether the map extent is a 'general' area of relevance for the project work, or 'specific' where there is on ground survey or sampling activities
MOD_DATE - Last modification date to the individual map layer (prior to merging)


Updates & Processing:
These maps were created by eAtlas and IMAS Data Wranglers as part of the NESP MaC Data Management activities. As new project information is made available, the maps may be updated and republished. The update log will appear below with notes to indicate when individual project maps are updated:
20220626 - Dataset published (All shapefiles have MOD_DATE 20230626)


Location of the data:
This dataset is filed in the eAtlas enduring data repository at: data\custodian
esp-mac-3\AU_AIMS-UTAS_NESP-MaC_Project-extents-maps

This dashboard draws upon a live spreadsheet maintained by the ACJV to track progress towards goals identified in the Saltmarsh Bird, Saltmarsh Sparrow, Black Rail, and American Black Duck Conservation Plans. All plans are accessible here. This dashboard identifies all Strategies, Objectives, and Actions in each of the plans and tracks the ACJV’s progress in attaining these goals. The ACJV staff meet annually to update the information contained in this tool. This tool was last updated in October 2023. The next planned update will occur in October 2024.This dashboard draws upon a live spreadsheet maintained by the ACJV to track progress towards goals identified in the Saltmarsh Bird, Saltmarsh Sparrow, Black Rail, and American Black Duck Conservation Plans. All plans are accessible here. This dashboard identifies all Strategies, Objectives, and Actions in each of the plans and tracks the ACJV’s progress in attaining these goals. The ACJV staff meet annually to update the information contained in this tool. This tool was last updated in October 2023. The next planned update will occur in October 2024.

Mapped ideas represent points of feedback from Brampton citizens as part of a neighbourhood audit called the Nurturing Neighbourhoods Program.This data is a component of the Nurturing Neighbourhoods Program, a neighbourhood audit public engagement project that is part of Brampton Vision 2040: https://nurturingneighbourhoods.brampton.ca/

Fayette County Ohio GIS Addresses Location Based Response System (LBRS). The information provided is for reference only and subject to independent verification. User assumes all responsibility for its use.https://das.ohio.gov/technology-and-strategy/ogrip/projects/lbrsOHIO'S LOCATION BASED RESPONSE SYSTEMThe Location Based Response System (LBRS) is an initiative of the Ohio Geographically Referenced Information Program (OGRIP). The LBRS establishes partnerships between State and County government for the creation of spatially accurate street centerlines with address ranges and field verified site-specific address locations.Funding to support the development of LBRS compliant systems is available to counties through a Memorandum of Agreement (MOA) that establishes roles and responsibilities for program participation. Participating counties provide project management and QA/QC on road names, addresses, etc to develop data that is compatible with the state's legacy roadway inventory.The Ohio Department of Transportation is the LBRS Program Sponsor, providing technical guidance, support, and QA/QC services. The program is being administered by OGRIP, the state's coordinating body for Geographic Information System (GIS) activities.Through the collaborative efforts of State and Local government the LBRS program is producing highly accurate field verified data that is current, complete, consistent, and accessible. LBRS data is maintained as an Ohio asset by local resources and is provided to the state as part of a coordinated long-term effort by OGRIP to reduce redundant data collection by developing data that meets the needs of several levels of government.The LBRS supports a multi-jurisdictional approach to protecting the health, safety and welfare of the state’s constituents.LBRS FAQsWhat is the LBRS? The LBRS is a County/State partnership that gathers accurate locational information on all roads and addresses in a county. The information is used to save lives and save taxpayer dollars by reducing redundant data collection activities. The information is web-based, and is therefore current for all stakeholders as agencies or local governments gather new information.Who is using LBRS data? 9-1-1 Dispatch/First Responders, County Auditors, County Commissioners and Engineers, Ohio Highway Patrol/MARCS, County Emergency Management Agencies, Ohio Department of Transportation, US Department of Homeland Security, US Census Bureau, Ohio Department of Natural Resources, Ohio Department of Agriculture, Ohio Utilites Protection Service.“Both disaster planning and emergency response efforts will benefit from the LBRS. By participating, counties may reduce redundant mapping projects while ensuring that Ohio’s citizens do not pay for multiple mapping initiatives.” - Shawn Smith, Ohio 911 Coordinator - Public Utilities Commission of Ohio.How is the state share determined per county? Each county that participates is assigned a ceiling amount, based on number of addressable structures and miles of public roads. The state share does not exceed 50% of the county’s cost of gathering data, and never exceeds the pre-determined ceiling.What other funding sources are available to support a county's LBRS development? The Ohio Department of Transportation (ODOT) and the County Engineers Association of Ohio (CEAO) each administer Safety Grant programs with funds that can be applied to LBRS projects. How are LBRS funds distributed? Counties enter into a Memorandum of Agreement with the state to secure funding. Counties may contract with a vendor or collect information on their own, with OIT/OGRIP and ODOT providing technical guidance throughout the process. Monies are deliverable based, as a County provides data that meets the State defined standards for program acceptance, the monies are released to the County.

Fayette County Ohio GIS Road Centerlines Location Based Response System (LBRS). The information provided is for reference only and subject to independent verification. User assumes all responsibility for its use.https://ogrip.oit.ohio.gov/ProjectsInitiatives/LBRS.aspxOHIO'S LOCATION BASED RESPONSE SYSTEMThe Location Based Response System (LBRS) is an initiative of the Ohio Geographically Referenced Information Program (OGRIP). The LBRS establishes partnerships between State and County government for the creation of spatially accurate street centerlines with address ranges and field verified site-specific address locations.Funding to support the development of LBRS compliant systems is available to counties through a Memorandum of Agreement (MOA) that establishes roles and responsibilities for program participation. Participating counties provide project management and QA/QC on road names, addresses, etc to develop data that is compatible with the state's legacy roadway inventory.The Ohio Department of Transportation is the LBRS Program Sponsor, providing technical guidance, support, and QA/QC services. The program is being administered by OGRIP, the state's coordinating body for Geographic Information System (GIS) activities.Through the collaborative efforts of State and Local government the LBRS program is producing highly accurate field verified data that is current, complete, consistent, and accessible. LBRS data is maintained as an Ohio asset by local resources and is provided to the state as part of a coordinated long-term effort by OGRIP to reduce redundant data collection by developing data that meets the needs of several levels of government.The LBRS supports a multi-jurisdictional approach to protecting the health, safety and welfare of the state’s constituents.LBRS FAQsWhat is the LBRS? The LBRS is a County/State partnership that gathers accurate locational information on all roads and addresses in a county. The information is used to save lives and save taxpayer dollars by reducing redundant data collection activities. The information is web-based, and is therefore current for all stakeholders as agencies or local governments gather new information.Who is using LBRS data? 9-1-1 Dispatch/First Responders, County Auditors, County Commissioners and Engineers, Ohio Highway Patrol/MARCS, County Emergency Management Agencies, Ohio Department of Transportation, US Department of Homeland Security, US Census Bureau, Ohio Department of Natural Resources, Ohio Department of Agriculture, Ohio Utilites Protection Service.“Both disaster planning and emergency response efforts will benefit from the LBRS. By participating, counties may reduce redundant mapping projects while ensuring that Ohio’s citizens do not pay for multiple mapping initiatives.” - Shawn Smith, Ohio 911 Coordinator - Public Utilities Commission of Ohio.How is the state share determined per county? Each county that participates is assigned a ceiling amount, based on number of addressable structures and miles of public roads. The state share does not exceed 50% of the county’s cost of gathering data, and never exceeds the pre-determined ceiling.What other funding sources are available to support a county's LBRS development? The Ohio Department of Transportation (ODOT) and the County Engineers Association of Ohio (CEAO) each administer Safety Grant programs with funds that can be applied to LBRS projects. How are LBRS funds distributed? Counties enter into a Memorandum of Agreement with the state to secure funding. Counties may contract with a vendor or collect information on their own, with OIT/OGRIP and ODOT providing technical guidance throughout the process. Monies are deliverable based, as a County provides data that meets the State defined standards for program acceptance, the monies are released to the County.

This Project Summary Report (PSR) was prepared by Modern Geosciences, LLC (Modern) for the City of Dallas (City) to provide a summary of project activities and air monitoring data collected during the full duration of remedial project at the Former Blue Star Recycling facility located at 9527 South Central Expressway, Dallas, Dallas County, Texas (Site). The Project Summary Report, dated October 26, 2023, provides a comprehensive overview of the project's scope, objectives, and progress to date. This detailed report includes key milestones, challenges encountered, and the strategies implemented to address them. It serves as a critical document for stakeholders to assess the project's alignment with its goals, timelines, and resource allocation.The report highlights achievements, including completed tasks and measurable outcomes, while also outlining ongoing activities and future priorities. Specific data points, such as budget usage, performance metrics, and any deviations from the original plan, are presented to ensure transparency and accountability.

The Forest Service's Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS) is the agency standard for managing information aboutactivities related to fire/fuels, silviculture, and invasive species. FACTS is an activity tracking application for all levels of the Forest Service. The application allows tracking and monitoring of NEPA decisions as well as the ability to create and manage KV trust fund plans at the timber sale level. This application complements its companion NRM applications, which cover the spectrum of living and non-living natural resource information. This layer represents Collaborative Forest Landscape Restoration (CFLR) Program project activities. Also included are other High Priority Restoration projects that are funded outside of CFLR. It is important to note that this layer does not contain all of the approved project activities. Instead, these are the accomplishments that project groups uploaded to the Forest Service corporate data holdings in FACTS. As spatial data is a new requirement for the program, improvements to the quality and comprehensiveness of this data is expected in coming years. Metadata

Examples of conservation efforts pulled out of Regional Implementation Plans. Stream lines extracted from StreamNet database.The Pacific Lamprey Conservation Initiative (PLCI) is a collaboration of Native American tribes, federal, state, municipal and local agencies, and non-governmental organizations working to achieve long-term persistence of Pacific Lamprey, their habitats, and support their traditional tribal use throughout their historical range spanning the West Coast of North America.The intent of the partnership is to achieve this goal, where ecologically and economically feasible, by maintaining viable populations and habitats in areas where Pacific Lamprey exist currently, restoring where they are at risk of extirpation or are extirpated, and doing so in a manner that addresses the importance of lamprey to tribal peoples. PLCI envisions a future where threats to Pacific Lamprey and their habitats are reduced, and the historic geographic range and ecological role of Pacific Lamprey are restored to the greatest extent possible.

Working both collectively and independently, Gulf Coast Joint Venture partners conduct activities in support of bird conservation goals cooperatively developed by the partnership. These activities include biological planning, conservation design, and prioritization, project development and implementation, monitoring, evaluation, and applied research activities, communications and outreach, and fund-raising for projects and activities.This shapefile is a detailed version of the Gulf Coast Joint Venture (GCJV) boundary. A more generalized version is available at https://ecos.fws.gov/ServCat/Reference/Profile/81415. This dataset includes the boundaries of the GCJV Initiative Areas: Laguna Madre, Texas Mid-Coast, Chenier Plain, Mississippi River Coastal Wetlands, and Coastal Mississippi - Alabama Initiative Areas.

Working both collectively and independently, Gulf Coast Joint Venture partners conduct activities in support of bird conservation goals cooperatively developed by the partnership. These activities include biological planning, conservation design, and prioritization, project development and implementation, monitoring, evaluation, and applied research activities, communications and outreach, and fund-raising for projects and activities.This feature layer shows the boundary for the Gulf Coast Joint Venture.