Web Soil Survey & Geospatial Data Gateway These requirements include:Provide a way to request data for an adhoc area of interest of any size.Provide a way to obtain data in real-time.Provide a way to request selected tabular and spatial attributes.Provide a way to return tabular and spatial data where the organization of that data doesn't hate to mirror that of the underlying source database.Provide a way to bundle results by request, rather tan by survey area.Click on Submit a custom request for soil tabular data, to input a query to extract data. For help click on:Creating my own custom database queries Index to SQL Library - Sample Scripts Using Soil Data Access website Using Soil Data Access web services

URL: https://geoscience.data.qld.gov.au/dataset/ds000001

This practice direction forms part of the Queensland Resources Reporting Lodgement, GSQ Open Data Portal, Reporting Guideline 2020 to assist industry with spatial data lodgement.

The purpose is to outline in greater detail, formatting and content requirements to ensure the standardisation of spatial information and provide consistency of submissions received, enabling the department to more effectively standardise, process and integrate the data.

Submitted data must meet the standards for content and file formats as set out in this document. Lodgement will be via use of submission templates, designed to guide the user in assembling data, and to ensure consistency with the defined data formats and standards.

This practice direction has been divided into two sections covering the submission of spatial information as digital spatial data and digital maps.

The dataset provides the usage statistics (covering both the number of downloads and the number of API requests) of open data (spatial data included) of the Open Data Portal per data provider in a specific time period

This dataset contains the White Mountain National Forest Boundary. The boundary was extracted from the National Forest boundaries coverage for the lower 48 states, including Puerto Rico developed by the USDA Forest Service - Geospatial Service and Technology Center. The coverage was projected from decimal degrees to UTM zone 19. This dataset includes administrative unit boundaries, derived primarily from the GSTC SOC data system, comprised of Cartographic Feature Files (CFFs), using ESRI Spatial Data Engine (SDE) and an Oracle database. The data that was available in SOC was extracted on November 10, 1999. Some of the data that had been entered into SOC was outdated, and some national forest boundaries had never been entered for a variety of reasons. The USDA Forest Service, Geospatial Service and Technology Center has edited this data in places where it was questionable or missing, to match the National Forest Inventoried Roadless Area data submitted for the President's Roadless Area Initiative. Data distributed as shapefile in Coordinate system EPSG:26919 - NAD83 / UTM zone 19N.

The passage of the Sustainable Groundwater Management Act (SGMA) in 2014 set forth a statewide framework to help protect groundwater resources over the long-term. SGMA requires local agencies to form groundwater sustainability agencies (GSAs) for the high and medium priority basins. GSAs develop and implement groundwater sustainability plans (GSPs) to avoid undesirable results and mitigate overdraft within 20 years. GSP boundaries are managed through the SGMA Portal. This dataset represents the current GSP boundaries and includes attributes such as GSP manager contacts and GSP status. The GSP submission process is completed via the SGMA Portal and includes two primary steps: 1) a GSA(s) submits a GSP, and all associated documents and data, to the SGMA Portal’s GSP Reporting System; and 2) the Department conducts an acceptance review and posts accepted GSPs to the SGMA Portal. A single GSP, or multiple GSPs with a Coordination Agreement, must cover an entire basin or subbasin, and can only be submitted once adopted by the underlying GSA(s). GSPs are submitted in the format required by 23 CCR §353.2 and 23 CCR §353.4 of the GSP Regulations and includes the shapefiles associated with this dataset. The Department of Water Resources (DWR) must complete the acceptance review within 20-days following the submission of a GSP. During the acceptance review, DWR verifies that the GSP submission demonstrates that the GSP was adopted, covers the entire basin or subbasin, and contains all required documents and data. If DWR accepts the submitted GSP, it is then posted to the SGMA Portal, and then a public comment period is opened.

A polygon features representing Alexandria Police reporting Districts within the City of Alexandria.

County SubdivisionsThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Census Bureau (USCB), displays the boundaries of all county subdivisions within the United States. Per the USCB, "county subdivisions are the primary divisions of counties and their equivalent entities for the reporting of Census Bureau data. They include legally-recognized minor civil divisions (MCDs) and statistical census county divisions (CCDs), and unorganized territories. The entire area of the United States, Puerto Rico, and the Island Areas are covered by county subdivisions."Asbury Park (City), Brookhaven (Town) & Queens (Borough)Data currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (County Subdivisions) and will support mapping, analysis, data exports and OGC API – Feature access.NGDAID: 77 (Series Information for County Subdivision State-based TIGER/Line Shapefiles, Current)OGC API Features Link: (County Subdivisions - OGC Features) copy this link to embed it in OGC Compliant viewersFor more information, please visit: County SubdivisionsFor feedback, please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Governmental Units, and Administrative and Statistical Boundaries Theme Community. Per the Federal Geospatial Data Committee (FGDC), this theme is defined as the "boundaries that delineate geographic areas for uses such as governance and the general provision of services (e.g., states, American Indian reservations, counties, cities, towns, etc.), administration and/or for a specific purpose (e.g., congressional districts, school districts, fire districts, Alaska Native Regional Corporations, etc.), and/or provision of statistical data (census tracts, census blocks, metropolitan and micropolitan statistical areas, etc.). Boundaries for these various types of geographic areas are either defined through a documented legal description or through criteria and guidelines. Other boundaries may include international limits, those of federal land ownership, the extent of administrative regions for various federal agencies, as well as the jurisdictional offshore limits of U.S. sovereignty. Boundaries associated solely with natural resources and/or cultural entities are excluded from this theme and are included in the appropriate subject themes."For other NGDA Content: Esri Federal Datasets

Address points represent all sites and structures with an assigned street address. The Address Points layer is strongly recommended in the NENA standard, but it is required in the Kansas NG911 GIS Data Model.At minimum, there is a point on every addressable single-unit building, a point on each living unit/occupancy of every multi-unit building or complex, and a point for every telephone service address in the TN listing. This data is updated quarterly.Please refer to the Kansas NG911 GIS Data Model for more details: Kansas NG9-1-1 GIS Data Model (kansasgis.org)Some Kansas PSAP GIS Data Stewards prefer to handle their address point GIS data distribution on a per request basis. If you would like to request address points for an area not included in this public dataset, you can do so by registering for an account and submitting a GIS Data Request on the Kansas NG911 Program Portal (https://portal.kansas911.org/).

The Skeena Salmon Data Centre was developed by the Skeena Knowledge Trust to become a comprehensive, central repository for credible information related to wild Pacific salmonids in the Skeena watershed, including data on salmon populations, habitat, water quality, hydrology, and climatology. The purpose of the Skeena Knowledge Trust is to provide public eduction on the Skeena wild salmon populations, their genetic diversity, and their ocean and freshwater habitats in order to inform and enable the assessment and implementation of resource development proposals and government policy, including First Nations' land-use plans, provincial land-use plans, and the federal Wild Salmon Policy. We are a purpose trust and charity; if you value our services and would like to support our activities, please visit skeenatrust.ca to make a donation. Spatial data on the Skeena Salmon Data Centre is also housed on the Skeena Maps Portal, an interactive mapping portal and repository for geospatial information related to Skeena River wild salmon. The portal includes geospatial data layers and complete maps on themes related to Skeena River wild salmon, such as watersheds, stream crossings, and roads. The portal allows users to view and download data layers in standard formats and to view and download complete maps, as well as create custom maps. The Skeena Salmon Data Centre uses a Comprehensive Knowledge Archive Network (CKAN) management system, which is a web-based open source management system for the storage and distribution of open data. CKAN is a powerful data catalogue system, and is being used by many public institutions, including the Government of British Columbia, Government of Alberta, and the City of Surrey. Information on how to use the Skeena Salmon Data Centre is available on our Help Pages. The Skeena Knowledge Trust maintains and populates the Skeena Salmon Data Centre with datasets and reports using a prioritization process described by its Annual Knowledge Plan. To find out more information on contributing to the Skeena Salmon Data Centre or Skeena Maps Portal, or to provide feedback or suggestions, contact us at info@skeenatrust.ca. Do you have resources or datasets you would like to contribute to the Skeena Salmon Data Centre or Skeena Maps Portal? Complete the appropriate metadata submission form and send us an email, we would love to hear from you!

The National Pollutant Release Inventory (NPRI) is Canada's public inventory of pollutant releases (to air, water and land), disposals and transfers for recycling. The files below contain a map of Canada showing the locations of all facilities that reported to the NPRI in the most recent reporting year. The map is available in both ESRI REST (to use with ARC GIS) and WMS (open source) formats. For more information about the individual reporting facilities, datasets are available in either CSV or XLS formats. Please consult the following resources to enhance your analysis: - Guide on using and Interpreting NPRI Data: https://www.canada.ca/en/environment-climate-change/services/national-pollutant-release-inventory/using-interpreting-data.html - Access additional data from the NPRI, including datasets and mapping products: https://www.canada.ca/en/environment-climate-change/services/national-pollutant-release-inventory/tools-resources-data/exploredata.html

A Graph Convolutional Neural Network-based Method for Predicting Computational Intensity of GeocomputationThis is the implementation for the paper "A Graph Convolutional Neural Network-based Method for Predicting Computational Intensity of Geocomputation".The framework is Learning-based Computing Framework for Geospatial data(LCF-G).Prediction, ParallelComputation and SampleGeneration.This paper includes three case studies, each corresponding to a folder. Each folder contains four subfolders: data, CIThe data folder contains geospatail data.The CIPrediction folder contains model training code.The ParallelComputation folder contains geographic computation code.The SampleGeneration folder contains code for sample generation.Case 1: Generation of DEM from point cloud datastep 1: Data downloadDataset 1 has been uploaded to the directory 1point2dem/data. The other two datasets, Dataset 2 and Dataset 3, can be downloaded from the following website:OpenTopographyBelow are the steps for downloading Dataset 2 and Dataset 3, along with the query parameters:Dataset 2:Visit OpenTopography Website: Go to Dataset 2 Download Link.https://portal.opentopography.org/lidarDataset?opentopoID=OTLAS.112018.2193.1Coordinates & Classification:In the section "1. Coordinates & Classification", select the option "Manually enter selection coordinates".Set the coordinates as follows: Xmin = 1372495.692761,Ymin = 5076006.86821,Xmax = 1378779.529766,Ymax = 5085586.39531Point Cloud Data Download:Under section "2. Point Cloud Data Download", choose the option "Point cloud data in LAS format".Submit:Click on "SUBMIT" to initiate the download.Dataset 3:Visit OpenTopography Website:Go to Dataset 3 Download Link: https://portal.opentopography.org/lidarDataset?opentopoID=OTLAS.052016.26912.1Coordinates & Classification:In the section "1. Coordinates & Classification", select the option "Manually enter selection coordinates".Set the coordinates as follows:Xmin = 470047.153826,Ymin = 4963418.512121,Xmax = 479547.16556,Ymax = 4972078.92768Point Cloud Data Download:Under section "2. Point Cloud Data Download", choose the option "Point cloud data in LAS format".Submit:Click on "SUBMIT" to initiate the download.step 2: Sample generationThis step involves data preparation, and samples can be generated using the provided code. Since the samples have already been uploaded to 1point2dem/SampleGeneration/data, this step is optional.cd 1point2dem/SampleGenerationg++ PointCloud2DEMSampleGeneration.cpp -o PointCloud2DEMSampleGenerationmpiexec -n {number_processes} ./PointCloud2DEMSampleGeneration ../data/pcd path/to/outputstep 3: Model trainingThis step involves training three models (GAN, CNN, GAT). The model results are saved in 1point2dem/SampleGeneration/result, and the results for Table 3 in the paper are derived from this output.cd 1point2dem/CIPredictionpython -u point_prediction.py --model [GCN|ChebNet|GATNet]step 4: Parallel computationThis step uses the trained models to optimize parallel computation. The results for Figures 11-13 in the paper are generated from the output of this command.cd 1point2dem/ParallelComputationg++ ParallelPointCloud2DEM.cpp -o ParallelPointCloud2DEMmpiexec -n {number_processes} ./ParallelPointCloud2DEM ../data/pcdCase 2: Spatial intersection of vector datastep 1: Data downloadSome data from the paper has been uploaded to 2intersection/data. The remaining OSM data can be downloaded from GeoFabrik. Below are the download steps and parameters:Directly click the following link to download the OSM data: GeoFabrik - Czech Republic OSM Datastep 2: Sample generationThis step involves data preparation, and samples can be generated using the provided code. Since the samples have already been uploaded to 2intersection/SampleGeneration/data, this step is optional.cd 2intersection/SampleGenerationg++ ParallelIntersection.cpp -o ParallelIntersectionmpiexec -n {number_processes} ./ParallelIntersection ../data/shpfile ../data/shpfilestep 3: Model trainingThis step involves training three models (GAN, CNN, GAT). The model results are saved in 2intersection/SampleGeneration/result, and the results for Table 5 in the paper are derived from this output.cd 2intersection/CIPredictionpython -u vector_prediction.py --model [GCN|ChebNet|GATNet]step 4: Parallel computationThis step uses the trained models to optimize parallel computation. The results for Figures 14-16 in the paper are generated from the output of this command.cd 2intersection/ParallelComputationg++ ParallelIntersection.cpp -o ParallelIntersectionmpiexec -n {number_processes} ./ParallelIntersection ../data/shpfile1 ../data/shpfile2Case 3: WOfS analysis using raster datastep 1: Data downloadSome data from the paper has been uploaded to 3wofs/data. The remaining data can be downloaded from http://openge.org.cn/advancedRetrieval?type=dataset. Below are the query parameters:Product Selection: Select LC08_L1TP and LC08_L1GTLatitude and Longitude Selection:Minimum Longitude: 112.5,Maximum Longitude: 115.5, Minimum Latitude: 29.5, Maximum Latitude: 31.5Time Range: 2013-01-01 to 2018-12-31Other parameters: Defaultstep 2: Sample generationThis step involves data preparation, and samples can be generated using the provided code. Since the samples have already been uploaded to 3wofs/SampleGeneration/data, this step is optional.cd 3wofs/SampleGenerationsbt packeagespark-submit --master {host1,host2,host3} --class whu.edu.cn.core.cube.raster.WOfSSampleGeneration path/to/package.jarstep 3: Model trainingThis step involves training three models (GAN, CNN, GAT). The model results are saved in 3wofs/SampleGeneration/result, and the results for Table 6 in the paper are derived from this output.cd 3wofs/CIPredictionpython -u raster_prediction.py --model [GCN|ChebNet|GATNet]step 4: Parallel computationThis step uses the trained models to optimize parallel computation. The results for Figures 18, 19 in the paper are generated from the output of this command.cd 3wofs/ParallelComputationsbt packeagespark-submit --master {host1,host2,host3} --class whu.edu.cn.core.cube.raster.WOfSOptimizedByDL path/to/package.jar path/to/outputStatement about Case 3The experiment Case 3 presented in this paper was conducted with improvements made on the GeoCube platform.Code Name: GeoCubeCode Link: GeoCube Source CodeLicense Information: The GeoCube project is openly available under the CC BY 4.0 license.The GeoCube project is licensed under CC BY 4.0, which is the Creative Commons Attribution 4.0 International License, allowing anyone to freely share, modify, and distribute the platform's code.Citation:Gao, Fan (2022). A multi-source spatio-temporal data cube for large-scale geospatial analysis. figshare. Software. https://doi.org/10.6084/m9.figshare.15032847.v1Clarification Statement:The authors of this code are not affiliated with this manuscript. The innovations and steps in Case 3, including data download, sample generation, and parallel computation optimization, were independently developed and are not dependent on the GeoCube’s code.RequirementsThe codes use the following dependencies with Python 3.8torch==2.0.0torch_geometric==2.5.3networkx==2.6.3pyshp==2.3.1tensorrt==8.6.1matplotlib==3.7.2scipy==1.10.1scikit-learn==1.3.0geopandas==0.13.2

This dataset includes information about total nitrogen (TN) concentrations, total phosphorus (TP) concentrations, TN:TP stoichiometry, and 12 driver variables that might predict nutrient concentrations and ratios. All observed values came from LAGOSLIMNO v. 1.054.1 and LAGOSGEO v. 1.03 (LAke multi-scaled GeOSpatial and temporal database), an integrated database of lake ecosystems (Soranno et al. 2015). LAGOS contains a complete census of lakes greater than or equal to 4 ha with corresponding geospatial information for a 17-state region of the U.S., and a subset of the lakes has observational data on morphometry and chemistry. Approximately 54 different sources of data were compiled for this dataset and were mostly generated by government agencies (state, federal, tribal) and universities. Here, we compiled chemistry data from lakes with concurrent observations of TN and TP from the summer stratified season (June 15-September 15) in the most recent 10 years of data included in LAGOSLIMNO v. 1.054.1 (2002-2011). We report the median TN, TP and molar TN:TP values for each lake, which was calculated as the grand median of each yearly median value. We also include data for lake and landscape characteristics that might be important controls on lake nutrients, including: land use (agricultural, pasture, row crop, urban, forest), nitrogen deposition, temperature, precipitation, hydrology (baseflow), maximum depth, and the ratio of lake area to watershed area, which is used to approximate residence time. These data were used to identify drivers of lake nutrient stoichiometry at sub-continental and regional scales (Collins et al, submitted). This research was supported by the NSF Macrosystems Biology program (awards EF-1065786 and EF-1065818) and by the NSF Postdoctoral Research Fellowship in Biology (DBI-1401954).

This geospatial data depicts potential development areas (PDAs) recommended by the Committee for the Regional Assessment of Offshore Wind Development in Nova Scotia. The Committee for the Regional Assessment of Offshore Wind Development in Nova Scotia (Committee) provides federal and provincial Ministers with information, knowledge, and analysis regarding future offshore wind (OSW) development in Nova Scotia. Its work is intended to inform and improve future planning, licencing, permitting, and impact assessment processes. Under the Terms of Reference, the Committee submitted an interim report to Ministers on March 23, 2024. This report included the preliminary identification of recommended areas for potential future development areas (PFDAs) for offshore wind. Based on work completed since the interim report, the Committee issued its final report on January 23, 2025, which included refined development areas, the Potential Development Areas (PDAs). These PDAs are based on technical feasibility (using available information) and have the least impact on other offshore users. It should be noted that the PDAs are recommendations only and do not reflect official offshore wind energy areas.

This point-based spatial data represents the location and content of miscellaneous text labels in the Digital Tax Map Collection. • Updates: Data is extracted from DOF’s internal system on the last Friday of each month and refreshed on ArcGIS Online on the 1st. The online map always shows the most recent version. • Accessing the Data: • Digital Tax Map on NYC Open Data: See the complete collection. • Individual layers: Downloadable from the Digital Tax Map Feature Server. • Complete source: Available through the Digital Tax Map service, which always points to the latest monthly release. Note: To ensure reliability, the Tax Map alternates between Set A and Set B each month. If one set has issues, the previous month’s copy remains online. Both sets are kept about a month apart and are available for download: • Set A link • Set B link • Digital Alteration Book (DAB): The DAB is the official log of map changes—such as new lots, merges, or boundary shifts—providing a clear record of how the Tax Map evolves. It is available through the Property Information Portal. Disclaimer: This dataset reflects formal applications submitted to DOF but may not reflect the latest changes in other City systems (e.g., exemptions or buildings data). It is provided for informational purposes only and is not guaranteed to be accurate as of today’s date.

This geospatial data depicts preliminary offshore wind licencing areas recommended by the Committee for the Regional Assessment of Offshore Wind Development in Newfoundland and Labrador (Committee). These areas were identified as an interim product during the Regional Assessment process. The Committee is tasked to complete its Regional Assessment Report by January 2025. As part of the terms of amended agreement set out by the Governments of Canada and Newfoundland and Labrador, the Committee submitted an interim report to Ministers on March 22, 2024. This report included a preliminary map of recommended areas for offshore wind. Based on work completed to date, the Committee has found these areas are where offshore wind development is most likely feasible and will have the least impact within offshore Newfoundland and Labrador. These areas are preliminary and will be refined throughout the remainder of the Regional Assessment. Through the Regional Assessment process, the Committee is providing federal and provincial Ministers with information, knowledge, and analysis regarding future offshore wind development. Their work is intended to inform and improve future planning, licencing, and impact assessment processes. Any offshore wind areas identified by the Committee do not reflect official offshore wind licencing areas. The Committee is providing these areas to Ministers for their consideration, as the offshore wind regulatory process is being established.

National Highway System (NHS) subset of Maryland’s 2018 National Bridge Inventory submission reporting conditions for 2017, with national transportation performance measure infrastructure conditions. This data will assist MPOs in analyzing the baseline conditions of the NHS in each planning area and inform 2-year and 4-year performance targets.Last Updated: September 2018For additional information, contact the MDOT SHA Geospatial Technologies Team:Email: GIS@mdot.maryland.govFor additional data, visit the MDOT GIS Open Data Portal:Website: https://data.imap.maryland.gov/pages/mdot/

CyberGIS-Jupyter for Water Quarterly Release Announcement (2020 Q2)

Dear HydroShare Users,

We are pleased to announce a new quarterly release of CyberGIS-Jupyter for Water (CJW) platform at https://go.illinois.edu/cybergis-jupyter-water. This release includes new capabilities to support the geoanalytics suite of GRASS for model pre/post-processing, PostGIS database, and Landlab Earth surface modelling toolkit along with several enhancements to job submission middleware, system security as well as service infrastructure. Please refer to the following list for details and examples.

Please let us know if you have any questions or run into any problems (help@cybergis.org). Any feedback would be greatly appreciated.

Best regards, CyberGIS-Hydro team

GRASS GIS for model pre/post-processing: Learn how to consolidate the features of the GRASS geoanalytics suite to support pre/post-processing for SUMMA and RHESSYs models in CJW. Example notebooks: https://www.hydroshare.org/resource/4cbcfdd6e7f943e2969dd52e780bc52d/

Manage geospatial data with PostGIS: PostGIS is an extension to the PostgreSQL object-relational database system which allows geospatial data to be efficiently stored while providing various advanced functions for in-situ data analysis and processing. Example notebooks: https://www.hydroshare.org/resource/bb779d4cce564dd6afcf463c8910786f/

Security and service infrastructure enhancements Trusted group: Starting from this release, all users are required to join the “CyberGIS-Jupyter for Water” trusted group at https://www.hydroshare.org/group/157 in order to access the CJW platform, which is a preventive measure to protect the shared computing resources from being abused by malicious users. A complete user profile page is highly recommended to expedite the approval process. User metric submission to XSEDE: CJW, as a science gateway, is now sending unique user usage metrics to XSEDE to comply with its requirements.

Landlab for enabling collaborative numerical modeling in Earth sciences using knowledge infrastructure Example notebooks: https://www.hydroshare.org/resource/370c288b61b84794b847ef85c4dd4ffb/ https://www.hydroshare.org/resource/6add6bee06bb4050bfe23e1081627614/

Job submission enhancements Refactored the structure of the cyberGIS job submission system Data-driven implementation for avoiding excessive data transmission between HydroShare and CJW Add the specification of input parameters into a JSON file to improve the flexibility and generality of model management Enable HPC-SUMMA object that can directly call SUMMA Example notebooks: https://www.hydroshare.org/resource/4a4a22a69f92497ead81cc48700ba8f8/

Connecticut 303(d) Impaired Waters 2016 is a subset of Connecticut 305(b) Assessed Waters 2016. If any one of the assigned designated uses is categorized as NOT Supporting, the assessed waterbody is included in this subset and is considered impaired. Additional information about Integrated Water Quality reporting is available on the CT DEEP website, Integrated Water Quality Report page. Connecticut 305(b) Assessed Waters files includes rivers, lakes and estuaries that have been assessed in compliance with Sections 305(b) and 303(d) of the federal Clean Water Act. Section 305(b) of the Clean Water Act requires each state to monitor, assess and report on the quality of its waters relative to attainment of designated uses established by the State's water quality standards. Section 303(d) requires each State to compile a subset of that list identifying only those waters not meeting water quality standards and assign a Total Maximum Daily Load (TMDL) priority ranking to each impaired waterbody.This assessment is based on information collected and compiled prior to 2016. It represents conditions at a particular point in time and does not represent current conditions. Depending on the type of waterbody - river, lake, or estuary - this information geographically displays attainment and non-attainment (e.g. full supporting, not supporting, not assessed) for each designated use - aquatic life, marine aquatic life, recreation use, fish consumption, shellfish harvesting, and drinking water supply.

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!

See full Resource Data Guide here.Abstract: The Natural Diversity Database Areas is a 1:24,000-scale, polygon feature-based layer that represents general locations of endangered, threatened and special concern species. The layer is based on information collected by DEEP biologists, cooperating scientists, conservation groups and landowners. In some cases an occurrence represents a _location derived from literature, museum records and specimens. These data are compiled and maintained by the DEEP Bureau of Natural Resources, Natural Diversity Database Program. The layer is updated every six months and reflects information that has been submitted and accepted up to that point. The layer includes state and federally listed species. It does not include Critical Habitats, Natural Area Preserves, designated wetland areas or wildlife concentration areas. These general locations were created by randomly shifting the true locations of terrestrial species and then adding a 0.25 mile buffer distance to each point, and by mapping linear segments with a 300 foot buffer associated with aquatic, riparian and coastal species. The exact _location of the species observation falls somewhere within the polygon area and not necessarily in the center. Attribute information includes the date when these data were last updated. Species names are withheld to protect sensitive species from collection and disturbance. Data is compiled at 1:24,000 scale. These data are updated every six months, approximately in June and December. It is important to use the most current data available.Purpose: This dataset was developed to help state agencies and landowners comply with the State Endangered Species Act. Under the Act, state agencies are required to ensure that any activity authorized, funded or performed by the state does not threatened the continued existence of endangered or threatened species or their essential habitat. Applicants for certain state and local permits may be required to consult with the Department of Energy and Environmental Protections's Natural Diversity Data Base (NDDB) as part of the permit process. Follow instructions provided in the appropriate permit guidance. If you require a federal endangered species review, work with your federal regulatory agency and review the US Fish & Wildlife IPaC tool. Natural Diversity Data Base Areas are intended to be used as a pre-screening tool to identify potential impacts to known locations of state listed species. To use this data for site-based endangered species review, locate the project boundaries and any additionally affected areas on the map. If any part of the project is within a NDDB Area then the project may have a conflict with listed species. In the case of a potential conflict, an Environmental Review Request (https://portal.ct.gov/deep-nddbrequest) should be made to the Natural Diversity Data Base for further review. The DEEP will provide recommendations for avoiding impacts to state listed species. Additional onsite surveys may be requested of the applicant depending on the nature and scope of a project. For this reason, applicants should apply early in the planning stages of a project. Not all land use choices will impact the particular species that is present. Often minor modifications to the proposed plan can alleviate conflicts with state listed species.Other uses of the data include targeting areas for conservation or site management to enhance and protect rare species habitats.Supplemental information: For additional information, refer to the Department of Energy and Environmental Protection En