The 2014 update of the U.S. Geological Survey (USGS) National Seismic Hazard Model (NSHM) for the conterminous United States (2014 NSHM; Petersen and others, 2014; https://pubs.usgs.gov/of/2008/1128/) included probabilistic ground motion maps for 2 percent and 10 percent probabilities of exceedance in 50 years, derived from seismic hazard curves for peak ground acceleration (PGA) and 0.2 and 1.0 second spectral accelerations (SAs) with 5 percent damping for the National Earthquake Hazards Reduction Program (NEHRP) site class boundary B/C (time-averaged shear wave velocity in the upper 30 meters [VS30]=760 meters per second [m/s]). This data release provides 0.1 degree by 0.1 degree gridded seismic hazard curves, 0.1 degree by 0.1 degree gridded probabilistic ground motions, and seismic hazard maps calculated for additional periods and additional uniform NEHRP site classes using the 2014 NSHM. For both the central and eastern U.S. (CEUS) and western U.S. (WUS), data and maps are provided for PGA, 0.1, 0.2, 0.3, 0.5, 1.0, and 2.0 second SAs with 5% damping for the NEHRP site class boundary B/C for 2, 5, and 10% probabilities of exceedance in 50 years. The WUS additionally includes data and maps for 0.75, 3.0, 4.0, and 5.0 SAs. The use of region-specific suites of weighted ground motion models (GMMs) in the 2014 NSHM precluded the calculation of ground motions for a uniform set of periods and site classes for the conterminous U.S. At the time of development of the 2014 NSHM, there was no consensus in the CEUS on an appropriate site-amplification model to use, therefore, we calculated hazard curves and maps for NEHRP Site Class A (VS30 = 2000 m/s), for which most stable continental GMMs were original developed, based on simulations for hard rock conditions. In the WUS, however, the GMMs allow amplification based on site class (defined by VS30), so we calculated hazard curves and maps for NEHRP site classes B (VS30 = 1080 m/s), C (VS30 = 530 m/s), D (VS30 = 260 m/s), and E (VS30 = 150 m/s) and site class boundaries A/B (VS30 = 1500 m/s), B/C (VS30 = 760 m/s), C/D (VS30 = 365 m/s), and D/E (VS30 = 185 m/s). Further explanation about how the data and maps were generated can be found in the accompanying U.S. Geological Survey Open-File Report 2018-1111 (https://doi.org/10.3133/ofr20181111). First Posted - July 18, 2018 Revised - February 20, 2019 (ver. 1.1)

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.

Improving the quality of water discharged from agricultural watersheds requires comprehensive and adaptive approaches for planning and implementing conservation practices. These measures will need to consider landscape hydrology, distributions of soil types, land cover, and crop distributions in an integrated manner. The two most consistent challenges to these efforts will be consistency and reliability of data, and the capacity to translate conservation planning from watershed to farm and field scales. The translation of scale is required because, while conservation practices can be planned based on a watershed scale framework, they must be implemented by landowners in specific fields and riparian sites that are under private ownership. To support these goals, it has been necessary to develop planning approaches, high-resolution spatial datasets, and conservation practice assessment tools that will allow the agricultural and conservation communities to characterize and mitigate these challenges. The field boundary dataset represents a spatial framework for assembling and maintaining geospatial data to support conservation planning at the scale where conservation practices are implemented. This field boundaries dataset has been assembled to support field-scale agricultural conservation planning using the USDA/ARS Agricultural Conservation Planning Framework (ACPF). The original data used to create this database are the pre-2008 Farm Bill FSA common land unit (CLU) datasets. A portion of metadata found herein pertains to the USDA FSA CLU. The remaining information has been developed to reflect the repurposing of the data in its aggregated form. It is important to note that all USDA programmatic and ownership information that was associated with the original data have been removed. Beyond that, these data has been extensively edited to reflect crop-specific land use consistent with 2009 land cover as derived from 2009 NASS Crop Data Layer datasets and 2009 aerial photography, and no longer reflects discrete ownership patterns. The ACPF field boundaries feature class incorporates two additional resources that form the Upper Mississippi River Basin (UMRB) ACPF Land Use database. The UMRB ACPF Fields Crop History table holds the dominant land use class, derived from the NASS CDL, for individual fields from 2010 to 2020. The UMRB ACPF Land Use table hold summary land use information for individual fields for 2015 to 2020 including an assigned General Land Use (GenLU) that represent the cropping system over that period. In lieu of a data dictionary for these resources, each dataset has a FGDC-compliant metadata file using the North American ISO 19115-2003 profile in .xml format. For more information about this dataset contact David E. James at davide.james@usda.gov or dejames@iastate.edu Resources in this dataset:Resource Title: Agricultural land use by field: Upper Mississippi River Basin 2010-2020. File Name: UMRB_ACPFfields2020.zipResource Description: This field boundaries dataset has been assembled to support field-scale agricultural conservation planning using the USDA/ARS Agricultural Conservation Planning Framework (ACPF).Resource Software Recommended: ESRI's ArcGIS,url: www.esri.com Resource Title: Upper Mississippi River Basin Field Boundaries 2020. File Name: UMRB_ACPFfields2020.pdfResource Description: UMRB Field Boundaries 2020 feature class metadataResource Software Recommended: Adobe Acrobat,url: www.adobe.com Resource Title: Upper Mississippi River Basin ACPF Crop History 2010-2020. File Name: UMRB_ACPFfields_CropHistory2010_2020.pdfResource Description: Upper Mississippi River Basin ACPF Crop History table 2010-2020 metadataResource Software Recommended: Adobe Acrobat,url: www.adobe.com Resource Title: Upper Mississippi River Basin ACPF Land Use 2015-2020. File Name: UMRB_ACPFfields_LandUse2015_2020.pdfResource Description: Upper Mississippi River Basin ACPF Land Use table 2015-2020 metadataResource Software Recommended: Adobe Acrobat,url: www.adobe.com

These Surface Water Supply Protection Areas delineate those areas included in 310 CMR 22.00, the Massachusetts Drinking Water Regulations, as Surface Water Supply Protection Zones:ZONEA: represents a) the land area between the surface water source and the upper boundary of the bank; b) the land area within a 400 foot lateral distance from the upper boundary of the bank of a Class A surface water source, as defined in 314 CMR 4.05(3)(a); and c) the land area within a 200 foot lateral distance from the upper boundary of the bank of a tributary or associated surface water body.ZONEB: represents the land area within one-half mile of the upper boundary of the bank of a Class A surface water source, as defined in 314 CMR 4.05(3)(a), or edge of watershed, whichever is less. Zone B always includes the land area within a 400 ft lateral distance from the upper boundary of the bank of a Class A surface water source. ZONEC: represents the land area not designated as Zone A or B within the watershed of a Class A surface water source, as defined in 314 CMR 4.05(3)(a). All active and inactive surface water supplies have zones delineated, but some may be covered by other legislation. Watershed extents for all surface water supplies including active, inactive, emergency, sources outside of Massachusetts, watersheds that extend into other states and watersheds of sources from other states that extend into Massachusetts are included in the datalayer SWP_Watersheds. Surface water intakes on Class B Rivers are not included in either datalayer. The reservoir features were taken from the DEP Wetlands 1:12000 datalayer and removed from the Zone A and B features and added as a separate feature.

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.

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".

This map describes actions proposed as part of the Upper Cheat River Project, proposed by the U.S. Forest Service in the Monongahela National Forest, in Tucker and Preston counties, WV.Purpose:Stakeholders have raised significant concerns about this project, particularly with regard to the amount of timbering involved. The Forest Service asserts that a more even distribution of forest age classes is necessary to promote the overall health of the ecosystem, and aims to significantly increase the proportion of early successional forest (clear cuts/burnt areas). However, in its analyses the USFS accounts only for the land within the USFS-administered boundaries of the forest. It completely disregards the age class distribution on the private lands within the Proclamation Boundary, and on private lands adjacent to the National Forest, which show a much younger age class distribution. Questions have been raised as to whether this is simply a good way to get mature timber out of the forest. Stakeholders have called for a more regional approach to determining the age class structure of contiguous forest lands...not just those within the US-owned properties.Click on the map layer names below for detailed descriptions of those data.