Jurisdictional Unit, 2022-05-21. For use with WFDSS, IFTDSS, IRWIN, and InFORM.This is a feature service which provides Identify and Copy Feature capabilities. If fast-drawing at coarse zoom levels is a requirement, consider using the tile (map) service layer located at https://nifc.maps.arcgis.com/home/item.html?id=3b2c5daad00742cd9f9b676c09d03d13.OverviewThe Jurisdictional Agencies dataset is developed as a national land management geospatial layer, focused on representing wildland fire jurisdictional responsibility, for interagency wildland fire applications, including WFDSS (Wildland Fire Decision Support System), IFTDSS (Interagency Fuels Treatment Decision Support System), IRWIN (Interagency Reporting of Wildland Fire Information), and InFORM (Interagency Fire Occurrence Reporting Modules). It is intended to provide federal wildland fire jurisdictional boundaries on a national scale. The agency and unit names are an indication of the primary manager name and unit name, respectively, recognizing that:There may be multiple owner names.Jurisdiction may be held jointly by agencies at different levels of government (ie State and Local), especially on private lands, Some owner names may be blocked for security reasons.Some jurisdictions may not allow the distribution of owner names. Private ownerships are shown in this layer with JurisdictionalUnitIdentifier=null,JurisdictionalUnitAgency=null, JurisdictionalUnitKind=null, and LandownerKind="Private", LandownerCategory="Private". All land inside the US country boundary is covered by a polygon.Jurisdiction for privately owned land varies widely depending on state, county, or local laws and ordinances, fire workload, and other factors, and is not available in a national dataset in most cases.For publicly held lands the agency name is the surface managing agency, such as Bureau of Land Management, United States Forest Service, etc. The unit name refers to the descriptive name of the polygon (i.e. Northern California District, Boise National Forest, etc.).These data are used to automatically populate fields on the WFDSS Incident Information page.This data layer implements the NWCG Jurisdictional Unit Polygon Geospatial Data Layer Standard.Relevant NWCG Definitions and StandardsUnit2. A generic term that represents an organizational entity that only has meaning when it is contextualized by a descriptor, e.g. jurisdictional.Definition Extension: When referring to an organizational entity, a unit refers to the smallest area or lowest level. Higher levels of an organization (region, agency, department, etc) can be derived from a unit based on organization hierarchy.Unit, JurisdictionalThe governmental entity having overall land and resource management responsibility for a specific geographical area as provided by law.Definition Extension: 1) Ultimately responsible for the fire report to account for statistical fire occurrence; 2) Responsible for setting fire management objectives; 3) Jurisdiction cannot be re-assigned by agreement; 4) The nature and extent of the incident determines jurisdiction (for example, Wildfire vs. All Hazard); 5) Responsible for signing a Delegation of Authority to the Incident Commander.See also: Unit, Protecting; LandownerUnit IdentifierThis data standard specifies the standard format and rules for Unit Identifier, a code used within the wildland fire community to uniquely identify a particular government organizational unit.Landowner Kind & CategoryThis data standard provides a two-tier classification (kind and category) of landownership. Attribute Fields JurisdictionalAgencyKind Describes the type of unit Jurisdiction using the NWCG Landowner Kind data standard. There are two valid values: Federal, and Other. A value may not be populated for all polygons.JurisdictionalAgencyCategoryDescribes the type of unit Jurisdiction using the NWCG Landowner Category data standard. Valid values include: ANCSA, BIA, BLM, BOR, DOD, DOE, NPS, USFS, USFWS, Foreign, Tribal, City, County, OtherLoc (other local, not in the standard), State. A value may not be populated for all polygons.JurisdictionalUnitNameThe name of the Jurisdictional Unit. Where an NWCG Unit ID exists for a polygon, this is the name used in the Name field from the NWCG Unit ID database. Where no NWCG Unit ID exists, this is the “Unit Name” or other specific, descriptive unit name field from the source dataset. A value is populated for all polygons.JurisdictionalUnitIDWhere it could be determined, this is the NWCG Standard Unit Identifier (Unit ID). Where it is unknown, the value is ‘Null’. Null Unit IDs can occur because a unit may not have a Unit ID, or because one could not be reliably determined from the source data. Not every land ownership has an NWCG Unit ID. Unit ID assignment rules are available from the Unit ID standard, linked above.LandownerKindThe landowner category value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. A value is populated for all polygons. There are three valid values: Federal, Private, or Other.LandownerCategoryThe landowner kind value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. A value is populated for all polygons. Valid values include: ANCSA, BIA, BLM, BOR, DOD, DOE, NPS, USFS, USFWS, Foreign, Tribal, City, County, OtherLoc (other local, not in the standard), State, Private.DataSourceThe database from which the polygon originated. Be as specific as possible, identify the geodatabase name and feature class in which the polygon originated.SecondaryDataSourceIf the Data Source is an aggregation from other sources, use this field to specify the source that supplied data to the aggregation. For example, if Data Source is "PAD-US 2.1", then for a USDA Forest Service polygon, the Secondary Data Source would be "USDA FS Automated Lands Program (ALP)". For a BLM polygon in the same dataset, Secondary Source would be "Surface Management Agency (SMA)."SourceUniqueIDIdentifier (GUID or ObjectID) in the data source. Used to trace the polygon back to its authoritative source.MapMethod:Controlled vocabulary to define how the geospatial feature was derived. Map method may help define data quality. MapMethod will be Mixed Method by default for this layer as the data are from mixed sources. Valid Values include: GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; DigitizedTopo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; Phone/Tablet; OtherDateCurrentThe last edit, update, of this GIS record. Date should follow the assigned NWCG Date Time data standard, using 24 hour clock, YYYY-MM-DDhh.mm.ssZ, ISO8601 Standard.CommentsAdditional information describing the feature. GeometryIDPrimary key for linking geospatial objects with other database systems. Required for every feature. This field may be renamed for each standard to fit the feature.JurisdictionalUnitID_sansUSNWCG Unit ID with the "US" characters removed from the beginning. Provided for backwards compatibility.JoinMethodAdditional information on how the polygon was matched information in the NWCG Unit ID database.LocalNameLocalName for the polygon provided from PADUS or other source.LegendJurisdictionalAgencyJurisdictional Agency but smaller landholding agencies, or agencies of indeterminate status are grouped for more intuitive use in a map legend or summary table.LegendLandownerAgencyLandowner Agency but smaller landholding agencies, or agencies of indeterminate status are grouped for more intuitive use in a map legend or summary table.DataSourceYearYear that the source data for the polygon were acquired.Data InputThis dataset is based on an aggregation of 4 spatial data sources: Protected Areas Database US (PAD-US 2.1), data from Bureau of Indian Affairs regional offices, the BLM Alaska Fire Service/State of Alaska, and Census Block-Group Geometry. NWCG Unit ID and Agency Kind/Category data are tabular and sourced from UnitIDActive.txt, in the WFMI Unit ID application (https://wfmi.nifc.gov/unit_id/Publish.html). Areas of with unknown Landowner Kind/Category and Jurisdictional Agency Kind/Category are assigned LandownerKind and LandownerCategory values of "Private" by use of the non-water polygons from the Census Block-Group geometry.PAD-US 2.1:This dataset is based in large part on the USGS Protected Areas Database of the United States - PAD-US 2.`. PAD-US is a compilation of authoritative protected areas data between agencies and organizations that ultimately results in a comprehensive and accurate inventory of protected areas for the United States to meet a variety of needs (e.g. conservation, recreation, public health, transportation, energy siting, ecological, or watershed assessments and planning). Extensive documentation on PAD-US processes and data sources is available.How these data were aggregated:Boundaries, and their descriptors, available in spatial databases (i.e. shapefiles or geodatabase feature classes) from land management agencies are the desired and primary data sources in PAD-US. If these authoritative sources are unavailable, or the agency recommends another source, data may be incorporated by other aggregators such as non-governmental organizations. Data sources are tracked for each record in the PAD-US geodatabase (see below).BIA and Tribal Data:BIA and Tribal land management data are not available in PAD-US. As such, data were aggregated from BIA regional offices. These data date from 2012 and were substantially updated in 2022. Indian Trust Land affiliated with Tribes, Reservations, or BIA Agencies: These data are not considered the system of record and are not intended to be used as such. The Bureau of Indian Affairs (BIA), Branch of Wildland Fire Management (BWFM) is not the originator of these data. The

SafeGraph Places provides baseline information for every record in the SafeGraph product suite via the Places schema and polygon information when applicable via the Geometry schema. The current scope of a place is defined as any location humans can visit with the exception of single-family homes. This definition encompasses a diverse set of places ranging from restaurants, grocery stores, and malls; to parks, hospitals, museums, offices, and industrial parks. Premium sets of Places include apartment buildings, Parking Lots, and Point POIs (such as ATMs or transit stations).

SafeGraph Places is a point of interest (POI) data offering with varying coverage depending on the country. Note that address conventions and formatting vary across countries. SafeGraph has coalesced these fields into the Places schema.

SafeGraph provides clean and accurate geospatial datasets on 52M+ physical places/points of interest (POI) globally. Hundreds of industry leaders like Mapbox, Verizon, Clear Channel, and Esri already rely on SafeGraph POI data to unlock business insights and drive innovation.

The Navigation Data Center had several objectives in developing the U.S. Waterway Data. These objectives support the concept of a National Spatial Data Provide public access to national waterway data. Foster interagency and intra-agency cooperation through data sharing. Provide a mechanism to integrate waterway data (U.S. Army Corps of Engineers Port/Facility and U.S. Coast Guard Accident Data, for example) Provide a basis for intermodal analysis. Assist standardization of waterway entity definitions (Ports/Facilities, Locks, etc.). Provide public access to the National Waterway Network, which can be used as a basemap to support graphical overlays and analysis with other spatial data (waterway and modal network/facility databases, for example). Provide reliable data to support future waterway and intermodal applications. Source of Data The data included in these files are based upon the Annual Summary of Lock Statistics published by the U.S. Army Corps of Engineers/CEIWR, Navigation Data Center. The data are collected at each Corps owned and/or operated Lock by Corps personnel and towing industry vessel operators. This data was collected from the US Army Corps of Engineers and distributed on the National Transportation Atlas Database (NTAD).

© The U.S. Army Corps of Engineers/CEIWR, Navigation Data Center This layer is sourced from maps.bts.dot.gov.

Monthly summary statistics are based on data from the Lock Performance Monitoring System (LPMS). The LPMS was developed to collect a 100% sample of data on the locks that are owned and/or operated by the US Army Corps of Engineers. Each record contains data summarized monthly by lock chamber, and direction (upbound and number and types of vessels and lockages (recreation, commercial, tows, other), cuts, hardware operations, delay and processing times, number of tows and all vessels delayed, total tons, commodity tonnages, and number of barges. The data are by waterway and by calendar year. The waterway files contain 5 years of data for one waterway. The calendar year files contain 1 year of data for all waterways.

The Navigation Data Center had several objectives in developing the U.S. Waterway Data. These objectives support the concept of a National Spatial Data Provide public access to national waterway data. Foster interagency and intra-agency cooperation through data sharing. Provide a mechanism to integrate waterway data (U.S. Army Corps of Engineers Port/Facility and U.S. Coast Guard Accident Data, for example) Provide a basis for intermodal analysis. Assist standardization of waterway entity definitions (Ports/Facilities, Locks, etc.). Provide public access to the National Waterway Network, which can be used as a basemap to support graphical overlays and analysis with other spatial data (waterway and modal network/facility databases, for example). Provide reliable data to support future waterway and intermodal applications. Source of Data The data included in these files are based upon the Annual Summary of Lock Statistics published by the U.S. Army Corps of Engineers/CEIWR, Navigation Data Center. The data are collected at each Corps owned and/or operated Lock by Corps personnel and towing industry vessel operators. This data was collected from the US Army Corps of Engineers and distributed on the National Transportation Atlas Database (NTAD).

© The U.S. Army Corps of Engineers/CEIWR, Navigation Data Center

This file contains the data set used to develop a random forest model predict background specific conductivity for stream segments in the contiguous United States. This Excel readable file contains 56 columns of parameters evaluated during development. The data dictionary provides the definition of the abbreviations and the measurement units. Each row is a unique sample described as R** which indicates the NHD Hydrologic Unit (underscore), up to a 7-digit COMID, (underscore) sequential sample month. To develop models that make stream-specific predictions across the contiguous United States, we used StreamCat data set and process (Hill et al. 2016; https://github.com/USEPA/StreamCat). The StreamCat data set is based on a network of stream segments from NHD+ (McKay et al. 2012). These stream segments drain an average area of 3.1 km2 and thus define the spatial grain size of this data set. The data set consists of minimally disturbed sites representing the natural variation in environmental conditions that occur in the contiguous 48 United States. More than 2.4 million SC observations were obtained from STORET (USEPA 2016b), state natural resource agencies, the U.S. Geological Survey (USGS) National Water Information System (NWIS) system (USGS 2016), and data used in Olson and Hawkins (2012) (Table S1). Data include observations made between 1 January 2001 and 31 December 2015 thus coincident with Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data (https://modis.gsfc.nasa.gov/data/). Each observation was related to the nearest stream segment in the NHD+. Data were limited to one observation per stream segment per month. SC observations with ambiguous locations and repeat measurements along a stream segment in the same month were discarded. Using estimates of anthropogenic stress derived from the StreamCat database (Hill et al. 2016), segments were selected with minimal amounts of human activity (Stoddard et al. 2006) using criteria developed for each Level II Ecoregion (Omernik and Griffith 2014). Segments were considered as potentially minimally stressed where watersheds had 0 - 0.5% impervious surface, 0 – 5% urban, 0 – 10% agriculture, and population densities from 0.8 – 30 people/km2 (Table S3). Watersheds with observations with large residuals in initial models were identified and inspected for evidence of other human activities not represented in StreamCat (e.g., mining, logging, grazing, or oil/gas extraction). Observations were removed from disturbed watersheds, with a tidal influence or unusual geologic conditions such as hot springs. About 5% of SC observations in each National Rivers and Stream Assessment (NRSA) region were then randomly selected as independent validation data. The remaining observations became the large training data set for model calibration. This dataset is associated with the following publication: Olson, J., and S. Cormier. Modeling spatial and temporal variation in natural background specific conductivity. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 53(8): 4316-4325, (2019).

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .

The National Marine Fisheries Service (NMFS) developed this geodatabase to standardize its Endangered Species Act (ESA) critical habitat spatial data. The spatial data represent critical habitat locations; however, the complete description and official boundaries of critical habitat proposed or designated by NMFS are provided in proposed rules, final rules, and the Code of Federal Regulations (50 CFR 226). Official critical habitat boundaries may include regulatory text that modifies or clarifies maps and spatial data. Proposed rules, final rules, and the CFR also describe any areas that are excluded from critical habitat or otherwise not part of critical habitat (e.g., ineligible areas), some of which have not been clipped out of the spatial data.Geodatabase feature classes are organized by ESA listed entities. A listed entity can be a species, subspecies, distinct population segment (DPS), or evolutionarily significant unit (ESU). NMFS and the U.S. Fish and Wildlife Service share jurisdiction of some listed entities; this geodatabase only contains spatial data for NMFS critical habitat. Critical habitat has not been designated for all listed entities.Generally, each listed entity has one feature class. However, a listed entity may have critical habitat locations represented by both lines and polygons. In these instances, "_poly" and "_line" are appended to the feature class names to differentiate between the spatial data types. Lines represent rivers, streams, or beaches and polygons represent waterbodies, marine areas, estuaries, marshes, or watersheds. The 8 digit date (YYYYMMDD) in each feature class name is the publication date of the proposed or final rule in the Federal Register. Both proposed and designated critical habitat are included in this geodatabase. To differentiate between these categories, all proposed critical habitat feature classes begin with "Proposed_". Proposed critical habitat will be replaced by final designations soon after a final rule is published in the Federal Register. This geodatabase version may not include spatial data for recently proposed, modified, or designated critical habitat. Additionally, spatial data are not available for the designated critical habitat of the Southern Oregon/Northern California Coast coho salmon ESU and the Snake River spring/summer-run Chinook salmon ESU. NMFS will add these spatial data when they become available. In the meantime, please consult the final rules or CFR. NMFS may periodically update existing lines or polygons if better information becomes available, such as higher resolution bathymetric surveys. The "All_critical_habitat" feature dataset includes merged line and polygon feature classes that contain all available spatial data for critical habitat proposed or designated by NMFS; therefore, these feature classes contain overlapping features. The "All_critical_habitat_line_YYYYMMDD" and "All_critical_habitat_poly_YYYYMMDD" feature classes should be used together to represent all available spatial data. The date appended to the feature class names is the date the geoprocessing (merge) occured. Features in this geodatabase were compiled from previously developed spatial data. The methods and sources used to create these spatial data are NOT standardized. Coastlines, bathymetric contours, and river lines, for example, were all derived from a variety of sources, using many different geoprocessing techniques, over the span of decades. If information was available on source data and/or processing steps, it was documented in the metadata lineage. Metadata descriptions and the "Notes" field describe line and boundary definitions. Line and boundary definitions are specific to each proposed or designated critical habitat dataset. For example, depending on the listed entity, a coastline could represent the Mean Higher High Water (MHHW) line in one designation and the Mean Lower Low Water (MLLW) line in another designation. Metadata for each feature class is a combination of standardized and unique content. Standardized content includes the field and value definitions, spatial reference (WGS 84 geographic coordinate system), and metadata style (ISO 19139). All other metadata content is unique to each feature class. eCFR official ESA listeCFR official NMFS critical habitat designationsNMFS critical habitat websiteNMFS maps and GIS data directoryNMFS ESA threatened and endangered species directoryNMFS ESA regulations and actions directory

Overview

3DHD CityScenes is the most comprehensive, large-scale high-definition (HD) map dataset to date, annotated in the three spatial dimensions of globally referenced, high-density LiDAR point clouds collected in urban domains. Our HD map covers 127 km of road sections of the inner city of Hamburg, Germany including 467 km of individual lanes. In total, our map comprises 266,762 individual items.

Our corresponding paper (published at ITSC 2022) is available here. Further, we have applied 3DHD CityScenes to map deviation detection here.

Moreover, we release code to facilitate the application of our dataset and the reproducibility of our research. Specifically, our 3DHD_DevKit comprises:

Python tools to read, generate, and visualize the dataset,

3DHDNet deep learning pipeline (training, inference, evaluation) for map deviation detection and 3D object detection.

The DevKit is available here:

https://github.com/volkswagen/3DHD_devkit.

The dataset and DevKit have been created by Christopher Plachetka as project lead during his PhD period at Volkswagen Group, Germany.

When using our dataset, you are welcome to cite:

@INPROCEEDINGS{9921866, author={Plachetka, Christopher and Sertolli, Benjamin and Fricke, Jenny and Klingner, Marvin and Fingscheidt, Tim}, booktitle={2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)}, title={3DHD CityScenes: High-Definition Maps in High-Density Point Clouds}, year={2022}, pages={627-634}}

Acknowledgements

We thank the following interns for their exceptional contributions to our work.

Benjamin Sertolli: Major contributions to our DevKit during his master thesis

Niels Maier: Measurement campaign for data collection and data preparation

The European large-scale project Hi-Drive (www.Hi-Drive.eu) supports the publication of 3DHD CityScenes and encourages the general publication of information and databases facilitating the development of automated driving technologies.

The Dataset

After downloading, the 3DHD_CityScenes folder provides five subdirectories, which are explained briefly in the following.

1. Dataset

This directory contains the training, validation, and test set definition (train.json, val.json, test.json) used in our publications. Respective files contain samples that define a geolocation and the orientation of the ego vehicle in global coordinates on the map.

During dataset generation (done by our DevKit), samples are used to take crops from the larger point cloud. Also, map elements in reach of a sample are collected. Both modalities can then be used, e.g., as input to a neural network such as our 3DHDNet.

To read any JSON-encoded data provided by 3DHD CityScenes in Python, you can use the following code snipped as an example.

import json

json_path = r"E:\3DHD_CityScenes\Dataset\train.json" with open(json_path) as jf: data = json.load(jf) print(data)

1. HD_Map

Map items are stored as lists of items in JSON format. In particular, we provide:

traffic signs,

traffic lights,

pole-like objects,

construction site locations,

construction site obstacles (point-like such as cones, and line-like such as fences),

line-shaped markings (solid, dashed, etc.),

polygon-shaped markings (arrows, stop lines, symbols, etc.),

lanes (ordinary and temporary),

relations between elements (only for construction sites, e.g., sign to lane association).

1. HD_Map_MetaData

Our high-density point cloud used as basis for annotating the HD map is split in 648 tiles. This directory contains the geolocation for each tile as polygon on the map. You can view the respective tile definition using QGIS. Alternatively, we also provide respective polygons as lists of UTM coordinates in JSON.

Files with the ending .dbf, .prj, .qpj, .shp, and .shx belong to the tile definition as “shape file” (commonly used in geodesy) that can be viewed using QGIS. The JSON file contains the same information provided in a different format used in our Python API.

1. HD_PointCloud_Tiles

The high-density point cloud tiles are provided in global UTM32N coordinates and are encoded in a proprietary binary format. The first 4 bytes (integer) encode the number of points contained in that file. Subsequently, all point cloud values are provided as arrays. First all x-values, then all y-values, and so on. Specifically, the arrays are encoded as follows.

x-coordinates: 4 byte integer

y-coordinates: 4 byte integer

z-coordinates: 4 byte integer

intensity of reflected beams: 2 byte unsigned integer

ground classification flag: 1 byte unsigned integer

After reading, respective values have to be unnormalized. As an example, you can use the following code snipped to read the point cloud data. For visualization, you can use the pptk package, for instance.

import numpy as np import pptk

file_path = r"E:\3DHD_CityScenes\HD_PointCloud_Tiles\HH_001.bin" pc_dict = {} key_list = ['x', 'y', 'z', 'intensity', 'is_ground'] type_list = ['

Layers are organized by ESA listed entities. A listed entity can be a species, subspecies, distinct population segment (DPS), or evolutionarily significant unit (ESU). NMFS and the U.S. Fish and Wildlife Service share jurisdiction of some listed entities; this service only contains spatial data for NMFS critical habitat. Critical habitat has not been designated for all listed entities.Generally, each listed entity has one layer. However, a listed entity may have critical habitat locations represented by both lines and polygons. In these instances, "_poly" and "_line" are appended to the layer names to differentiate between the spatial data types. Lines represent rivers, streams, or beaches and polygons represent waterbodies, marine areas, estuaries, marshes, or watersheds. The 8 digit date (YYYYMMDD) in each layer name is the publication date of the proposed or final rule in the Federal Register.Both proposed and designated critical habitat are included in this service. To differentiate between these categories, all proposed critical habitat layers begin with "Proposed_". Proposed critical habitat will be replaced by final designations soon after a final rule is published in the Federal Register. This service version may not include spatial data for recently proposed, modified, or designated critical habitat. Additionally, spatial data are not available for the designated critical habitat of the Southern Oregon/Northern California Coast coho salmon ESU and the Snake River spring/summer-run Chinook salmon ESU. NMFS will add these spatial data when they become available. In the meantime, please consult the final rules or CFR. NMFS may periodically update existing lines or polygons if better information becomes available, such as higher resolution bathymetric surveys.The "All_critical_habitat" layer group includes merged line and polygon feature classes that contain all available spatial data for critical habitat proposed or designated by NMFS; therefore, these layers contain overlapping features. The "All_critical_habitat_line_YYYYMMDD" and "All_critical_habitat_poly_YYYYMMDD" layers should be used together to represent all available spatial data. The date appended to the layer names is the date the geoprocessing (merge) occured.Features in this service were compiled from previously developed spatial data. The methods and sources used to create these spatial data are NOT standardized. Coastlines, bathymetric contours, and river lines, for example, were all derived from a variety of sources, using many different geoprocessing techniques, over the span of decades. If information was available on source data and/or processing steps, it was documented in the metadata lineage. Metadata descriptions and the "Notes" field describe line and boundary definitions. Line and boundary definitions are specific to each proposed or designated critical habitat dataset. For example, depending on the listed entity, a coastline could represent the Mean Higher High Water (MHHW) line in one designation and the Mean Lower Low Water (MLLW) line in another designation.Metadata for each layer is a combination of standardized and unique content and can be viewed at https://www.fisheries.noaa.gov/inport/item/65207. Standardized content includes the field and value definitions, spatial reference, and metadata style (ISO 19139). All other metadata content is unique to each layer.These data have been made publicly available from an authoritative source other than this Atlas and data should be obtained directly from that source for any re-use. See the original metadata from the authoritative source for more information about these data and use limitations. The authoritative source of these data can be found at the following location: NMFS Critical Habitat

1. Multiscale codependence analysis (MCA) quantifies the joint spatial distribution of a pair of variables in order to provide a spatially-explicit assessment of their relationships to one another. For the sake of simplicity, the original definition of MCA only considered a single response variable (e.g. a single species). However, that definition would limit the application of MCA when many response variables are studied jointly, for example when one wants to study the effect of the environment on the spatial organisation of a multi-species community in an explicit manner.
2. In the present paper, we generalize MCA to multiple response variables. We conducted a simulation study to assess the statistical properties (i.e. type I error rate and statistical power) of multivariate MCA (mMCA) and found that it had honest type I error rate and sufficient statistical power for practical purposes, even with modest sample sizes. We also exemplified mMCA by applying it to two ecological data sets.
3. The simulation study confirmed the adequacy of mMCA from a statistical standpoint: it has honest type I error rates and sufficient power to be useful in practice. Using mMCA, we were able to detect variation in fish community structure along the Doubs River (in France), which was associated with large spatial structures in the variation of physical and chemical variables related to water quality. Also, mMCA usefully described the spatial variation of an Oribatid mite community structure associated with a gradient of water content superimposed on various smaller-scale spatial features associated with vegetation cover in the peat blanket surrounding Lac Geai (in Québec, Canada).
4. In addition to demonstrating the soundness of mMCA in theory and practice, we further discuss the strengths and assumptions of mMCA and describe other potential scenarios where it would be helpful to biologists interested in assessing influence of environmental conditions on community structure in a spatially-explicit way.

description: The Geopspatial Fabric provides a consistent, documented, and topologically connected set of spatial features that create an abstracted stream/basin network of features useful for hydrologic modeling.The GIS vector features contained in this Geospatial Fabric (GF) data set cover the lower 48 U.S. states, Hawaii, and Puerto Rico. Four GIS feature classes are provided for each Region: 1) the Region outline ("one"), 2) Points of Interest ("POIs"), 3) a routing network ("nsegment"), and 4) Hydrologic Response Units ("nhru"). A graphic showing the boundaries for all Regions is provided at http://dx.doi.org/doi:10.5066/F7542KMD. These Regions are identical to those used to organize the NHDPlus v.1 dataset (US EPA and US Geological Survey, 2005). Although the GF Feature data set has been derived from NHDPlus v.1, it is an entirely new data set that has been designed to generically support regional and national scale applications of hydrologic models. Definition of each type of feature class and its derivation is provided within the

The National Marine Fisheries Service (NMFS) developed this geodatabase to standardize its Endangered Species Act (ESA) critical habitat spatial data. The spatial data represent critical habitat locations; however, the complete description and official boundaries of critical habitat proposed or designated by NMFS are provided in proposed rules, final rules, and the Code of Federal Regulations (50 CFR 226). Official critical habitat boundaries may include regulatory text that modifies or clarifies maps and spatial data. Proposed rules, final rules, and the CFR also describe any areas that are excluded from critical habitat or otherwise not part of critical habitat (e.g., ineligible areas), some of which have not been clipped out of the spatial data.Geodatabase feature classes are organized by ESA listed entities. A listed entity can be a species, subspecies, distinct population segment (DPS), or evolutionarily significant unit (ESU). NMFS and the U.S. Fish and Wildlife Service share jurisdiction of some listed entities; this geodatabase only contains spatial data for NMFS critical habitat. Critical habitat has not been designated for all listed entities.Generally, each listed entity has one feature class. However, a listed entity may have critical habitat locations represented by both lines and polygons. In these instances, "_poly" and "_line" are appended to the feature class names to differentiate between the spatial data types. Lines represent rivers, streams, or beaches and polygons represent waterbodies, marine areas, estuaries, marshes, or watersheds. The 8 digit date (YYYYMMDD) in each feature class name is the publication date of the proposed or final rule in the Federal Register. Both proposed and designated critical habitat are included in this geodatabase. To differentiate between these categories, all proposed critical habitat feature classes begin with "Proposed_". Proposed critical habitat will be replaced by final designations soon after a final rule is published in the Federal Register. This geodatabase version may not include spatial data for recently proposed, modified, or designated critical habitat. Additionally, spatial data are not available for the designated critical habitat of the Southern Oregon/Northern California Coast coho salmon ESU and the Snake River spring/summer-run Chinook salmon ESU. NMFS will add these spatial data when they become available. In the meantime, please consult the final rules or CFR. NMFS may periodically update existing lines or polygons if better information becomes available, such as higher resolution bathymetric surveys. The "All_critical_habitat" feature dataset includes merged line and polygon feature classes that contain all available spatial data for critical habitat proposed or designated by NMFS; therefore, these feature classes contain overlapping features. The "All_critical_habitat_line_YYYYMMDD" and "All_critical_habitat_poly_YYYYMMDD" feature classes should be used together to represent all available spatial data. The date appended to the feature class names is the date the geoprocessing (merge) occured. Features in this geodatabase were compiled from previously developed spatial data. The methods and sources used to create these spatial data are NOT standardized. Coastlines, bathymetric contours, and river lines, for example, were all derived from a variety of sources, using many different geoprocessing techniques, over the span of decades. If information was available on source data and/or processing steps, it was documented in the metadata lineage. Metadata descriptions and the "Notes" field describe line and boundary definitions. Line and boundary definitions are specific to each proposed or designated critical habitat dataset. For example, depending on the listed entity, a coastline could represent the Mean Higher High Water (MHHW) line in one designation and the Mean Lower Low Water (MLLW) line in another designation. Metadata for each feature class is a combination of standardized and unique content. Standardized content includes the field and value definitions, spatial reference (WGS 84 geographic coordinate system), and metadata style (ISO 19139). All other metadata content is unique to each feature class. eCFR official ESA listeCFR official NMFS critical habitat designationsNMFS critical habitat websiteNMFS maps and GIS data directoryNMFS ESA threatened and endangered species directoryNMFS ESA regulations and actions directory

A new technique called generalized additive spatial smoothing (GASS) is introduced for modeling neighborhood effects within a regression framework. GASS has a number of desirable features, namely that it provides a data-driven mechanism for endogenously selecting neighborhoods based on a spatial scale hyperparameter. By allowing different scale hyperparameters to be selected for different relationships in the model, the technique is inherently multiscale and allows neighborhoods to vary by relationship. In addition, GASS includes a measure of uncertainty associated with each scale hyperparameter. These characteristics make it attractive for modeling phenomena where proximity might be an important aspect of a process, especially when a clear definition of proximity is not immediately available. Through multiscale data-driven spatial smoothing, GASS conducts a form of change of support and therefore also facilitates the incorporation of data from diverse sources. Finally, the technique is flexible and can be adapted and expanded with relative ease because it builds on generalized additive modeling. After providing an overview of the methodology, including a modified backfitting algorithm for calibration, a simulation experiment is described and an empirical example modeling bike-share usage is presented. The simulation results indicate that GASS can generally produce reliable results pertaining to both the regression coefficients and scale hyperparameters, and the results from the empirical example demonstrate that the GASS approach provides a better model fit and captures relationships that might otherwise be obfuscated. Overall, these results highlight the potential of the GASS framework and the importance of measuring multiscale neighborhood effects.

This data package corresponds to a research paper by Giesbrecht et. al. (2025) in the journal Ecosystems with the title "Mapping the spatial heterogeneity of watershed ecosystems and water quality in rainforest fjordlands". https://doi.org/10.1007/s10021-025-00964-x The data package contains:

A shapefile representing sampled watershed boundaries in .shp format ("G2025_Wts_boundaries.shp") Table of watershed characteristics in .csv format ("G2025_Wts_data.csv") Table of quality controlled water quality data in .csv format ("G2025_WQ_data.csv") A README file with variable definitions for water quality data ("G2025_WQ_README.csv") A README file with variable definitions for watershed characteristics ("G2025_Wts_README.csv")

Methods In this study, we examined spatial controls on the quality of freshwater exported from diverse watersheds in fjordlands of a coastal temperate rainforest. Samples were collected about once per month for a year from the outlets of 56 watersheds spanning from high mountains with icefields to low islands with extensive wetlands. Watershed size ranged from < 1.5 km2 to 5,782 km2 (Homathko River), yet in the regional and global context, all are considered “small” coastal watersheds (< 10,000 km2 following Milliman and Syvitski, 1992). The study watersheds were spatially distributed along two fjordland transects on the south-central coast of British Columbia, Canada (51°57' N to 50°07' N and 128°09' W to 123°44' W). Watershed characterization and classification This study takes advantage of a previous watershed classification effort, which used four widely available (open access) datasets and 12 easily computed watershed characteristics to define 12 types of small coastal watersheds with cluster analysis (Giesbrecht and others 2022). These clusters separated watersheds by characteristic water source (glacial, snowmelt, rain), topography (mountains, hills, lowland), climate and geographic location within the NPCTR (north, central, south). For the present study, we assigned every sampled watershed to one of the 12 types of coastal watershed defined in Giesbrecht and others (2022). This assignment required a modelling step because 34 of our 56 sampled watersheds were smaller than the minimum size (20 km2) of well delineated watersheds (DW) used in the regional scale classification (2022). We used a random forest (Breiman, 2001) classifier (randomForest package (version 4.6-14) in R (R Core Team, 2020)) to assign class membership to newly delineated (very small) watersheds.The predictor variables were the 12 watershed characteristics originally used to define the regional watershed types via cluster analysis (Giesbrecht and others, 2022). The response variable was watershed type. The present analysis revised the previous regional watershed classification by better resolving the locations and extent of watersheds in the ~ 1 to 10 km2 size range, particularly those with very low relief and extensive wetland cover. Stream chemistry data Water samples were collected from the watershed outlets roughly once every month for a year (March 2018 to March 2019), for a total of 405 observation site-days after quality control. Most watersheds were sampled eight to ten times. Each transect was surveyed over two to three consecutive days in order to sample under relatively similar weather and flow conditions. The two transects were surveyed as close together in time as feasible, but were often more than a week apart, thus not always capturing the same weather system. From each water sample, we measured 22 aspects of riverine water quality, including DOC, alkalinity, cations, organic and inorganic nutrients, 𝛿18O-H2O, 𝛿2H-H2O, and handheld sensor (YSI ProDSS) readings of temperature, specific conductance, pH, and turbidity. Water samples and sensor readings were taken from the main flow, avoiding eddies, shallow water, loose substrates, or woody debris. Samples for dissolved constituents were field-filtered with a 0.45 µm Millipore® Millex-HP hydrophilic polyethyl sulfonate (PES) syringe filter. All samples were kept cool and dark during the field work. Samples were then preserved by freezing or acidification as appropriate, within 24 hours of field collection. The field and laboratory procedures for this study follow those of St. Pierre and others (2021) and Tank and others (2020). Laboratory results below the detection limit were replaced by ½ the detection limit, following common convention (e.g., EPA, 2000). In addition to direct measurements, we calculated several variables from the analytical laboratory results: the total concentration of dissolved inorganic nitrogen (DIN), dissolved organic nitrogen (DON), particulate nitrogen (PN) dissolved organic phosphorous (DOP), and particulate phosphorous (PP). Finally, we computed the mass ratio of sodium to calcium ions (Na:Ca) as a simple index of cation origin. High Na:Ca ratios can be caused by high inputs of cyclic marine salts (via precipitation) relative to cation inputs from rock weathering (Gibbs, 1970; Schlesinger, 1997) and by high inputs from silicate weathering relative to carbonate weathering (Gaillardet and others, 1999; Tank and others, 2012a). Several quality control (QC) and data cleaning procedures were implemented prior to the analysis, using a combination of visual inspection and data-based criteria. For visual inspection, tables and plots of the water quality measurements were examined while cross referencing metadata from field notes and laboratory notes. We omitted any suspiciously high or low values that could be readily explained by a procedural anomaly such as a cracked sample vial. For data-based QC, outlier values of sensitive species (DIN species, TN, and SRP) were identified (mean ± 4SD) and omitted unless supported by independent measurements (e.g., high DIN supported by high TDN and high TN). Additional quality control procedures were applied to calculated values to avoid use of illogical results. For example, where DIN > TDN, the resulting negative DON value was replaced with ½ the detection limit of TDN to indicate a small non-zero quantity. We also omitted samples where specific conductance exceeded 200 µS cm-1, which are suspiciously high for the geological conditions we sampled. These samples also had high concentrations of Na+, K+, Cl-/SO42-, or Sr2+ (where available), likely due to tidal mixing of brackish water. We identified seven such cases, representing five site-dates. Please refer to the corresponding research paper for a more complete description of methods: https://doi.org/10.1007/s10021-025-00964-x

NOTE: This geodatabase is depreciated. To view most recent version, go to the following link.The National Marine Fisheries Service (NMFS) developed this geodatabase to standardize its Endangered Species Act (ESA) critical habitat spatial data. The spatial data represent critical habitat locations; however, the complete description and official boundaries of critical habitat proposed or designated by NMFS are provided in proposed rules, final rules, and the Code of Federal Regulations (50 CFR 226). Official critical habitat boundaries may include regulatory text that modifies or clarifies maps and spatial data. Proposed rules, final rules, and the CFR also describe any areas that are excluded from critical habitat or otherwise not part of critical habitat (e.g., ineligible areas), some of which have not been clipped out of the spatial data.Geodatabase feature classes are organized by ESA listed entities. A listed entity can be a species, subspecies, distinct population segment (DPS), or evolutionarily significant unit (ESU). NMFS and the U.S. Fish and Wildlife Service share jurisdiction of some listed entities; this geodatabase only contains spatial data for NMFS critical habitat. Critical habitat has not been designated for all listed entities.Generally, each listed entity has one feature class. However, a listed entity may have critical habitat locations represented by both lines and polygons. In these instances, "_poly" and "_line" are appended to the feature class names to differentiate between the spatial data types. Lines represent rivers, streams, or beaches and polygons represent waterbodies, marine areas, estuaries, marshes, or watersheds. The 8 digit date (YYYYMMDD) in each feature class name is the publication date of the proposed or final rule in the Federal Register. Both proposed and designated critical habitat are included in this geodatabase. To differentiate between these categories, all proposed critical habitat feature classes begin with "Proposed_". Proposed critical habitat will be replaced by final designations soon after a final rule is published in the Federal Register. This geodatabase version may not include spatial data for recently proposed, modified, or designated critical habitat. Additionally, spatial data are not available for the designated critical habitat of the Southern Oregon/Northern California Coast coho salmon ESU and the Snake River spring/summer-run Chinook salmon ESU. NMFS will add these spatial data when they become available. In the meantime, please consult the final rules or CFR. NMFS may periodically update existing lines or polygons if better information becomes available, such as higher resolution bathymetric surveys. The "All_critical_habitat" feature dataset includes merged line and polygon feature classes that contain all available spatial data for critical habitat proposed or designated by NMFS; therefore, these feature classes contain overlapping features. The "All_critical_habitat_line_YYYYMMDD" and "All_critical_habitat_poly_YYYYMMDD" feature classes should be used together to represent all available spatial data. The date appended to the feature class names is the date the geoprocessing (merge) occured. Features in this geodatabase were compiled from previously developed spatial data. The methods and sources used to create these spatial data are NOT standardized. Coastlines, bathymetric contours, and river lines, for example, were all derived from a variety of sources, using many different geoprocessing techniques, over the span of decades. If information was available on source data and/or processing steps, it was documented in the metadata lineage. Metadata descriptions and the "Notes" field describe line and boundary definitions. Line and boundary definitions are specific to each proposed or designated critical habitat dataset. For example, depending on the listed entity, a coastline could represent the Mean Higher High Water (MHHW) line in one designation and the Mean Lower Low Water (MLLW) line in another designation. Metadata for each feature class is a combination of standardized and unique content. Standardized content includes the field and value definitions, spatial reference (WGS 84 geographic coordinate system), and metadata style (ISO 19139). All other metadata content is unique to each feature class. eCFR official ESA listeCFR official NMFS critical habitat designationsNMFS critical habitat websiteNMFS maps and GIS data directoryNMFS ESA threatened and endangered species directoryNMFS ESA regulations and actions directory

Data innovations happen daily: the semantic web, the cloud, visualization, mapping, sensors, spatial data infrastructures, etc. This portion of the Training Day will focus on recent access to public data initiatives in Canada with an emphasis on open government and open data. In this session participants will be introduced to data and participatory democracy, open data definitions and examples of good government policy. In addition, we will look at what some community groups are doing, the leadership in Canada’s big cities and the Province of BC by administrations and citizens. This will include licenses, open data initiatives, hackfests, hackathons, applications, challenges and opportunities. It is hoped that this overview will provide participants with insight about what is new in the Canadian access to public data world.

The dataset contains information on the European river basin districts, the river basin district sub-units, the surface water bodies and the groundwater bodies delineated for the 1st River Basin Management Plans (RBMP) under the Water Framework Directive (WFD) as well as the European monitoring sites used for the assessment of the status of the abovementioned surface water bodies and groundwater bodies.

The information was reported to the European Commission under the Water Framework Directive (WFD) reporting obligations.

The dataset compiles the available spatial data related to the 1st RBMPs which were due in 2010 (hereafter WFD2010). See http://rod.eionet.europa.eu/obligations/521 for further information on the WFD2010 reporting.

It was prepared to support the reporting of the 2nd RBMPs due in 2016 (hereafter WFD2016). See http://rod.eionet.europa.eu/obligations/715 for further information on the WFD2016 reporting.

The data reported in WFD2010 were updated using data reported in WFD2016, whenever the spatial objects are identical in 2010 and 2016. For WFD2010 objects, some information may be missing, if the objects no longer exist in the 2nd River Basin Management Plans, and were not reported in WFD2016.

Relevant concepts:

River basin district (RBD): The area of land and sea, made up of one or more neighbouring river basins together with their associated groundwaters and coastal waters, which is the main unit for management of river basins.

River basin: The area of land from which all surface run-off flows through a sequence of streams, rivers and, possibly, lakes into the sea at a single river mouth, estuary or delta.

Sub-basin: The area of land from which all surface run-off flows through a series of streams, rivers and, possibly, lakes to a particular point in a water course (normally a lake or a river confluence).

Sub-unit [Operational definition. Not in the WFD]: Reporting unit. River basin districts larger than 50000 square kilometre should be divided into comparable sub-units with an area between 5000 and 50000 square kilometre. The sub-units should be created using river basins (if more than one river basin exists in the RBD), set of contiguous river basins, or sub-basins, for example. If the RBD area is less than 50000 square kilometre, the RBD itself should be used as a sub-unit.

Surface water body: Body of surface water means a discrete and significant element of surface water such as a lake, a reservoir, a stream, river or canal, part of a stream, river or canal, a transitional water or a stretch of coastal water.

Surface water: Inland waters, except groundwater; transitional waters and coastal waters, except in respect of chemical status for which it shall also include territorial waters.

Inland water: All standing or flowing water on the surface of the land, and all groundwater on the landward side of the baseline from which the breadth of territorial waters is measured.

River: Body of inland water flowing for the most part on the surface of the land but which may flow underground for part of its course.

Lake: Body of standing inland surface water.

Transitional waters: Bodies of surface water in the vicinity of river mouths which are partly saline in character as a result of their proximity to coastal waters but which are substantially influenced by freshwater flows.

Coastal water: Surface water on the landward side of a line, every point of which is at a distance of one nautical mile on the seaward side from the nearest point of the baseline from which the breadth of territorial waters is measured, extending where appropriate up to the outer limit of transitional waters.

Territorial sea: The territorial waters, or territorial sea as defined by the 1982 United Nations Convention on the Law of the Sea, extend up to a limit not exceeding 12 nautical miles (22.2 km), measured from the baseline. The normal baseline is the low-water line along the coast.

Territorial waters [Operational definition. Not in WFD.]: Reporting unit. The zone between the limit of the coastal water bodies and the limit of the territorial sea, geometrically subdivided in Thiessen polygons according to the adjacent coastal sub-unit (or using any alternative delineation provided by the national competent authorities), and assigned to an adjacent sub-unit for the purpose of reporting the chemical status of the territorial waters under the Water Framework Directive.

Groundwater body: 'Body of groundwater' means a distinct volume of groundwater within an aquifer or aquifers.

Groundwater: All water which is below the surface of the ground in the saturation zone and in direct contact with the ground or subsoil. Aquifer: Subsurface layer or layers of rock or other geological strata of sufficient porosity and permeability to allow either a significant flow of groundwater or the abstraction of significant quantities of groundwater.

Monitoring site: [Operational definition. Not in the WFD] Monitoring point included in a WFD surveillance, operational or investigative monitoring programme.

SafeGraph Places provides baseline information for every record in the SafeGraph product suite via the Places schema and polygon information when applicable via the Geometry schema. The current scope of a place is defined as any location humans can visit with the exception of single-family homes. This definition encompasses a diverse set of places ranging from restaurants, grocery stores, and malls; to parks, hospitals, museums, offices, and industrial parks. Premium sets of Places include apartment buildings, Parking Lots, and Point POIs (such as ATMs or transit stations).

SafeGraph Places is a point of interest (POI) data offering with varying coverage depending on the country. Note that address conventions and formatting vary across countries. SafeGraph has coalesced these fields into the Places schema.

SafeGraph provides clean and accurate geospatial datasets on 51M+ physical places/points of interest (POI) globally. Hundreds of industry leaders like Mapbox, Verizon, Clear Channel, and Esri already rely on SafeGraph POI data to unlock business insights and drive innovation.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

This is a polygon coverage representing the wetlands cited in the "A Directory of Important Wetlands in Australia" Third Edition (EA, 2001), plus various additions for wetlands listed after 2001. This dataset includes attribute information showing the wetlands type and criteria for listing for each wetland.

The public version which does not include wetland type is available for download at https://data.gov.au/data/dataset/6636846e-e330-4110-afbb-7b89491fe567

This coverage is a compilation of various data sources and has been collected using a variety of methods. This dataset should therefore be used as an indicative guide only to wetland boundaries and locations. The data has been collated by the Australian Government Department of the Environment from various datasets including those supplied by the relevant State agencies.

Attributes in the dataset include:

WNAME: the name of the wetland site as listed in the Directory.

REFCODE: an individual reference number including a cross reference to the State in which it occurs. The first 2-3 characters relate to the State or Territory of origin followed by the 3 digit sequential wetland numeric code. (eg. "NSW001": NSW=New South Wales; 001=wetland number).

WET_TYPE: The wetland type code. Definitions are shown below.

CRITERIA: The criteria for listing code. Definitions are shown below.

WETLAND TYPE CODES:

A-Marine and Coastal Zone wetlands

1. Marine waters-permanent shallow waters less than six metres deep at low tide; includes sea bays, straits

2. Subtidal aquatic beds; includes kelp beds, seagrasses, tropical marine meadows

3. Coral reefs

4. Rocky marine shores; includes rocky offshore islands, sea cliffs

5. Sand, shingle or pebble beaches; includes sand bars, spits, sandy islets

6. Estuarine waters; permanent waters of estuaries and estuarine systems of deltas

7. Intertidal mud, sand or salt flats

8. Intertidal marshes; includes saltmarshes, salt meadows, saltings, raised salt marshes, tidal brackish and freshwater marshes

9. Intertidal forested wetlands; includes mangrove swamps, nipa swamps, tidal freshwater swamp forests

10. Brackish to saline lagoons and marshes with one or more relatively narrow connections with the sea

11. Freshwater lagoons and marshes in the coastal zone

12. Non-tidal freshwater forested wetlands

B-Inland wetlands

1. Permanent rivers and streams; includes waterfalls

2. Seasonal and irregular rivers and streams

3. Inland deltas (permanent)

4. Riverine floodplains; includes river flats, flooded river basins, seasonally flooded grassland, savanna and palm savanna

5. Permanent freshwater lakes (> 8 ha); includes large oxbow lakes

6. Seasonal/intermittent freshwater lakes (> 8 ha), floodplain lakes

7. Permanent saline/brackish lakes

8. Seasonal/intermittent saline lakes

9. Permanent freshwater ponds ( 8 ha)

10. Ponds, including farm ponds, stock ponds, small tanks (generally

Purpose

State agency contributors include the Queensland Environmental Protection Authority, NSW Department of Environment and Conservation and the Victorian Department of Sustainability and Environment.

For the identification of wetland boundaries or locations in regard to the compliance of activities with relevant State legislation, the relevant State authority should be contacted to obtain the most recent and accurate wetland boundary information available.

The criteria for the definition of a wetland used in this dataset is that adopted by the Ramsar Convention, namely: "areas of marsh, fen, peatland or water, whether natural or artificial, permanent or temporary, with water that is static or flowing, fresh, brackish or salt, including areas of marine water the depth of which at low tide does not exceed six meters."

Dataset History

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

This version of the Directory of Important Wetlands has been developed to include information regarding wetland type and criteria, which allows greater access to wetland information for non-technical users of this dataset. This version of the Directory contains the same wetland boundaries as the full dataset maintained by the Department, but has been dissolved to a multipart polygon coverage on fileds WNAME (Wetland Name) and REFCODE (Reference Code). This has reduced the total number of records in the attribute table from around 30 000 to around 900.

The wetland type and criteria for listing codes have been added to the attribute table to allow users of this dataset to identify important ecological characteristics of each wetland without having to seperately refer to the full Directory of Important Wetlands textual database maintained within the Department as an oracle database.

The full textual database giving further detals on each wetland can be accessed on the internet at http://www.environment.gov.au/water/wetlands/database/index.html

The coverage has been largely derived from the TOPO250K.WATERBOD coverage (AUSLIG, 1992).

A significant portion of some of the wetland boundaries for each state have also been supplied directly from the relevant state agencies. These include the QLD Environmental Protection Agency, NSW Department of Environment and Conservation, and the Victorian Department of Sustainability and Environment.

Data supplied from State agencies may have been collected using different collection methods. These may include remotely sensed images and digitized boundaries from topographic maps or aerial photos.

For the identification of wetland boundaries or locations in regard to the compliance of activities with relevant State legislation, the relevant State authority should be contacted to obtain the most recent and accurate boundary information available.

In general the accuracy of wetland boundaries may be taken to be approximately +/- 250m in most cases.

In considering the accuracy of the wetland boundaries, users of this data should be aware of the dynamic nature of wetland boundaries and their likelihood of experiencing significant alteration over time due to climatic conditions.

Additional datasets used in compiling the Directory of Important Wetlands spatial data include:

Collaborative Australian Protected Areas Database CAPAD (Version 2.0, 1998)

National Estate, Important Wetlands Tasmania (WETLANDSREP97)

BUFFERED CENTROIDS: derived from coordinates contained in the Wetlands Inventory 2nd Ed. (ANCA, 1996) & 3rd Ed. (EA, 2001).

Location of Cook Island Nature Reserve off the North NSW coast updated to match NSW Dept of Environment and Climat Change data.

Quality

Scope: Dataset

External accuracy:

+/ - 250m can be assumed for most cases.

This dataset should be used only to indicatively locate wetland boundaries or approximate locations.

The data has been compiled from various sources and therefore mapping methodologies for wetlands is variable across State jurisdictions and over time.

In considering the accuracy of the wetland boundaries, users of this data should be aware of the dynamic nature of wetland boundaries and their likelihood of experiencing significant alteration over time due to climatic conditions.

Non Quantitative accuracy:

Most refcode attributes have been checked against the Directory of Important Wetlands. Refcode accuracy assumed to be 99% correct.

Most wname attributes have been checked against the Directory of Important Wetlands. wname accuracy assumed to be 99% correct.

Conceptual consistency:

Wetland boundaries (minimum sample of 1 in 5) are visually compared to various datasources (e.g. streamlines and topographic data) to ensure approximate accuracy when updates are received by the Department.

All wetlands are attributed with a unique reference code (REFCODE).

Some wetlands may be located only by approximate centroid points until data updates are received.

NSW wetlands supplied t in March 2004 are derived from Kingsford et. al, 'The Distribution of Wetlands in New South Wales', NSW NPWS.

NSW wetlands supplied t in March 2004 have been checked by NSW DEC against 100K and 250K topo mapsheets, Auslig waterbody data (1994) and 25K topo maps and aerial photos along coastal areas. (see 2003, Kingsford et. al, 'The Distribution of Wetlands in New South Wales', NSW NPWS).

QLD wetlands supplied by QLD EPA in June 2005 have had polygons attributed as 'exclusions' removed from this dataset to ensure that unlabelled maps do not misrepresent the extent of Directory wetlands.

Attributes of WET_TYPE and CRITERIA correspond wiht those supplied from state agenceis and inclued in the online textual database of wetlands accessed at http://www.environment.gov.au/water/wetlands/database/index.html .

Completeness omission:

99% Complete as of August 2005, Coverage will be updated and rebuilt as States/Territories provide full wetland boundaries.

Dataset Citation

Department of the Environment (2010) Directory of Important Wetlands in Australia (DIWA) Spatial Database including Wetlands Type and Criteria. Bioregional Assessment Source Dataset. Viewed 31 May 2018, http://data.bioregionalassessments.gov.au/dataset/e0e4b50c-029b-4b7a-b0f8-592c24fc2ac9.

Connecticut Quaternary Geology Long Island Submerged Marine Fluvial-Estuarine, Channel-Fill Deposits identifies early postglacial, channel-fill deposits submerged in Long Island Sound and Fishers Island Sound. This information appears on Sheet 1 of the The Quaternary Geologic Map of Connecticut and Long Island Sound Basin (Stone and others, 2005). The Connecticut Quaternary Geology digital spatial data combines the information portrayed on the on-land portion of the Quaternary Geologic Map of Connecticut and Long Island Sound Basin (Stone and others 2005) with the information portrayed on its sister map, the Surficial Materials Map of Connecticut (Stone and others, 1992). When used together, these maps provide a three dimensional context for understanding and predicting the internal composition, resource potential and hydrologic character of Connecticut's glacial and postglacial deposits. Both were compiled at 1:24,000 scale, and published at 1:125,000 scale. The Quaternary Geologic Map of Connecticut and Long Island Sound Basin (Stone and others, 2005) portrays the glacial and postglacial deposits of Connecticut (including Long Island Sound) with an emphasis on where and how they were emplaced. Glacial Ice-Laid Deposits (thin till, thick till, and deposits of individual end moraines), Early Postglacial Deposits (Late Wisconsinan to Early Holocene stream terrace and inland dune deposits) and Holocene Postglacial Deposits (alluvium, swamp deposits, marsh deposits, beach and dune deposits, talus, and artificial fill) are differentiated from Glacial Meltwater Deposits. This mapping is based on the concept of systematic northward retreat of the Late Wisconsinan glacier. Meltwater deposits are divided into six depositional system categories (Deposits of Major Ice-Dammed Lakes, Deposits of Major Sediment-Dammed Lakes, Deposits of Related Series of Ice-Dammed Ponds, Deposits of Related Series of Sediment-Dammed Ponds, Deposits of Proximal Meltwater Streams, and Deposits of Distal Meltwater Streams) based on the arrangement and character of the groupings of sedimentary facies (morphosequences). The Surficial Materials Map of Connecticut (Stone and others, 1992) portrays the glacial and postglacial deposits of Connecticut in terms of their aerial extent and subsurface textural relationships. Glacial Ice-Laid Deposits (thin till, thick till, end moraine deposits) and Postglacial Deposits (alluvium, swamp deposits, marsh deposits, beach deposits, talus, and artificial fill) are differentiated from Glacial Meltwater Deposits. The meltwater deposits are further characterized using four texturally-based map units (g = gravel, sg = sand and gravel, s = sand, and f = fines). In many places a single map unit (e.g. sand) is sufficient to describe the entire meltwater section. Where more complex stratigraphic relationships exist, "stacked" map units are used to characterize the subsurface (e.g. sg/s/f - sand and gravel overlying sand overlying fines). Where postglacial deposits overlie meltwater deposits, this relationship is also described (e.g. alluvium overlying sand). Map unit definitions (Surficial Materials Polygon Code definitions, found in the metadata) provide a short description of the inferred depositional environment for each of the glacial meltwater map units. The geologic contacts between till and meltwater deposits coincide on both the Quaternary and Surficial Materials maps, as do the boundaries of polygons that define areas of thick till, alluvium, swamp deposits, marsh deposits, beach and dune deposits, talus, and artificial fill. Within the meltwater deposits, a Quaternary map unit (deposit) may contain several Surficial Materials textural units (akin to facies within a delta, for example). Combining the textural and vertical stacking information from the Surficial Materials map with the orderly portrayal of morphosequence relationships, up and down valley, that can be gleaned from the Quaternary map provides a three dimensional predictive context for relating the geologic setting of Connecticut's glacial meltwater deposits to their behavior as aquifers and/or transmitters of contaminants. Since this data layer is a polygon and line feature representation of the two maps combined, each map unit's depiction and description could provide information as to its aerial extent, subsurface textural characteristics, depositional and paleogeographic settings, and facies composition in a morphosequence context. Therefore, a typical meltwater polygon would have a combination of Quaternary (e.g. Deposit of Major Sediment-Dammed Lake; Glacial Lake Middletown Cromwell Deltaic Deposit) and Surficial Materials (e.g. sand and gravel overlying sand overlying fine) map attributes. Additional polygon features are incorporated to define surface water areas for streams, lakes, ponds, bays, and estuaries greater than 5 acres in size. Line features describe the type of boundary between individual geologic or textural units such as a geologic contact line between two different geologic units or a linear shoreline feature between a textural unit and an adjacent waterbody. The data have been updated to reflect minor changes in map unit name (QUPOLY_COD) for consistency with the 2005 publication of the Quaternary Geologic Map of Connecticut and Long Island Sound Basin. Previously distributed versions of CTQSGEOM were consistent with the 1998 Open-file Report for the same map. It is important to note that this data layer represents only the on-land portion of the Quaternary Geologic Map of Connecticut and Long Island Sound Basin (Stone and others, 2005). The off-shore geologic units are organized in separate data layers (LISQMOR, LISQFAN, LISQLAKE, LISQCHAN, LISQMARD) which can be used in conjunction with this data layer. These Long Island Sound layers have been mapped at 1:80,000 scale using seismic reflection data. The CTQSGEOM data layer should be used as the geologic base for Connecticut Quaternary Geology / Surficial Materials Features (CTQSFEAT) data layer which represents features such as eskers, meltwater channels, spillways, and locations of radio-carbon dated samples.

Connecticut Quaternary Geology Long Island Submerged Marine Deltaic Deposits identifies early postglacial, marine deltaic deposits submerged in Long Island Sound. This information appears on Sheet 1 of the The Quaternary Geologic Map of Connecticut and Long Island Sound Basin (Stone and others, 2005). The Connecticut Quaternary Geology digital spatial data combines the information portrayed on the on-land portion of the Quaternary Geologic Map of Connecticut and Long Island Sound Basin (Stone and others 2005) with the information portrayed on its sister map, the Surficial Materials Map of Connecticut (Stone and others, 1992). When used together, these maps provide a three dimensional context for understanding and predicting the internal composition, resource potential and hydrologic character of Connecticut's glacial and postglacial deposits. Both were compiled at 1:24,000 scale, and published at 1:125,000 scale. The Quaternary Geologic Map of Connecticut and Long Island Sound Basin (Stone and others, 2005) portrays the glacial and postglacial deposits of Connecticut (including Long Island Sound) with an emphasis on where and how they were emplaced. Glacial Ice-Laid Deposits (thin till, thick till, and deposits of individual end moraines), Early Postglacial Deposits (Late Wisconsinan to Early Holocene stream terrace and inland dune deposits) and Holocene Postglacial Deposits (alluvium, swamp deposits, marsh deposits, beach and dune deposits, talus, and artificial fill) are differentiated from Glacial Meltwater Deposits. This mapping is based on the concept of systematic northward retreat of the Late Wisconsinan glacier. Meltwater deposits are divided into six depositional system categories (Deposits of Major Ice-Dammed Lakes, Deposits of Major Sediment-Dammed Lakes, Deposits of Related Series of Ice-Dammed Ponds, Deposits of Related Series of Sediment-Dammed Ponds, Deposits of Proximal Meltwater Streams, and Deposits of Distal Meltwater Streams) based on the arrangement and character of the groupings of sedimentary facies (morphosequences). The Surficial Materials Map of Connecticut (Stone and others, 1992) portrays the glacial and postglacial deposits of Connecticut in terms of their aerial extent and subsurface textural relationships. Glacial Ice-Laid Deposits (thin till, thick till, end moraine deposits) and Postglacial Deposits (alluvium, swamp deposits, marsh deposits, beach deposits, talus, and artificial fill) are differentiated from Glacial Meltwater Deposits. The meltwater deposits are further characterized using four texturally-based map units (g = gravel, sg = sand and gravel, s = sand, and f = fines). In many places a single map unit (e.g. sand) is sufficient to describe the entire meltwater section. Where more complex stratigraphic relationships exist, "stacked" map units are used to characterize the subsurface (e.g. sg/s/f - sand and gravel overlying sand overlying fines). Where postglacial deposits overlie meltwater deposits, this relationship is also described (e.g. alluvium overlying sand). Map unit definitions (Surficial Materials Polygon Code definitions, found in the metadata) provide a short description of the inferred depositional environment for each of the glacial meltwater map units. The geologic contacts between till and meltwater deposits coincide on both the Quaternary and Surficial Materials maps, as do the boundaries of polygons that define areas of thick till, alluvium, swamp deposits, marsh deposits, beach and dune deposits, talus, and artificial fill. Within the meltwater deposits, a Quaternary map unit (deposit) may contain several Surficial Materials textural units (akin to facies within a delta, for example). Combining the textural and vertical stacking information from the Surficial Materials map with the orderly portrayal of

OverviewThe Jurisdictional Units dataset outlines wildland fire jurisdictional boundaries for federal, state, and local government entities on a national scale and is used within multiple wildland fire systems including the Wildland Fire Decision Support System (WFDSS), the Interior Fuels and Post-Fire Reporting System (IFPRS), the Interagency Fuels Treatment Decision Support System (IFTDSS), the Interagency Fire Occurrence Reporting Modules (InFORM), the Interagency Reporting of Wildland Fire Information System (IRWIN), and the Wildland Computer-Aided Dispatch Enterprise System (WildCAD-E).In this dataset, agency and unit names are an indication of the primary manager’s name and unit name, respectively, recognizing that:There may be multiple owner names.Jurisdiction may be held jointly by agencies at different levels of government (ie State and Local), especially on private lands, Some owner names may be blocked for security reasons.Some jurisdictions may not allow the distribution of owner names. Private ownerships are shown in this layer with JurisdictionalUnitIID=null, JurisdictionalKind=null, and LandownerKind="Private", LandownerCategory="Private". All land inside the US country boundary is covered by a polygon.Jurisdiction for privately owned land varies widely depending on state, county, or local laws and ordinances, fire workload, and other factors, and is not available in a national dataset in most cases.For publicly held lands the agency name is the surface managing agency, such as Bureau of Land Management, United States Forest Service, etc. The unit name refers to the descriptive name of the polygon (i.e. Northern California District, Boise National Forest, etc.).AttributesField NameDefinitionGeometryIDPrimary key for linking geospatial objects with other database systems. Required for every feature. Not populated for Census Block Groups.JurisdictionalUnitIDWhere it could be determined, this is the NWCG Unit Identifier (Unit ID). Where it is unknown, the value is ‘Null’. Null Unit IDs can occur because a unit may not have a Unit ID, or because one could not be reliably determined from the source data. Not every land ownership has an NWCG Unit ID. Unit ID assignment rules are available in the Unit ID standard.JurisdictionalUnitID_sansUSNWCG Unit ID with the "US" characters removed from the beginning. Provided for backwards compatibility.JurisdictionalUnitNameThe name of the Jurisdictional Unit. Where an NWCG Unit ID exists for a polygon, this is the name used in the Name field from the NWCG Unit ID database. Where no NWCG Unit ID exists, this is the “Unit Name” or other specific, descriptive unit name field from the source dataset. A value is populated for all polygons except for Census Blocks Group and for PAD-US polygons that did not have an associated name.LocalNameLocal name for the polygon provided from agency authoritative data, PAD-US, or other source.JurisdictionalKindDescribes the type of unit jurisdiction using the NWCG Landowner Kind data standard. There are two valid values: Federal, Other, and Private. A value is not populated for Census Block Groups.JurisdictionalCategoryDescribes the type of unit jurisdiction using the NWCG Landowner Category data standard. Valid values include: BIA, BLM, BOR, DOD, DOE, NPS, USFS, USFWS, Foreign, Tribal, City, County, State, OtherLoc (other local, not in the standard), Private, and ANCSA. A value is not populated for Census Block Groups.LandownerKindThe landowner kind value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. Legal values align with the NWCG Landowner Kind data standard. A value is populated for all polygons.LandownerCategoryThe landowner category value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. Legal values align with the NWCG Landowner Category data standard. A value is populated for all polygons.LandownerDepartmentFederal department information that aligns with a unit’s landownerCategory information. Legal values include: Department of Agriculture, Department of Interior, Department of Defense, and Department of Energy. A value is not populated for all polygons.DataSourceThe database from which the polygon originated. An effort is made to be as specific as possible (i.e. identify the geodatabase name and feature class in which the polygon originated).SecondaryDataSourceIf the DataSource field is an aggregation from other sources, use this field to specify the source that supplied data to the aggregation. For example, if DataSource is "PAD-US 4.0", then for a TNC polygon, the SecondaryDataSource would be " TNC_PADUS2_0_SA2015_Public_gdb ".SourceUniqueIDIdentifier (GUID or ObjectID) in the data source. Used to trace the polygon back to its authoritative source.DataSourceYearYear that the source data for the polygon were acquired.MapMethodControlled vocabulary to define how the geospatial feature was derived. MapMethod will be Mixed Methods by default for this layer as the data are from mixed sources. Valid Values include: GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; DigitizedTopo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; Phone/Tablet; Other.DateCurrentThe last edit, update, of this GIS record. Date should follow the assigned NWCG Date Time data standard, using the 24-hour clock, YYYY-MM-DDhh.mm.ssZ, ISO8601 Standard.CommentsAdditional information describing the feature.JoinMethodAdditional information on how the polygon was matched to information in the NWCG Unit ID database.LegendJurisdictionalCategoryJurisdictionalCategory values grouped for more intuitive use in a map legend or summary table. Census Block Groups are classified as “No Unit”.LegendLandownerCategoryLandownerCategory values grouped for more intuitive use in a map legend or summary table.Other Relevant NWCG Definition StandardsUnitA generic term that represents an organizational entity that only has meaning when it is contextualized by a descriptor, e.g. jurisdictional.Definition Extension: When referring to an organizational entity, a unit refers to the smallest area or lowest level. Higher levels of an organization (region, agency, department, etc.) can be derived from a unit based on organization hierarchy.Unit, JurisdictionalThe governmental entity having overall land and resource management responsibility for a specific geographical area as provided by law.Definition Extension: 1) Ultimately responsible for the fire report to account for statistical fire occurrence; 2) Responsible for setting fire management objectives; 3) Jurisdiction cannot be re-assigned by agreement; 4) The nature and extent of the incident determines jurisdiction (for example, Wildfire vs. All Hazard); 5) Responsible for signing a Delegation of Authority to the Incident Commander.See also: Protecting Unit; LandownerData SourcesThis dataset is an aggregation of multiple spatial data sources: • Authoritative land ownership records from BIA, BLM, NPS, USFS, USFWS, and the Alaska Fire Service/State of Alaska• The Protected Areas Database US (PAD-US 4.0)• Census Block-Group Geometry BIA and Tribal Data:BIA and Tribal land management data were aggregated from BIA regional offices. These data date from 2012 and were reviewed/updated in 2024. Indian Trust Land affiliated with Tribes, Reservations, or BIA Agencies: These data are not considered the system of record and are not intended to be used as such. The Bureau of Indian Affairs (BIA), Branch of Wildland Fire Management (BWFM) is not the originator of these data. The spatial data coverage is a consolidation of the best available records/data received from each of the 12 BIA Regional Offices. The data are no better than the original sources from which they were derived. Care was taken when consolidating these files. However, BWFM cannot accept any responsibility for errors, omissions, or positional accuracy in the original digital data. The information contained in these data is dynamic and is continually changing. Updates to these data will be made whenever such data are received from a Regional Office. The BWFM gives no guarantee, expressed, written, or implied, regarding the accuracy, reliability, or completeness of these data.Alaska:The state of Alaska and Alaska Fire Service (BLM) co-manage a process to aggregate authoritative land ownership, management, and jurisdictional boundary data, based on Master Title Plats. Data ProcessingTo compile this dataset, the authoritative land ownership records and the PAD-US data mentioned above were crosswalked into the Jurisdictional Unit Polygon schema and aggregated through a series of python scripts and FME models. Once aggregated, steps were taken to reduce overlaps within the data. All overlap areas larger than 300 acres were manually examined and removed with the assistance of fire management SMEs. Once overlaps were removed, Census Block Group geometry were crosswalked to the Jurisdictional Unit Polygon schema and appended in areas in which no jurisdictional boundaries were recorded within the authoritative land ownership records and the PAD-US data. Census Block Group geometries represent areas of unknown Landowner Kind/Category and Jurisdictional Kind/Category and were assigned LandownerKind and LandownerCategory values of "Private".Update FrequencyThe Authoritative land ownership records and PAD-US data used to compile this dataset are dynamic and are continually changing. Major updates to this dataset will be made once a year, and minor updates will be incorporated throughout the year as needed. New to the Latest Release (1/15/25)Now pulling from agency authoritative sources for BLM, NPS, USFS, and USFWS (instead of getting this data from PADUS).

Field Name Changes