This layer shows workers by employer type (private sector, government, etc.) in Austin, Texas. This is shown by censustract and place boundaries. Tract data contains the most currently released American Community Survey (ACS) 5-year data for all tracts within Bastrop, Caldwell, Hays, Travis, and Williamson Counties in Texas. Place data contains the most recent ACS 1-year estimate for the City of Austin, Texas. Data contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023 (Tract), 2023 (Place)ACS Table(s): C24060 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: February 12, 2025National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

description: This data represents Travel Management Areas (TMA) used for travel planning efforts for the BLM Arizona State Office. Data contains information about status of route inventory, route evaluation, TMP and signage status. Dataset is in a File Geodatabase Feature Class format. The boundaries may be updated by BLM Staff as boundaries are refined.This feature class was created by combining the most recent and updated geometry for TMAs (edited by Sprint Contractor Ricardo Franco) and the best attributes available in the AZ Corporate Layers (cjallen - 4/13/17). Additional boundary edits and the creation of a new TMA (Prescott Metro) were completed with close guidance from Bill Gibson. TMA boundaries for Bumble Bee, Table Mesa, Lower Black Canyon Trail, Upper Agua Fria River Basin, and Prescott Metro have been updated in this feature class. Additional boundary updates include changing some TMA boundaries to align with transportation routes, PLSS, BLM district/field office boundaries, etc. This data was updated for use in 2 maps requested by Bill Gibson (April 2017) located here:blmdfslocEGISAZState_Officeprojects932_Renewable_MineralsTravelManagementTMA_TMPState_Park_GrantsStatePark_GrantFunds_TMP_2017_PDO.mxdblmdfslocEGISAZState_Officeprojects932_Renewable_MineralsTravelManagementTMA_TMPState_Park_GrantsStatePark_GrantFunds_TMP_2017_UP.mxdThe geodatabase containing the Sprint Contractor geometry updates is located here:blmdfslocEGISAZState_OfficeQA_QCTrackingTMAPTMA_Vertical_IntegrationTMA_Vertical_Integration.gdbAdditional edits wer made to Imperial Hills and Lower Colorado in June, 2017, and changes with intermittent frequency.; abstract: This data represents Travel Management Areas (TMA) used for travel planning efforts for the BLM Arizona State Office. Data contains information about status of route inventory, route evaluation, TMP and signage status. Dataset is in a File Geodatabase Feature Class format. The boundaries may be updated by BLM Staff as boundaries are refined.This feature class was created by combining the most recent and updated geometry for TMAs (edited by Sprint Contractor Ricardo Franco) and the best attributes available in the AZ Corporate Layers (cjallen - 4/13/17). Additional boundary edits and the creation of a new TMA (Prescott Metro) were completed with close guidance from Bill Gibson. TMA boundaries for Bumble Bee, Table Mesa, Lower Black Canyon Trail, Upper Agua Fria River Basin, and Prescott Metro have been updated in this feature class. Additional boundary updates include changing some TMA boundaries to align with transportation routes, PLSS, BLM district/field office boundaries, etc. This data was updated for use in 2 maps requested by Bill Gibson (April 2017) located here:blmdfslocEGISAZState_Officeprojects932_Renewable_MineralsTravelManagementTMA_TMPState_Park_GrantsStatePark_GrantFunds_TMP_2017_PDO.mxdblmdfslocEGISAZState_Officeprojects932_Renewable_MineralsTravelManagementTMA_TMPState_Park_GrantsStatePark_GrantFunds_TMP_2017_UP.mxdThe geodatabase containing the Sprint Contractor geometry updates is located here:blmdfslocEGISAZState_OfficeQA_QCTrackingTMAPTMA_Vertical_IntegrationTMA_Vertical_Integration.gdbAdditional edits wer made to Imperial Hills and Lower Colorado in June, 2017, and changes with intermittent frequency.

A Bureau of Land Management (BLM) Administrative Unit is a geographic area in which an organizational unit of the BLM has distinct jurisdictional responsibility for land and resource management activities occurring on the public lands, the maintained rights of the United States (i.e. mineral estate) and actions relating to the Trust responsibilities of the U.S. Government as stipulated in Law or Treaty.

These polygons should represent lower level administrative units (Field Office boundaries). Higher level administrative units may be derived from the attribution in this feature class. This feature class should be used to document the physical boundary of an administrative unit. In some cases, the administrative unit may manage areas outside of the boundary for other programs’ purposes. Please refer to the BLM Administrative Unit (Boundaries) Data Standard Report for the full list and detailed descriptions of the business rules that govern the data standard, and this implementation.

Each BLM state is responsible for the changes to the boundaries, the state data steward or the designated person for the state will be responsible for the creation and update of the boundaries.

Hydro-Corrected Catchment Boundaries for areas that impact Tallahassee and Leon County Florida. This feature layer was generated from a 2009 digital elevation model of Leon County, Florida.Catchments: Catchments are an aggregated set of data. Meaning groups of sub-basins (deranged areas) are merged to create larger more meaningful boundary areas. For natural areas this could be several shallow depressions being included with a larger nearby natural feature (lake, sink, etc.). Meaning depending upon rainfall conditions an entire area of interconnected lower elevation depressions coalesce into a larger drainage area. For developed areas, this could mean a series inlets direct surface drainage by way of conduits to a storm-water management facility. This information is processed by either known inventory data connection or by best assumption. Project SummaryESRI’s Arc Hydro toolset was used to “hydro-correct” a high-fidelity Lidar-derived bare earth DEM for the purpose of mapping all drainage areas that contribute water to Leon County. This process allowed for the mapping of actual surface drainage patterns on the ground, including natural areas of unaltered drainage along with areas where surface drainage has been engineered to not follow the natural topography. This process yielded a much more accurate delineation of the amount of water draining to a given location than would otherwise be possible using topographic DEMs alone.Since one of the goals of the project was to map all drainage areas that contribute surface drainage to Leon County, the mapping area extends beyond Leon County to include an additional nine counties in Florida and Georgia. This effort marked the first time that all areas that contribute surface drainage to Leon County were mapped.Hydro-CorrectionHydro-correction methods were used to modify the DEMs in order to take into account the presence of stormwater inventory. For this dataset, hydro-correction was accomplished by using 3 geoprocessing tools from the Arc Hydro toolset:Build Walls: The Build Wall tool allows the user to superimpose a polyline on an input DEM to raise the elevation values in the output DEM by a user-specified amount. This tool was primarily used where drainage divides were not well expressed in the source DEM. Additionally it was used to force drainage to stop at a particular point of interest (such as a stream confluence).DEM Reconditioning: This tool allows the user to superimpose a polyline on an input DEM to reduce the elevation values in the output DEM by a user-specified amount. This is often referred to as “burning” and is the opposite of Build Walls.Level DEM: This tool allows the user to superimpose a polygon on an input DEM to reassign cell values in the output DEM to a constant user-specified elevation. This tool was used to create pour point locations in the output DEM by reassigning the output elevation to be slightly lower than the surrounding input pixel values, thereby creating a “pit” in the output DEM. The area that drains to each “pit” is referred to as a Sub-Basin.Drainage Area DelineationThe Arc Hydro tool Sink Evaluation uses the 3 hydro-correction feature classes, the pour point feature class and the base earth DEM to create the drainage areas. Four feature classes representing drainage areas were produced: Sub-basins, Catchments, Watersheds, and Drainage basins. Sub-basins represent the smallest units of coherent drainage that were mapped. The Sub-Basins were the “building blocks” for creating a nested hierarchy of drainage units that are designed to be used at local scales as well as regional scales. The Sub-Basins were aggregated into larger nested drainage units, referred to as Catchments. The Catchments were aggregated into still larger nested drainage units, referred to as Watersheds. Finally, the Watersheds were aggregated into still larger nested drainage units, referred to as Drainage Basins.Drainage Boundary 3DDrainage Boundary 3D is a Polyline Z feature class created by intersecting the Sub-Basin boundaries with the DEM. The purpose of this feature class is to provide a set of metrics for identifying the high and low points along a Sub-Basin boundary, as well as pop-off locations for areas of closed drainage.This Feature Layer can be downloaded from TLCGIS' Open Data Portal as a zipped file geodatabase. FGDB Download Link.

131 views (3 recent) Confidence in the classification of biological zones in the EUSeaMap (2019) broad-scale predictive habitat map. Biological Zone is one of the layers of information used to categorise physical habitat types in EUSeaMap; these layers of information are collectively known as 'habitat descriptors'. Confidence in the classification of a Biological Zone at any location is driven by both the confidence in the values of the input variables, and the confidence in the classification based on proximity to, and uncertainty in, the boundary between classes (i.e. areas closer to a boundary between two classes will have lower confidence).

Confidence in the classification of energy level in the EUSeaMap (2023) broad-scale predictive habitat map.

Values are on a range from 1 (Low confidence), 2 (Moderate confidence), 3 (High confidence).

Energy level is one of the layers of information used to categorise physical habitat types in EUSeaMap; these layers of information are collectively known as 'habitat descriptors'. Confidence in the classification of an energy level at any location is driven by both the confidence in the values of the input variables, and the confidence in the classification based on proximity to, and uncertainty in, the boundary between classes (i.e. areas closer to a boundary between two classes will have lower confidence).

Layers are also available showing confidence in the values of the input variables used to model energy levels (kinetic energy at the seabed and wave exposure).

A report on the methods used in the 2023 version of EUSeaMap and reports on previous versions (v2019 and V2021) are linked in Online Resources.

Created by the EMODnet Seabed Habitats project consortium.

This dataset is an asset of the scientific manuscript "BIS-4D: Mapping soil properties and their uncertainties at 25m resolution in the Netherlands" (Helfenstein et al., 2024, under review). It contains maps of soil properties and their uncertainties at 25m resolution in the Netherlands obtained using the BIS-4D soil modelling and mapping platform. BIS-4D is based on well-established digital soil mapping practices. This dataset includes maps of predictions of the mean, 0.05, 0.50 (median) and 0.95 quantiles and the 90th prediction interval width (PI90) of clay content [%], silt content [%], sand content [%], bulk density (BD) [g/cm3], soil organic matter (SOM) [%], pH [KCl], total N (Ntot) [mg/kg], oxalate-extractable P (Pox) [mmol/kg] and cation exchange capacity (CEC) [mmol(c)/kg]. Prediction maps are available for the standard depth layers specified by the GlobalSoilMap initiative (0-5, 5-15, 15-30, 30-60, 60-100 and 100-200cm). For SOM, these prediction maps are available for the years 1953, 1960, 1970, 1980, 1990, 2000, 2010, 2020 and 2023 based on changing land use, peat classes and peat occurrence over time. BIS-4D uses georeferenced soil point data (field estimates and laboratory measurements), spatially explicit environmental variables (covariates), and machine learning to predict in 3D space, and for SOM, in 3D space and time.

More information about how these maps were created, the BIS-4D soil modelling and mapping platform, accuracy assessment, strengths, limitations, map assessment scale and specific user recommendations can be found in the scientific paper "BIS-4D: Mapping soil properties and their uncertainties at 25m resolution in the Netherlands" (Helfenstein et al., 2024, under review). The BIS-4D model code is available on GitLab.

Please note that an earlier version of soil pH prediction maps were published. In comparison, this version contains several important updates. Firstly, covariates of peat classes, groundwater classes in agricultural areas and Sentinel 2 RGB and NIR bands and spectral indices were added, all of which were selected and thus used for model calibration and prediction of the updated BIS-4D prediction maps. We also included de-correlation and recursive feature elimination to increase the signal to noise ratio, make models more parsimonious and increase reproducibility.

Please consider the following file naming structure to make it easier to find the prediction maps you need:

• File naming structure: "[soil property]_d_[upper depth layer boundary]_[lower depth layer boundary]_QRF_[PI90/pred type]_[processed].tif"
• Example: "clay_per_d_0_5_QRF_pred_mean_processed.tif"

Soil property denotes the target soil property (listed above), depth upper and lower boundaries indicate the prediction target depth, QRF = quantile regression forest, which is the algorithm used for model calibration and prediction, PI90 is a measure of prediction uncertainy and is the 95th - 5th quantile, "pred_mean" indicates mean predictions, "pred50" indicates median predictions, "pred5" indicates 5th quantile prediction and "pred95" indicates 95th quantile prediction. For clay, silt and sand content, predictions were post-processed so that they add up to 100% and therefore for those GeoTIFF files the names contain "_processed". For SOM, the target prediction year is also indicated directly after "SOM_per", e.g. "SOM_per_2023_d_0_5_QRF_pred_mean.tif".

Confidence in the full output of the 2016 EUSeaMap broad-scale predictive model, produced by EMODnet Seabed Habitats.

Values are on a range from 1 (low confidence) to 3 (high confidence).

Confidence is calculated by amalgamating the confidence values of the underlying applicable habitat descriptors used to generate the habitat value in the area in question.

Habitat descriptors differ per region but include: Biological zone Energy class Oxygen regime Salinity regime Seabed Substrate Riverine input

Confidence in habitat descriptors are driven by the confidence in the source data used to determine the descriptor, and the confidence in the threshold/margin (areas closer to a boundary between two classes will have lower confidence).

Confidence values are also available for each habitat descriptor and input data layer.

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

Confidence in the classification of biological zones in the EUSeaMap (2023) broad-scale predictive habitat map for the Caspian Sea.

Values are on a range from 1 (Low confidence) to 3 (High confidence).

Biological Zone is one of the layers of information used to categorise physical habitat types in EUSeaMap; these layers of information are collectively known as 'habitat descriptors'. Confidence in the classification of a Biological Zone at any location is driven by both the confidence in the values of the input variables, and the confidence in the classification based on proximity to, and uncertainty in, the boundary between classes (i.e. areas closer to a boundary between two classes will have lower confidence).

Layers are also available showing confidence in the values of the input variables used to model Biological Zones.

Detailed information on the modelling process is found in the EMODnet Seabed Habitats technical reports and its appendices (links in Resources).

Created by the EMODnet Seabed Habitats project consortium.

Confidence in the 2016 EUSeaMap salinity regime class in the Baltic Sea and Kattegat strait in the North Sea/Baltic Sea, produced by EMODnet Seabed Habitats for the 2016 EUSeaMap broad-scale predictive habitat maps.

Values are on a range from 1 (Low confidence) to 3 (High confidence).

Confidence in salinity regime are driven by the confidence in the salinity data used to determine the descriptor, and the confidence in the threshold/margin between classes (areas closer to a boundary between two classes will have lower confidence).

Detailed information on the confidence assessment in Populus J. et al 2017. EUSeaMap, a European broad-scale seabed habitat map. Ifremer. http://doi.org/10.13155/49975

Confidence in the PAR at seabed values, produced by EMODnet Seabed Habitats for the 2016 EUSeaMap broad-scale predictive habitat maps.

Values are on a range from 1 (Low confidence) to 3 (High confidence). Confidence in Photosynthetically Active Radiation (PAR) at the seabed are driven by the confidence in the underlying data used to determine the descriptor (PAR at the surface, light attenuation coefficient KD(PAR) and depth to the seabed.) and the confidence in the threshold/margin between classes (areas closer to a boundary between two classes will have lower confidence).

Detailed information on the confidence assessment in Populus J. et al 2017. EUSeaMap, a European broad-scale seabed habitat map. Ifremer. http://doi.org/10.13155/49975