Best Management Practices (BMPs) are structural controls used to manage stormwater runoff. Examples include green roofs, rain gardens, and cisterns. BMPs reduce the effects of stormwater pollution and help restore the District’s waterbodies. The District’s stormwater regulations require that large construction or renovation projects install BMPs to manage stormwater runoff once construction is complete. The District also provides financial incentives for properties that install BMPs voluntarily. This dataset includes BMPs that were installed to comply with the District’s stormwater regulations, to participate in the Stormwater Retention Credit (SRC) trading program, to participate in the RiverSmart Homes program, to participate in the Green Roof Rebate program, or to participate in the RiverSmart Rewards stormwater fee discount program. These BMPs have been reviewed by the Department of Energy and Environment (DOEE) as part of these programs. This dataset is updated weekly with data from the District’s Stormwater Database.

Stave off the garbage-in-garbage-out scenario and learn how to maintain authoritative geographic data. The courses and resources below will help you build the skills needed to store, organize, update, and disseminate accurate data that supports sound decision-making.Goals Create a geodatabase to organize and manage geographic data. Deploy recommended editing workflows to update 2D and 3D data. Apply ArcGIS best practices to maintain the accuracy of geographic data over time.

This layer represents locations of best management practices (BMPs) reported to the Board of Water and Soil Resources (BWSR) via the eLINK grant reporting system from 2003 to present. This dataset includes all mapped BMPs associated with completed and closed grants. Practices connected with open grants considered “in progress” are included here only if an installed date has also been reported. Please be advised, any practice(s) associated with open grants are subject to change until all grants have final reports submitted, approved and grants are closed. The attribute HAS_OPEN_GRANTS_YN can be used to determine if a practice may be subject to change in the future.

The eLINK data represents BWSR grants funded through BWSR via the State General Fund, Clean Water Fund, and Bonding funds. Practices funded through the Minnesota Pollution Control Agency (MPCA) Clean Water Partnership and 319 programs and Minnesota Department of Health (MDH) well sealing grants are also included. This data set does not include BWSR Reinvest in Minnesota (RIM) easements, which are collected in a database outside of eLINK. BWSR RIM easement data is provided through a separate dataset on the MnGEO Commons.

This layer shows the Best Management Practices for water pollution control located on West Chester University's campus. | Publication Date: April 2018, Last Updated: April 2018 | West Chester University’s Geography and Planning department upholds its mission to provide spatial analysis expertise in order to solve many problems regarding spatial applications that facilitates research, sustainability goals, planning and communal integration.This dataset was curated by West Chester University’s Department of Geography and Planning and presented using West Chester University's Open GIS Data.

The Arizona Department of Transportation (ADOT) is evaluating the feasibility of establishing a data management office (DMO) to enhance the accessibility, quality, and coordination of its data assets. As data grows in volume and strategic importance, transportation agencies nationwide have adopted formal structures to improve data governance. This study examines national practices related to the organizational and financial structures of DMOs, including roles, staffing models, funding strategies, and implementation challenges. The research also explores the potential benefits of creating leadership positions such as a chief data officer (CDO) and geographic information officer (GIO) within ADOT. Findings support informed decision-making by identifying best practices and key lessons learned to guide the development of a DMO tailored to ADOT’s needs.

Dataset containing all nine metrics for the Utility Management program Best Practice Scores, including data collected by Rural Utility Business Advisor (RUBA).These scores are collected in collaboration with the Department of Environmental Conservation twice a year. Data collection started in 2016 and is currently on-going. Best Practice scores help to determine funding for water utility projects.

Managed PostGIS Services Market Outlook

As per our latest research, the managed PostGIS services market size reached USD 1.45 billion in 2024, reflecting robust adoption across diverse industries. The market is experiencing a strong upward trajectory, with a projected CAGR of 15.7% from 2025 to 2033. By 2033, the market is anticipated to achieve a value of USD 4.65 billion. The primary driver behind this impressive growth is the increasing demand for scalable, cloud-native spatial database solutions that support complex geospatial analytics, particularly as organizations seek to leverage location intelligence for digital transformation.

The growth of the managed PostGIS services market is fundamentally propelled by the exponential rise in geospatial data generation across sectors such as IT, telecommunications, government, and retail. As businesses and public sector agencies increasingly incorporate location-based analytics into their operations, the need for robust, managed spatial databases has become critical. Managed PostGIS services, built on the open-source PostgreSQL framework, offer advanced spatial data management capabilities that are essential for real-time mapping, asset tracking, and geographic information system (GIS) applications. This surge in spatial data is further amplified by the proliferation of IoT devices, mobile applications, and smart city initiatives, all of which require reliable, scalable, and secure geospatial data infrastructure. Organizations are turning to managed services to offload the complexity of maintaining and optimizing PostGIS environments, ensuring high availability and performance while focusing on core business functions.

Another significant growth factor is the rapid advancement and adoption of cloud computing technologies. Cloud-based managed PostGIS services enable organizations to deploy spatial databases with minimal upfront investment, offering flexible scalability and seamless integration with other cloud-native tools. This is particularly advantageous for small and medium enterprises (SMEs) that may lack the resources to maintain sophisticated on-premises infrastructure. The rise of hybrid deployment models, which combine the benefits of both cloud and on-premises solutions, is also contributing to market expansion. These models cater to organizations with stringent data residency, compliance, and security requirements, enabling them to leverage the agility of the cloud while retaining control over sensitive spatial data. As a result, managed PostGIS services are becoming a cornerstone of modern data architectures, supporting advanced analytics, machine learning, and AI-driven insights.

Furthermore, the increasing emphasis on data security, compliance, and disaster recovery is driving organizations to adopt managed PostGIS services. With stringent regulations governing data privacy and protection, especially in sectors like BFSI, healthcare, and government, enterprises are seeking managed service providers that offer robust security frameworks, automated backup and recovery, and comprehensive compliance support. Managed PostGIS services deliver these critical features, ensuring business continuity and mitigating the risk of data breaches or loss. The market is also benefiting from the growing demand for consulting, support, and value-added services, as organizations require specialized expertise to optimize their spatial database environments, migrate legacy systems, and implement best practices for geospatial data management.

Regionally, North America currently holds the largest share of the managed PostGIS services market, driven by widespread adoption across technology-driven industries and strong investment in digital transformation initiatives. Europe follows closely, with significant uptake in government, energy, and utilities sectors. Asia Pacific is emerging as the fastest-growing region, fueled by rapid urbanization, expanding IT infrastructure, and government-backed smart city projects. Latin America and the Middle East & Africa are also witnessing increasing demand, albeit at a slower pace, as organizations in these regions begin to recognize the value of managed geospatial database solutions for operational efficiency and decision-making.

Service Type Analysis

The service type segment of the managed PostGIS services market is highly diversified, encompassing database hosting, database management, consulting & support, backup & reco

This data is represented through point features. This layer identifies but not limited BMP location, and BMP Type.

This data is represented through point features. This layer identifies but not limited BMP location, and BMP Type.

The data is maintained by Department of Public Works, Division of Environmental Stormwater Management.

This dataset is available using the link : https://norfolkgisdata-orf.opendata.arcgis.com/datasets/267648babe274a7bb92f1253d54271d6_3/about

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

NLEAP GIS 5.0 can help users identify hot spots across the landscape and identify management practices that can increase nitrogen use efficiency. A Nitrogen Trading Tool (NTT) analysis can be conducted to determine the potential benefits of implementing best management practices and the quantity of nitrogen savings that could potentially be traded in future air or water quality markets. Resources in this dataset:Resource Title: NLEAP GIS 5.0. File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=428&modecode=30-12-30-15 download page

The GIS Asset Management Software market has emerged as a critical component for organizations aiming to optimize their asset management processes through Geographic Information System (GIS) technology. By leveraging powerful mapping and spatial analysis tools, businesses across various industries, including utiliti

This layer presents the best-known point and perimeter locations of wildfire occurrences within the United States over the past 7 days. Points mark a location within the wildfire area and provide current information about that wildfire. Perimeters are the line surrounding land that has been impacted by a wildfire.Consumption Best Practices:

As a service that is subject to very high usage, ensure peak performance and accessibility of your maps and apps by avoiding the use of non-cacheable relative Date/Time field filters. To accommodate filtering events by Date/Time, we suggest using the included "Age" fields that maintain the number of days or hours since a record was created or last modified, compared to the last service update. These queries fully support the ability to cache a response, allowing common query results to be efficiently provided to users in a high demand service environment. When ingesting this service in your applications, avoid using POST requests whenever possible. These requests can compromise performance and scalability during periods of high usage because they too are not cacheable.Source:  Wildfire points are sourced from Integrated Reporting of Wildland-Fire Information (IRWIN) and perimeters from National Interagency Fire Center (NIFC). Current Incidents: This layer provides a near real-time view of the data being shared through the Integrated Reporting of Wildland-Fire Information (IRWIN) service. IRWIN provides data exchange capabilities between participating wildfire systems, including federal, state and local agencies. Data is synchronized across participating organizations to make sure the most current information is available. The display of the points are based on the NWCG Fire Size Classification applied to the daily acres attribute.Current Perimeters: This layer displays fire perimeters posted to the National Incident Feature Service. It is updated from operational data and may not reflect current conditions on the ground. For a better understanding of the workflows involved in mapping and sharing fire perimeter data, see the National Wildfire Coordinating Group Standards for Geospatial Operations.Update Frequency:  Every 15 minutes using the Aggregated Live Feed Methodology based on the following filters:Events modified in the last 7 daysEvents that are not given a Fire Out DateIncident Type Kind: FiresIncident Type Category: Prescribed Fire, Wildfire, and Incident Complex

Area Covered: United StatesWhat can I do with this layer? The data includes basic wildfire information, such as location, size, environmental conditions, and resource summaries. Features can be filtered by incident name, size, or date keeping in mind that not all perimeters are fully attributed.Attribute InformationThis is a list of attributes that benefit from additional explanation. Not all attributes are listed.Incident Type Category: This is a breakdown of events into more specific categories.Wildfire (WF) -A wildland fire originating from an unplanned ignition, such as lightning, volcanos, unauthorized and accidental human caused fires, and prescribed fires that are declared wildfires.Prescribed Fire (RX) - A wildland fire originating from a planned ignition in accordance with applicable laws, policies, and regulations to meet specific objectives.Incident Complex (CX) - An incident complex is two or more individual incidents in the same general proximity that are managed together under one Incident Management Team. This allows resources to be used across the complex rather than on individual incidents uniting operational activities.IrwinID: Unique identifier assigned to each incident record in both point and perimeter layers.

Acres: these typically refer to the number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.Discovery: An estimate of acres burning upon the discovery of the fire.Calculated or GIS:  A measure of acres calculated (i.e., infrared) from a geospatial perimeter of a fire.Daily: A measure of acres reported for a fire.Final: The measure of acres within the final perimeter of a fire. More specifically, the number of acres within the final fire perimeter of a specific, individual incident, including unburned and unburnable islands.

Dates: the various systems contribute date information differently so not all fields will be populated for every fire.FireDiscovery: The date and time a fire was reported as discovered or confirmed to exist. May also be the start date for reporting purposes.

Containment: The date and time a wildfire was declared contained. Control: The date and time a wildfire was declared under control.ICS209Report: The date and time of the latest approved ICS-209 report.Current: The date and time a perimeter is last known to be updated.FireOut: The date and time when a fire is declared out.ModifiedOnAge: (Integer) Computed days since event last modified.DiscoveryAge: (Integer) Computed days since event's fire discovery date.CurrentDateAge: (Integer) Computed days since perimeter last modified.CreateDateAge: (Integer) Computed days since perimeter entry created.

GACC: A code that identifies one of the wildland fire geographic area coordination centers. A geographic area coordination center is a facility that is used for the coordination of agency or jurisdictional resources in support of one or more incidents within a geographic coordination area.Fire Mgmt Complexity: The highest management level utilized to manage a wildland fire event.Incident Management Organization: The incident management organization for the incident, which may be a Type 1, 2, or 3 Incident Management Team (IMT), a Unified Command, a Unified Command with an IMT, National Incident Management Organization (NIMO), etc. This field is null if no team is assigned.Unique Fire Identifier: Unique identifier assigned to each wildland fire. yyyy = calendar year, SSUUUU = Point Of Origin (POO) protecting unit identifier (5 or 6 characters), xxxxxx = local incident identifier (6 to 10 characters)RevisionsJan 4, 2021: Added Integer fields 'Days Since...' to Current_Incidents point layer and Current_Perimeters polygon layer. These fields are computed when the data is updated, reflecting the current number of days since each record was last updated. This will aid in making 'age' related, cache friendly queries.Mar 12, 2021: Added second set of 'Age' fields for Event and Perimeter record creation, reflecting age in Days since service data update.Apr 21, 2021: Current_Perimeters polygon layer is now being populated by NIFC's newest data source. A new field was added, 'IncidentTypeCategory' to better distinguish Incident types for Perimeters and now includes type 'CX' or Complex Fires. Five fields were not transferrable, and as a result 'Comments', 'Label', 'ComplexName', 'ComplexID', and 'IMTName' fields will be Null moving forward.Apr 26, 2021: Updated Incident Layer Symbology to better clarify events, reduce download size and overhead of symbols. Updated Perimeter Layer Symbology to better distingish between Wildfires and Prescribed Fires.May 5, 2021: Slight modification to Arcade logic for Symbology, refining Age comparison to Zero for fires in past 24-hours.Aug 16, 2021: Enabled Time Series capability on Layers (off by default) using 'Fire Discovery Date' for Incidents and 'Creation Date' for Perimeters.This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

MassGIS is working very closely with the State 911 Department in the state’s Executive Office of Public Safety and Security on the Next Generation 911 Emergency Call System. MassGIS developed and is maintaining the map and address information that are at the heart of this new system. Statewide deployment of this new 9-1-1 call routing system was completed in 2018.Address sources include the Voter Registration List from the Secretary of the Commonwealth, site addresses from municipal departments (primarily assessors), and customer address lists from utilities. Addresses from utilities were “anonymized” to protect customer privacy. The MAD was also validated for completeness using the Emergency Service List (a list of telephone land line addresses) from Verizon.The MAD contains both tabular and spatial data, with addresses being mapped as point features. At present, the MAD contains 3.2 million address records and 2.2 million address points. As the database is very dynamic with changes being made daily, the data available for download will be refreshed weekly.A Statewide Addressing Standard for Municipalities is another useful asset that has been created as part of this ongoing project. It is a best practices guide for the creation and storage of addresses for Massachusetts Municipalities.Points features with each point having an address to the building/floor/unit level, when that information is available. Where more than one address is located at a single location multiple points are included (i.e. "stacked points"). The points for the most part represent building centroids. Other points are located as assessor parcel centroids.Points will display at scales 1:75,000 and closer.MassGIS' service does not contain points for Boston; they may be accessed at https://data.boston.gov/dataset/live-street-address-management-sam-addresses/resource/873a7659-68b6-4ac0-98b7-6d8af762b6f1.More details about the MAD and Master Address Points.Feature service also available.

The 2020 and 2021 results of the SWAMP eDNA Metabarcoding Monitoring and Analysis Project (SeMMAP) sampling efforts using Jonah Ventures aquatic eDNA kits and sequencing. Included are the locations, field measures and MiFish and 23S sequencing results of eDNA sampling for fish and phytoplankton communities respectively. 45 samples were taken between June 2020 and October of 2021.SeMMAP was created to explore the use of eDNA metabarcoding for surface water quality monitoring and how this method may achieve our main goals of monitoring more with less resources and integrating SWAMP programs through a single data source. We will be exploring a data management plan as well as the benefits of using volunteer and Tribal partners to collect the same quality data as our internal SWAMP programs. Our aim is to bring inclusion to the Water Boards by putting monitoring into the hands of those who have been marginalized by the Boards and other government agencies.This map is part of a larger engagement tool, the SeMMAP Portal which will serve as a site for external partners, regional partners, SWAMP program personnel and the public to view the pace and direction of the project and the collected data. The SeMMAP Portal contains data dashboards, maps, a partner project gallery for partners to view how each is using their eDNA to answer scientific questions and monitor water quality. The Portal also includes guides and instructions on best practices as well as the raw data and metadata.This content was created by Peter Houpt from the State Water Resources Control Boards, SWAMP eDNA Metabarcoding Monitoring and Analysis Project.

peter.houpt@waterboards.ca.gov oima-semmap@waterboards.ca.gov

The Prioritize Target Measure Application (PTMApp) is a geospatial watershed model that uses land use data, lidar derived hydro conditioned elevation models, soils, and travel time grids to develop source assessments of the fate and transport of sediment, phosphorus, and nitrogen across agriculture watersheds. This model also predicts appropriate geospatial locations for the placement of agricultural best management practices (BMPs) based on United State Department of Agriculture Natural Resource Conservation Service (USDA-NRCS) practice types as outlined in the Field Office Technical Guide (FOTG). The model also applies economic information to the practices to determine the overall cost effectiveness of implementing specific BMPs. The model is a toolbar add in that utilizes the ESRI Arc 10.X and Arc Pro software platforms and can be downloaded HERE.

The data outputs from this model can be used at multiple scales and has been used for Comprehensive Watershed Management Plans at the major watershed scale down to individual practice effectiveness at the field scale. Modeled geospatial data has been made available to the public through the deployment of the PTMApp web application, which is hosted by MnGEO at THIS SITE. In addition, technical documentation and training materials for PTMApp can be found at the Minnesota Board of Water and Soil Resources (BWSR) website HERE, along with a Story Map that provides an descriptive overview.

Map of Watersheds with Completed PTMApp Analysis: MAP

The Bureau of Indian Affairs (BIA) Bison Project will serve a variety of purposes that are designed to uphold the best of Tribal bison herd expansion interests, including a focus on ecosystem restoration through bison conservation. The Bison Project will foster practices that are traditional and culturally attentive to the historical coevolved relationship with bison to support the Tribe’s own self-determined well-being. Furthermore, the projects will work to foster the intent of the Department of the Interior Secretary’s Order 3410, the purpose of which is to restore wild and healthy populations of American bison and the prairie grassland ecosystem through collaboration among the Department’s Bureaus and partners such as other Federal agencies, states, Tribes, and landowners using the best available science and Indigenous Knowledge. The analysis in this dashboard is based on data provided to the BIA from a multitude of resources including but not limited to the BIA, Federally Recognized Tribes, and partner organizations. All data was current as of time of collection for this project, data will be updated as determined by the BIA. Data displayed within this application can vary at different times of the year as external factors may affect herd sizes and will not reflect changes in real time. Herd numbers can also decrease between data updates due to range management practices performed on the local level, not managed by the BIA. The Bison Program Application’s data is made up of a polygon feature layer and a point feature layer hosted on the BIA online portal. The Bison Polygon layer features the geospatial extent of known Bison ranches as provided to the BIA. Tribes without any GIS data on ranch boundaries will only be featured in the Bison points feature layer. Both feature layers contain data including, name of Tribe, herd size, rangeland acres, and a link to their Bison website (if available). The Bison Program Application will focus on ecosystem restoration through bison conservation and expansion and improved management of existing herds on Tribal trust lands, individual Indian allotment lands, or in areas managed by Tribes through treaties or agreements. The Bison Project will focus on bison conservation and expansion and improved management of existing herds on Tribal trust resources and describe the role of Tribal bison on ecosystem restoration on Tribal landscapes and altered Tribal environmental conditions. This can cover bison as indicator keystone species on agricultural pasture, grassland, and rangeland settings. The Bison Program Application also features data from partner organizations who focus on promoting the restoration of Bison. These organizations include The Nature Conservancy (TNC). For more information on data contributors, follow the links below. The feature layers used in this application from partner organizations are not managed by the BIA. The Nature Conservancy (TNC): https://www.nature.org/en-us/. Disclaimer: The analysis in this dashboard is based on the analysis of available data provided to the BIA from a multitude of resources including but not limited to the BIA, Federally Recognized Tribes, and partner organizations. All data was current as of time of collection for this project, data will be updated as determined by the BIA. Data displayed within this application can vary at different times of the year as external factors may affect available foliage due to weather or other uncontrollable circumstances. The number of Bison within herds may also change throughout the year and might not be accounted for within this application. Herd numbers can also decrease between data updates due to outside factors or range management practices performed on the local level, not managed by the BIA.This application also uses data provided from other sources such as The Nature Conservancy (TNC). This data is owned and maintained by their respective owners. These data sources have been developed from the best available sources. Although efforts have been made to ensure that the data are accurate and reliable, errors and variable conditions originating from source documents and/or the translation of information from source documents to the systems of record continue to exist. Users must be aware of these conditions and bear responsibility for the appropriate use of the information with respect to possible errors, scale, resolution, rectification, positional accuracy, development methodology, time period, environmental and climatic conditions and other circumstances specific to these data. The user is responsible for understanding the accuracy limitations of the data provided herein. The burden for determining fitness for use lies entirely with the user. The user should refer to the accompanying metadata notes for a description of the data and data development procedures.

This layer contains the data for the stormwater Best Management Practices (BMP) in the City of Round Rock, located in Williamson County, Texas. This layer is part of an original dataset provided and maintained by the City of Round Rock GIS/IT Department. The data in this layer are represented as points.Stormwater management includes controlling flooding, reducing erosion and improving water quality. This can be accomplished by implementing what are known as Best Management Practices (BMP). Best Management Practices are structural, vegetative or managerial practices used to treat, prevent, or reduce water pollution.

Nitrogen and phosphorus losses from agricultural areas have impacted the water quality of downstream rivers, lakes, and oceans. As a result, investment in the adoption of agricultural best management practices (BMPs) has grown but assessments of their effectiveness at large spatial scales have been sparse. This study applies regional Spatially Referenced Regression On Watershed-attributes (SPARROW) models developed for the Midwest, Northeast, and Southeast regions of the United States to quantify regional effects of BMPs on nutrient losses from agricultural lands. These models were used because they account for specific BMPs in the prediction of instream nutrient loads. This data release accompanies the journal article "Quantifying regional effects of best management practices on nutrient losses from agricultural lands" (https:// doi:10.5066/pending), and it contains the input and output data for the modeling scenarios that were evaluated relative to the 2012 regional SPARROW mode ...

This layer presents the best-known point and perimeter locations of wildfire occurrences within the United States over the past 7 days. Points mark a location within the wildfire area and provide current information about that wildfire. Perimeters are the line surrounding land that has been impacted by a wildfire.Consumption Best Practices:

As a service that is subject to very high usage, ensure peak performance and accessibility of your maps and apps by avoiding the use of non-cacheable relative Date/Time field filters. To accommodate filtering events by Date/Time, we suggest using the included "Age" fields that maintain the number of days or hours since a record was created or last modified, compared to the last service update. These queries fully support the ability to cache a response, allowing common query results to be efficiently provided to users in a high demand service environment. When ingesting this service in your applications, avoid using POST requests whenever possible. These requests can compromise performance and scalability during periods of high usage because they too are not cacheable.Source:  Wildfire points are sourced from Integrated Reporting of Wildland-Fire Information (IRWIN) and perimeters from National Interagency Fire Center (NIFC). Current Incidents: This layer provides a near real-time view of the data being shared through the Integrated Reporting of Wildland-Fire Information (IRWIN) service. IRWIN provides data exchange capabilities between participating wildfire systems, including federal, state and local agencies. Data is synchronized across participating organizations to make sure the most current information is available. The display of the points are based on the NWCG Fire Size Classification applied to the daily acres attribute.Current Perimeters: This layer displays fire perimeters posted to the National Incident Feature Service. It is updated from operational data and may not reflect current conditions on the ground. For a better understanding of the workflows involved in mapping and sharing fire perimeter data, see the National Wildfire Coordinating Group Standards for Geospatial Operations.Update Frequency:  Every 15 minutes using the Aggregated Live Feed Methodology based on the following filters:Events modified in the last 7 daysEvents that are not given a Fire Out DateIncident Type Kind: FiresIncident Type Category: Prescribed Fire, Wildfire, and Incident Complex

Area Covered: United StatesWhat can I do with this layer? The data includes basic wildfire information, such as location, size, environmental conditions, and resource summaries. Features can be filtered by incident name, size, or date keeping in mind that not all perimeters are fully attributed.Attribute InformationThis is a list of attributes that benefit from additional explanation. Not all attributes are listed.Incident Type Category: This is a breakdown of events into more specific categories.Wildfire (WF) -A wildland fire originating from an unplanned ignition, such as lightning, volcanos, unauthorized and accidental human caused fires, and prescribed fires that are declared wildfires.Prescribed Fire (RX) - A wildland fire originating from a planned ignition in accordance with applicable laws, policies, and regulations to meet specific objectives.Incident Complex (CX) - An incident complex is two or more individual incidents in the same general proximity that are managed together under one Incident Management Team. This allows resources to be used across the complex rather than on individual incidents uniting operational activities.IrwinID: Unique identifier assigned to each incident record in both point and perimeter layers.

Acres: these typically refer to the number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.Discovery: An estimate of acres burning upon the discovery of the fire.Calculated or GIS:  A measure of acres calculated (i.e., infrared) from a geospatial perimeter of a fire.Daily: A measure of acres reported for a fire.Final: The measure of acres within the final perimeter of a fire. More specifically, the number of acres within the final fire perimeter of a specific, individual incident, including unburned and unburnable islands.

Dates: the various systems contribute date information differently so not all fields will be populated for every fire.FireDiscovery: The date and time a fire was reported as discovered or confirmed to exist. May also be the start date for reporting purposes.

Containment: The date and time a wildfire was declared contained. Control: The date and time a wildfire was declared under control.ICS209Report: The date and time of the latest approved ICS-209 report.Current: The date and time a perimeter is last known to be updated.FireOut: The date and time when a fire is declared out.ModifiedOnAge: (Integer) Computed days since event last modified.DiscoveryAge: (Integer) Computed days since event's fire discovery date.CurrentDateAge: (Integer) Computed days since perimeter last modified.CreateDateAge: (Integer) Computed days since perimeter entry created.

GACC: A code that identifies one of the wildland fire geographic area coordination centers. A geographic area coordination center is a facility that is used for the coordination of agency or jurisdictional resources in support of one or more incidents within a geographic coordination area.Fire Mgmt Complexity: The highest management level utilized to manage a wildland fire event.Incident Management Organization: The incident management organization for the incident, which may be a Type 1, 2, or 3 Incident Management Team (IMT), a Unified Command, a Unified Command with an IMT, National Incident Management Organization (NIMO), etc. This field is null if no team is assigned.Unique Fire Identifier: Unique identifier assigned to each wildland fire. yyyy = calendar year, SSUUUU = Point Of Origin (POO) protecting unit identifier (5 or 6 characters), xxxxxx = local incident identifier (6 to 10 characters)RevisionsJan 4, 2021: Added Integer fields 'Days Since...' to Current_Incidents point layer and Current_Perimeters polygon layer. These fields are computed when the data is updated, reflecting the current number of days since each record was last updated. This will aid in making 'age' related, cache friendly queries.Mar 12, 2021: Added second set of 'Age' fields for Event and Perimeter record creation, reflecting age in Days since service data update.Apr 21, 2021: Current_Perimeters polygon layer is now being populated by NIFC's newest data source. A new field was added, 'IncidentTypeCategory' to better distinguish Incident types for Perimeters and now includes type 'CX' or Complex Fires. Five fields were not transferrable, and as a result 'Comments', 'Label', 'ComplexName', 'ComplexID', and 'IMTName' fields will be Null moving forward.Apr 26, 2021: Updated Incident Layer Symbology to better clarify events, reduce download size and overhead of symbols. Updated Perimeter Layer Symbology to better distingish between Wildfires and Prescribed Fires.May 5, 2021: Slight modification to Arcade logic for Symbology, refining Age comparison to Zero for fires in past 24-hours.Aug 16, 2021: Enabled Time Series capability on Layers (off by default) using 'Fire Discovery Date' for Incidents and 'Creation Date' for Perimeters.This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

Abstract

The Global Data Regulation Diagnostic provides a comprehensive assessment of the quality of the data governance environment. Diagnostic results show that countries have put in greater effort in adopting enabler regulatory practices than in safeguard regulatory practices. However, for public intent data, enablers for private intent data, safeguards for personal and nonpersonal data, cybersecurity and cybercrime, as well as cross-border data flows. Across all these dimensions, no income group demonstrates advanced regulatory frameworks across all dimensions, indicating significant room for the regulatory development of both enablers and safeguards remains at an intermediate stage: 47 percent of enabler good practices and 41 percent of good safeguard practices are adopted across countries. Under the enabler and safeguard pillars, the diagnostic covers dimensions of e-commerce/e-transactions, enablers further improvement on data governance environment.

The Global Data Regulation Diagnostic is the first comprehensive assessment of laws and regulations on data governance. It covers enabler and safeguard regulatory practices in 80 countries providing indicators to assess and compare their performance. This Global Data Regulation Diagnostic develops objective and standardized indicators to measure the regulatory environment for the data economy across countries. The indicators aim to serve as a diagnostic tool so countries can assess and compare their performance vis-á-vis other countries. Understanding the gap with global regulatory good practices is a necessary first step for governments when identifying and prioritizing reforms.

Geographic coverage

80 countries

Analysis unit

Country

Kind of data

Observation data/ratings [obs]

Sampling procedure

The diagnostic is based on a detailed assessment of domestic laws, regulations, and administrative requirements in 80 countries selected to ensure a balanced coverage across income groups, regions, and different levels of digital technology development. Data are further verified through a detailed desk research of legal texts, reflecting the regulatory status of each country as of June 1, 2020.

Mode of data collection

Mail Questionnaire [mail]

Research instrument

The questionnaire comprises 37 questions designed to determine if a country has adopted good regulatory practice on data governance. The responses are then scored and assigned a normative interpretation. Related questions fall into seven clusters so that when the scores are averaged, each cluster provides an overall sense of how it performs in its corresponding regulatory and legal dimensions. These seven dimensions are: (1) E-commerce/e-transaction; (2) Enablers for public intent data; (3) Enablers for private intent data; (4) Safeguards for personal data; (5) Safeguards for nonpersonal data; (6) Cybersecurity and cybercrime; (7) Cross-border data transfers.

Response rate

100%