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

This dataset is built for time-series Sentinel-2 cloud detection and stored in Tensorflow TFRecord (refer to https://www.tensorflow.org/tutorials/load_data/tfrecord).

Each file is compressed in 7z format and can be decompressed using Bandzip or 7-zip software.

Dataset Structure:

Each filename can be split into three parts using underscores. The first part indicates whether it is designated for training or validation ('train' or 'val'); the second part indicates the Sentinel-2 tile name, and the last part indicates the number of samples in this file.

For each sample, it includes:

1. Sample ID;
2. Array of time series 4 band image patches in 10m resolution, shaped as (n_timestamps, 4, 42, 42);
3. Label list indicating cloud cover status for the center \(6\times6\) pixels of each timestamp;
4. Ordinal list for each timestamp;
5. Sample weight list (reserved);

Here is a demonstration function for parsing the TFRecord file:

import tensorflow as tf

# init Tensorflow Dataset from file name
def parseRecordDirect(fname):
  sep = '/'
  parts = tf.strings.split(fname,sep)
  tn = tf.strings.split(parts[-1],sep='_')[-2]
  nn = tf.strings.to_number(tf.strings.split(parts[-1],sep='_')[-1],tf.dtypes.int64)
  t = tf.data.Dataset.from_tensors(tn).repeat().take(nn)
  t1 = tf.data.TFRecordDataset(fname)
  ds = tf.data.Dataset.zip((t, t1))
  return ds

keys_to_features_direct = {
  'localid': tf.io.FixedLenFeature([], tf.int64, -1),
  'image_raw_ldseries': tf.io.FixedLenFeature((), tf.string, ''),
  'labels': tf.io.FixedLenFeature((), tf.string, ''),
  'dates': tf.io.FixedLenFeature((), tf.string, ''),
  'weights': tf.io.FixedLenFeature((), tf.string, '')
    }

# The Decoder (Optional)
class SeriesClassificationDirectDecorder(decoder.Decoder):
 """A tf.Example decoder for tfds classification datasets."""
 def _init_(self) -> None:
  super()._init_()

 def decode(self, tid, ds):
  parsed = tf.io.parse_single_example(ds, keys_to_features_direct)
  encoded = parsed['image_raw_ldseries']
  labels_encoded = parsed['labels']
  decoded = tf.io.decode_raw(encoded, tf.uint16)
  label = tf.io.decode_raw(labels_encoded, tf.int8)
  dates = tf.io.decode_raw(parsed['dates'], tf.int64)
  weight = tf.io.decode_raw(parsed['weights'], tf.float32)
  decoded = tf.reshape(decoded,[-1,4,42,42])
  sample_dict = {
   'tid': tid, # tile ID
   'dates': dates, # Date list
   'localid': parsed['localid'], # sample ID
   'imgs': decoded, # image array
   'labels': label, # label list
   'weights': weight
  }
  return sample_dict

# simple function 
def preprocessDirect(tid, record):
  parsed = tf.io.parse_single_example(record, keys_to_features_direct)
  encoded = parsed['image_raw_ldseries']
  labels_encoded = parsed['labels']
  decoded = tf.io.decode_raw(encoded, tf.uint16)
  label = tf.io.decode_raw(labels_encoded, tf.int8)
  dates = tf.io.decode_raw(parsed['dates'], tf.int64)
  weight = tf.io.decode_raw(parsed['weights'], tf.float32)
  decoded = tf.reshape(decoded,[-1,4,42,42])
  return tid, dates, parsed['localid'], decoded, label, weight

t1 = parseRecordDirect('filename here')
dataset = t1.map(preprocessDirect, num_parallel_calls=tf.data.experimental.AUTOTUNE)

#

Class Definition:

• 0: clear
• 1: opaque cloud
• 2: thin cloud
• 3: haze
• 4: cloud shadow
• 5: snow

Dataset Construction:

First, we randomly generate 500 points for each tile, and all these points are aligned to the pixel grid center of the subdatasets in 60m resolution (eg. B10) for consistence when comparing with other products.
It is because that other cloud detection method may use the cirrus band as features, which is in 60m resolution.

Then, the time series image patches of two shapes are cropped with each point as the center.
The patches of shape \(42 \times 42\) are cropped from the bands in 10m resolution (B2, B3, B4, B8) and are used to construct this dataset.
And the patches of shape \(348 \times 348\) are cropped from the True Colour Image (TCI, details see sentinel-2 user guide) file and are used to interpreting class labels.

The samples with a large number of timestamps could be time-consuming in the IO stage, thus the time series patches are divided into different groups with timestamps not exceeding 100 for every group.

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

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

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

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

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.

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

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/ .

Modification of river corridors, particularly deforestation and the removal of large wood, has greatly altered the abundance and influences of large wood on most rivers in the temperate latitudes. The conceptual framework of large wood process domains can assist in both directing research and facilitating large wood-related management and restoration in rivers. Large wood process domains are spatially or temporally distinct portions of a river network or region with distinct processes of wood recruitment, transport, and storage. Previous research has shown wood to be unevenly distributed across space and time. We use a dataset of logjam distribution density in 304 spatially distinct reaches of mountain streams in the Colorado Front Range, and up to 11 years of repeat measurements at some reaches, to (i) statistically evaluate whether a priori designated process domains for logjam distribution density are distinctly different and (ii) evaluate the sensitivity of process domain delineatio..., We analyzed data using R software and packages (R Core Team, 2022). Statistical differences between population medians were assessed with t-tests and the Wilcoxon Rank Sum test when data did not meet the assumptions of normality or equal variance. We used multivariate linear models to describe relationships between spatial characteristics and logjam storage. To select the best variables to describe logjam density, we used the MuMin package in R (Barton, 2018). We explored using a mixed model to reflect the nested nature of the data, but opted for simple regression models to more easily facilitate model iteration. Statistical tests were assessed at the α = 0.05 level for significance.

To address the influence of the number of reaches on the conclusions, we created subsets of the spatially extensive dataset by randomly resampling the complete spatial dataset (n = 304) to create subsets of data of different sample sizes. One hundred random subsamples of the complete spatial dataset were t..., # Data from: Evaluating the sensitivity of process domains for logjams to spatial and temporal sample size in river networks of the Southern Rockies, USA

Data describing logjam distribution density (number of jams per 100 m of channel length) for the Southern Rocky Mountains. Each reach was previously defined. Data were compiled from Beckman (2013), Jackson and Wohl (2015), Livers (2016), Triantafillou & Wohl (2024), Wohl and Beckman (2014), Wohl and Jaeger (2009), Wohl and Scamardo (2020), Wohl and Iskin (2022), and Wohl (unpublished data).

Description of the data and file structure

spatial.csv: A csv with the spatially extensive data. Data cover 304 reaches from 38 catchments.

spatial_key.csv: A table with variable definitions associated with 'spatial.csv,' including category, variable name, units if applicable, description of methods, and format.

temporal.csv: A csv with the temporally extensive data. Data are from the North Saint Vrain catchment and its tributaries and cover...,

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

The high spatial resolution and century-long Standardized Precipitation Evapotranspiration Index (SPEI) dataset with a spatial resolution of 0.0083 degrees (~1 km) was spatially downscaled from the global SPEI data with a 0.5 degrees spatial resolution (https://spei.csic.es/database.html) based on machine learning integrated with high spatial resolution climatic and topographic variables. The 1-km SPEI datasets are across the land areas of China from January 1901 to December 2020, including 1-month, 3-month, 6-month and 12-month SPEIs. The unit of the data is 0.01. The dataset was evaluated using the root zone soil moisture and the historical drought events, and the evaluation indicated that the high spatial resolution SPEI dataset is reliable.

Data Information:

GPRChinaSPEI1km: High spatial resolution and century-long SPEI datasets over China from 1901 to 2020 generated by machine learning

Publication:

He, Q., Wang, M., Liu, K., & Wang, B. (2025). High-resolution Standardized Precipitation Evapotranspiration Index (SPEI) reveals trends in drought and vegetation water availability in China. Geography and Sustainability, 6(2), 100228. https://doi.org/10.1016/j.geosus.2024.08.007

----------------------------------------------------data description---------------------------------------------

This is a gridded dataset for the Standardized Precipitation Evapotranspiration Index (SPEI) at a spatial resolution of 1 km over the main terrestrial lands of China for each month during 1901-2020, which is generated using the Gaussian process regression (GPR) based on the Global SPEI database (https://spei.csic.es/database.html) integrated with high spatial resolution climatic and topographic variables. Four timescales of SPEI were generated: 1-month (SPEI-1), 3-month (SPEI-3), 6-month (SPEI-6) and 12-month (SPEI-12). The details are as follows:

Region: China

Temporal Extent: January 1901 to December 2020

Spatial resolution: 0.0083° (~1 km)

Temporal resolution: month

Timescales: 1-month, 3-month, 6-month and 12-month

Data format: GeoTIFF

Unit: unitless (0.01)

Geographic coordinate system: WGS 1984

---------------------------------------------------dataset filename---------------------------------------------

The file name specifically shows the data information.

For example,

“SPEI_1_2020_1.tif” means “1-month SPEI of January 2020”.

“SPEI_3_2020_1.tif” means “3-month SPEI of January 2020”.

All the file names are formatted in “SPEI_timescale_year_month”

timescale: 1, 3, 6 and 12 indicate 1-month, 3-month, 6-month and 12-month, respectively

year: from 1901 to 2020

month: from 1 to 12

--------------------------------------------------storage information-------------------------------------------

The high-resolution SPEI dataset is stored in TIFF format using WGS 1984 coordinate system. The data type is int16 with a scale factor of 0.01. The nodata value is -32768. The dataset requires multiplication by 0.01 during application to obtain the actual value ranges.

The data were compressed into .rar format every 10 years for each timescale SPEI.

This is the data and code related to paperEnhancing Spatial Count Data Modeling: A new method for Poisson Means of Stratified NonhomogeneityAbstract: Spatial count data is a prevalent data type in natural and social sciences. As the data present complicated spatial autocorrelation and heterogeneity inherent in geographical analysis, current methods lack a theoretical approach to model and predict the count data, especially with limited spatial samples. To address the gap, this study develops a new method named Poisson Means of Stratified Nonhomogeneity (PoiMSN). It theoretically considers both autocorrelation and heterogeneity, and without any covariate, incorporates local samples and out-stratum neighbors that traditional methods neglected, to accurately model and predict the latent process for Poisson distributed data. PoiMSN, compared to Poisson geostatistics and traditional MSN, was validated by simulation. It demonstrated superior performance, achieving the lowest mean absolute error and root-mean-squared error, with at least 5% improvement in accuracy for autocorrelated and stratified Poisson data. The application to hand, foot, mouth disease data showed PoiMSN could precisely map the disease risks with lower uncertainty. PoiMSN has the ability to accommodate autocorrelated and heterogeneous statistical population and leverage extensive sample information, substantiating its theoretical and empirical superiority in spatially non-stationary count data.

We include a description of the data sets in the meta-data as well as sample code and results from a simulated data set. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: The R code is available on line here: https://github.com/warrenjl/SpGPCW. Format: Abstract The data used in the application section of the manuscript consist of geocoded birth records from the North Carolina State Center for Health Statistics, 2005-2008. In the simulation study section of the manuscript, we simulate synthetic data that closely match some of the key features of the birth certificate data while maintaining confidentiality of any actual pregnant women. Availability Due to the highly sensitive and identifying information contained in the birth certificate data (including latitude/longitude and address of residence at delivery), we are unable to make the data from the application section publicly available. However, we will make one of the simulated datasets available for any reader interested in applying the method to realistic simulated birth records data. This will also allow the user to become familiar with the required inputs of the model, how the data should be structured, and what type of output is obtained. While we cannot provide the application data here, access to the North Carolina birth records can be requested through the North Carolina State Center for Health Statistics and requires an appropriate data use agreement. Description Permissions: These are simulated data without any identifying information or informative birth-level covariates. We also standardize the pollution exposures on each week by subtracting off the median exposure amount on a given week and dividing by the interquartile range (IQR) (as in the actual application to the true NC birth records data). The dataset that we provide includes weekly average pregnancy exposures that have already been standardized in this way while the medians and IQRs are not given. This further protects identifiability of the spatial locations used in the analysis. File format: R workspace file. Metadata (including data dictionary) • y: Vector of binary responses (1: preterm birth, 0: control) • x: Matrix of covariates; one row for each simulated individual • z: Matrix of standardized pollution exposures • n: Number of simulated individuals • m: Number of exposure time periods (e.g., weeks of pregnancy) • p: Number of columns in the covariate design matrix • alpha_true: Vector of “true” critical window locations/magnitudes (i.e., the ground truth that we want to estimate). This dataset is associated with the following publication: Warren, J., W. Kong, T. Luben, and H. Chang. Critical Window Variable Selection: Estimating the Impact of Air Pollution on Very Preterm Birth. Biostatistics. Oxford University Press, OXFORD, UK, 1-30, (2019).

Access API Administrative Boundaries Theme - Parish Please Note WGS 84 service aligned to GDA94 This dataset has spatial reference [WGS 84 ≈ GDA94] which may result in misalignments when viewed in GDA2020 environments. A similar service with a ‘multiCRS’ suffix is available which can support GDA2020, GDA94 and WGS 84 ≈ GDA2020 environments. In due course, and allowing time for user feedback and testing, it is intended that the original service name will adopt the new multiCRS functionality. Metadata Portal Metadata InformationContent TitleNSW Administrative Boundaries Theme - Mines Subsidence DistrictContent TypeHosted Feature LayerDescriptionNSW Parish is a dataset within the Administrative Boundaries Theme of the FSDF. It contains 7,459 administrative areas (Parishes) formed by the division of 141 counties. Counties and parishes are administrative divisions of the state and are not separately disposable land parcels. County and Parish are historical layers and the information contained on these layers was gathered from Parish and County maps which are now held at State Records (digital versions can be accessed through the Historical Lands Records Viewer). However, they can be updated (if necessary) after a title inspection.Parishes are divided into separately land parcels called “portions”, these being the common basic units of land disposed of by the Crown (sold), held in occupation (leased) or reserved for public purposes. Other basic units are section and allotments in Towns and Villages. The dataset contains county and parish names. Any changes that occur to the dataset should have a reference in the authority of reference feature class in the lot and property data sets.Features are positioned in topological alignment within the extents of the land and property polygons for each county and are held in alignment, including changes resulting cadastral maintenance and upgrades. NSW Parish is a subset of NSW County.This dataset contains an historical land administration boundary. The original Parish definition is static, however, data will move with changes to the Land Parcel and Property theme.Initial Publication Date05/02/2020Data Currency01/01/3000Data Update FrequencyOtherContent SourceData provider filesFile TypeESRI File Geodatabase (*.gdb)Attribution© State of New South Wales (Spatial Services, a business unit of the Department of Customer Service NSW). For current information go to spatial.nsw.gov.auData Theme, Classification or Relationship to other DatasetsNSW Administrative Boundaries Theme of the Foundation Spatial Data Framework (FSDF)AccuracyThe dataset maintains a positional relationship to, and alignment with, the Lot and Property digital datasets. This dataset was captured by digitising the best available cadastral mapping at a variety of scales and accuracies, ranging from 1:500 to 1:250 000 according to the National Mapping Council of Australia, Standards of Map Accuracy (1975). Therefore, the position of the feature instance will be within 0.5mm at map scale for 90% of the well-defined points. That is, 1:500 = 0.25m, 1:2000 = 1m, 1:4000 = 2m, 1:25000 = 12.5m, 1:50000 = 25m and 1:100000 = 50m. A program to upgrade the spatial location and accuracy of data is ongoing.Spatial Reference System (dataset)GDA94Spatial Reference System (web service)EPSG:4326WGS84 Equivalent ToGDA94Spatial ExtentFull StateContent LineageFor additional information, please contact us via the Spatial Services Customer HubData ClassificationUnclassifiedData Access PolicyOpenData QualityFor additional information, please contact us via the Spatial Services Customer HubTerms and ConditionsCreative CommonsStandard and SpecificationOpen Geospatial Consortium (OGC) implemented and compatible for consumption by common GIS platforms. Available as either cache or non-cache, depending on client use or requirement.Information about the Feature Class and Domain Name descriptions for the NSW Administrative Boundaries Theme can be found in the NSW Cadastral Data Dictionary.Some of Spatial Services Datasets are designed to work together for example NSW Address Point and NSW Address String (table), NSW Property (Polygon) and NSW Property Lot (table) and NSW Lot (polygons). To do this you need to add a Spatial Join.A Spatial Join is a GIS operation that affixes data from one feature layer’s attribute table to another from a spatial perspective.To see how NSW Address, Property, Lot Geometry data and tables can be spatially joined, download the Data Model Document. Data CustodianDCS Spatial Services346 Panorama AveBathurst NSW 2795Point of ContactPlease contact us via the Spatial Services Customer HubData AggregatorDCS Spatial Services346 Panorama AveBathurst NSW 2795Data DistributorDCS Spatial Services346 Panorama AveBathurst NSW 2795Additional Supporting InformationData DictionariesData Model Document. TRIM Number

In recent years, formalization and reasoning of topological relations have become a hot topic as a means to generate knowledge about the relations between spatial objects at the conceptual and geometrical levels. These mechanisms have been widely used in spatial data query, spatial data mining, evaluation of equivalence and similarity in a spatial scene, as well as for consistency assessment of the topological relations of multi-resolution spatial databases. The concept of computational fuzzy topological space is applied to simple fuzzy regions to efficiently and more accurately solve fuzzy topological relations. Thus, extending the existing research and improving upon the previous work, this paper presents a new method to describe fuzzy topological relations between simple spatial regions in Geographic Information Sciences (GIS) and Artificial Intelligence (AI). Firstly, we propose a new definition for simple fuzzy line segments and simple fuzzy regions based on the computational fuzzy topology. And then, based on the new definitions, we also propose a new combinational reasoning method to compute the topological relations between simple fuzzy regions, moreover, this study has discovered that there are (1) 23 different topological relations between a simple crisp region and a simple fuzzy region; (2) 152 different topological relations between two simple fuzzy regions. In the end, we have discussed some examples to demonstrate the validity of the new method, through comparisons with existing fuzzy models, we showed that the proposed method can compute more than the existing models, as it is more expressive than the existing fuzzy models.

Access APIAdministrative Boundaries Theme - Parish Please Note WGS 84 service aligned to GDA94 This dataset has spatial reference [WGS 84 ≈ GDA94] which may result in misalignments when viewed in …Show full description Access APIAdministrative Boundaries Theme - Parish Please Note WGS 84 service aligned to GDA94 This dataset has spatial reference [WGS 84 ≈ GDA94] which may result in misalignments when viewed in GDA2020 environments. A similar service with a ‘multiCRS’ suffix is available which can support GDA2020, GDA94 and WGS 84 ≈ GDA2020 environments. In due course, and allowing time for user feedback and testing, it is intended that the original service name will adopt the new multiCRS functionally.NSW Parish is a dataset within the Administrative boundaries theme of the FSDF. It contains 7,459 administrative areas (Parishes) formed by the division of 141 counties. Counties and parishes are administrative divisions of the state and are not separately disposable land parcels. County and Parish are historical layers and the information contained on these layers was gathered from Parish and County maps which are now held at State Records (digital versions can be accessed through the Historical Lands Records Viewer). However, they can be updated (if necessary) after a title inspection. Parishes are divided into separately land parcels called “portions”, these being the common basic units of land disposed of by the Crown (sold), held in occupation (leased) or reserved for public purposes. Other basic units are section and allotments in Towns and Villages. The dataset contains county and parish names. Any changes that occur to the dataset should have a reference in the authority of reference feature class in the lot and property data sets. Features are positioned in topological alignment within the extents of the land and property polygons for each county and are held in alignment, including changes resulting cadastral maintenance and upgrades. NSW Parish is a subset of NSW County. This dataset contains an historical land administration boundary. The original Parish definition is static, however, data will move with changes to the Land Parcel and Property theme. Metadata Type Esri Feature Service Update Frequency As required Contact Details Contact us via the Spatial Services Customer Hub Relationship to Themes and Datasets Administrative Boundaries Theme of the Foundation Spatial Data Framework (FSDF) Accuracy The dataset maintains a positional relationship to, and alignment with, the Lot and Property digital datasets. This dataset was captured by digitising the best available cadastral mapping at a variety of scales and accuracies, ranging from 1:500 to 1:250 000 according to the National Mapping Council of Australia, Standards of Map Accuracy (1975). Therefore, the position of the feature instance will be within 0.5mm at map scale for 90% of the well-defined points. That is, 1:500 = 0.25m, 1:2000 = 1m, 1:4000 = 2m, 1:25000 = 12.5m, 1:50000 = 25m and 1:100000 = 50m. A program of positional upgrade (accuracy improvement) is currently underway. Spatial Reference System (dataset) Geocentric Datum of Australia 1994 (GDA94), Australian Height Datum (AHD) Spatial Reference System    (web service) EPSG 4326: WGS 84 Geographic 2D WGS 84 Equivalent To GDA94 Spatial Extent Full State Standards and Specifications Open Geospatial Consortium (OGC) implemented and compatible for consumption by common GIS platforms. Available as either cache or non-cache, depending on client use or requirement. Information about the Feature Class and Domain Name descriptions for the NSW Administrative Boundaries Theme can be found in the NSW Cadastral Delivery Model Data Dictionary Some of Spatial Services Datasets are designed to work together for example NSW Address Point and NSW Address String (table), NSW Property (Polygon) and NSW Property Lot (table) and NSW Lot (polygons). To do this you need to add a Spatial Join. A Spatial Join is a GIS operation that affixes data from one feature layer’s attribute table to another from a spatial perspective. To see how NSW Address, Property, Lot Geometry data and tables can be spatially joined, download the Data Model Document. Distributors Service Delivery, DCS Spatial Services 346 Panorama Ave Bathurst NSW 2795 Dataset Producers and Contributors Administrative Spatial Programs, DCS Spatial Services 346 Panorama Ave Bathurst NSW 2795

The dataset contains information on European groundwater bodies, monitoring sites, river basin districts, river basin districts sub-units and surface bodies reported to the European Environment Agency between 2001-11-29 and 2019-02-19.

The information was reported to the European Environment Agency under the State of Environment reporting obligations. For the EU28 countries and Norway, the EIONET spatial data was consolidated with the spatial data reported under the Water Framework Directive reporting obligations. For these countries, the reference spatial data set is the "WISE WFD Reference Spatial Datasets reported under Water Framework Directive".

Relevant concepts:

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. 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. River basin district: 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.

Export DataAccess APIAdministrative Boundaries Theme – Local Government AreaPlease Note WGS 84 service aligned to GDA94 This dataset has spatial reference [WGS 84 ≈ GDA94] which may result in misalignments when viewed in GDA2020 environments. A similar service with a ‘multiCRS’ suffix is available which can support GDA2020, GDA94 and WGS 84 ≈ GDA2020 environments. In due course, and allowing time for user feedback and testing, it is intended that the original service name will adopt the new multiCRS functionality.Metadata Portal Metadata InformationContent TitleNSW Administrative Boundaries Theme - Local Government AreaContent TypeHosted Feature LayerDescriptionNSW Local Government Area is a dataset within the Administrative Boundaries Theme (FSDF). It depicts polygons of gazetted boundaries defining the Local Government Area. It contains all of the cadastral line data or topographic features which are used to define the boundaries between adjoining shires, municipalities, cities (Local Government Act) and the unincorporated areas of NSW.The dataset also contains Council Names, ABS Codes, Ito Codes, Vg Codes, and Wb Codes. Any changes that occur to the dataset should have a reference in the authority of reference feature class in the Land Parcel and Property.Features are positioned in topological alignment within the extents of the land parcel and property polygons for each Local Government Area and are held in alignment, including changes resulting cadastral maintenance and upgrades.Initial Publication Date05/05/2020Data Currency01/01/3000Data Update FrequencyDailyContent SourceData provider filesFile TypeESRI File Geodatabase (*.gdb)Attribution© State of New South Wales (Spatial Services, a business unit of the Department of Customer Service NSW). For current information go to spatial.nsw.gov.auData Theme, Classification or Relationship to other DatasetsNSW Administrative Boundaries Theme of the Foundation Spatial Data Framework (FSDF)AccuracyThe dataset maintains a positional relationship to, and alignment with, the Lot and Property digital datasets. This dataset was captured by digitising the best available cadastral mapping at a variety of scales and accuracies, ranging from 1:500 to 1:250 000 according to the National Mapping Council of Australia, Standards of Map Accuracy (1975). Therefore, the position of the feature instance will be within 0.5mm at map scale for 90% of the well-defined points. That is, 1:500 = 0.25m, 1:2000 = 1m, 1:4000 = 2m, 1:25000 = 12.5m, 1:50000 = 25m and 1:100000 = 50m. A program to upgrade the spatial location and accuracy of data is ongoing.Spatial Reference System (dataset)GDA94Spatial Reference System (web service)EPSG:4326WGS84 Equivalent ToGDA94Spatial ExtentFull StateContent LineageFor additional information, please contact us via the Spatial Services Customer HubData ClassificationUnclassifiedData Access PolicyOpenData QualityFor additional information, please contact us via the Spatial Services Customer HubTerms and ConditionsCreative CommonsStandard and SpecificationOpen Geospatial Consortium (OGC) implemented and compatible for consumption by common GIS platforms. Available as either cache or non-cache, depending on client use or requirement.Information about the Feature Class and Domain Name descriptions for the NSW Administrative Boundaries Theme can be found in the NSW Cadastral Data Dictionary.Some of Spatial Services Datasets are designed to work together for example NSW Address Point and NSW Address String (table), NSW Property (Polygon) and NSW Property Lot (table) and NSW Lot (polygons). To do this you need to add a Spatial Join.A Spatial Join is a GIS operation that affixes data from one feature layer’s attribute table to another from a spatial perspective.To see how NSW Address, Property, Lot Geometry data and tables can be spatially joined, download the Data Model Document. Data CustodianDCS Spatial Services346 Panorama AveBathurst NSW 2795Point of ContactPlease contact us via the Spatial Services Customer HubData AggregatorDCS Spatial Services346 Panorama AveBathurst NSW 2795Data DistributorDCS Spatial Services346 Panorama AveBathurst NSW 2795Additional Supporting InformationData DictionariesData Model Document. TRIM Number