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.

Yearly citation counts for the publication titled "The Low/High BCS Permeability Class Boundary: Physicochemical Comparison of Metoprolol and Labetalol".

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.

Soil_Samples_BACI Available only by request on a case by case basis. Contact rthe author, David Nowak, at dnowak@fs.fed.us Tags Biophysical Resources, Land, Social Institutions, Health, BES, Soil, Lead, Sample, UFORE Summary Samples were taken to relate soil data to vegetation data obtained for the Urban Forestry Effects Model (UFORE). Description The data is soil concentrations and characteristics of the following: land use, bulk density, sand, silt, clay, pH, organic matter, nitrogen, Al, P, S, Ti, Cr, Mn, Fe, Co, Ni, Cu Zn, Mo, Pb, Cd, Na, Mg, K, Ca, and V. Soils were sampled in 125 plots located within the City of Baltimore in the summer of 2000. The plots were randomly stratified by Anderson Land Cover Classification System Level II, which included commercial, industrial, institutional, transportation right-of-ways, high and medium density residential (there were no low density residential areas identified within the city boundaries), golf course, park, urban open, forest, and wetland land-use types. The number of plots situated in each land-use type was weighted to their proportion of spatial area within the City. The resultant number of plots sampled for soil by land-use type was: commercial (n = 2); industrial (n = 3); institutional (n = 10); transportation right-of-ways (n = 7); high density residential (n = 19); medium density residential (n = 33); golf course (n = 3); riparian (n=2); park (n = 10); urban open (n = 10); and forest (n = 26) land-use types, respectively. The distribution of plots represents the proportion of area covered by impervious surfaces. Credits Rich Pouyat, USDA Forest Service Use limitations Not for profit use only Extent West -76.711030 East -76.530612 North 39.371355 South 39.200686 Scale Range There is no scale range for this item. The data is soil concentrations and characteristics of the following: land use, bulk density, sand, silt, clay, pH, organic matter, nitrogen, Al, P, S, Ti, Cr, Mn, Fe, Co, Ni, Cu Zn, Mo, Pb, Cd, Na, Mg, K, Ca, and V. Soils were sampled in 125 plots located within the City of Baltimore in the summer of 2000. The plots were randomly stratified by Anderson Land Cover Classification System Level II, which included commercial, industrial, institutional, transportation right-of-ways, high and medium density residential (there were no low density residential areas identified within the city boundaries), golf course, park, urban open, forest, and wetland land-use types. The number of plots situated in each land-use type was weighted to their proportion of spatial area within the City. The resultant number of plots sampled for soil by land-use type was: commercial (n = 2); industrial (n = 3); institutional (n = 10); transportation right-of-ways (n = 7); high density residential (n = 19); medium density residential (n = 33); golf course (n = 3); riparian (n=2); park (n = 10); urban open (n = 10); and forest (n = 26) land-use types, respectively. The distribution of plots represents the proportion of area covered by impervious surfaces.

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.

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.

Network of 44 papers and 86 citation links related to "The Low/High BCS Permeability Class Boundary: Physicochemical Comparison of Metoprolol and Labetalol".

The purpose of this study was to explore how White and Black working-class men construct their sense of self-worth, and how they perceive social hierarchy by interpreting differences between themselves and others. Interviews were conducted with 150 lower-middle class men. Thirty blue-collar workers from each of four groups were interviewed: White American workers, Black American workers, French White workers, and North African Immigrant workers living in France. These were supplemented with 30 interviews with lower-status white-collar workers in both France and the United States. In order to be part of the working class sample, participants had to (1) be employed in blue-collar or lower-status white-collar jobs; (2) have a high-school but not college degree; (3) supervise no more than 10 people, if any; (4) show continuous full-time participation in the labor force for at least five years; (5) describe themselves as Black or White for the United States; (6) have resided in the study area for at least five years; (7) be a native of the United States, France, Morocco, Tunisia, or Algeria; and (8) be a man between the ages of 25 and 65, in order to keep constant various socio-demographic variations such as place of birth and gender. All immigrants in America and children of North African immigrants (considered French citizens) were excluded, also to keep the socio-demographic dimensions constant. The interviews were approximately two hours long. Each interview was tape-recorded and conducted at a time and place chosen by both the participant and the principal investigator. Towns that included large numbers of working class individuals were identified. Names were sampled randomly from the phone books for these towns, and individuals were sent a letter of introduction. A short phone interview was conducted to ascertain eligibility and willingness to participate. Variables assessed included participants' definitions of worthy and less worthy persons; descriptions of associates; superiority and inferiority in relation to different types of people; descriptions of people that evoked hostility, indifference, or sympathy; and negative and positive traits of coworkers and acquaintances. One goal of this study was to determine the labels participants used to describe people whom they considered to be above or below themselves. Participants were also asked to describe their perceptions of cultural traits that are most valued in the workplace, child-rearing values, and the meanings assigned to each value. The Murray Archive holds additional analogue materials for this study (original record paper data, and audiotape data for this study). If you would like to access this material, please apply to use the data. A comparison study conducted in the late 1980's by Lamont with American and French White upper-middle class participants is also archived at the Murray Archive (Log# 00133). Audio Data Availability Note: This study contains audio data that have been digitized. There are 334 audio files available.

136 views (4 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).

This shapefile represents habitat suitability categories (High, Moderate, Low, and Non-Habitat) derived from a composite, continuous surface of sage-grouse habitat suitability index (HSI) values for Nevada and northeastern California formed from the multiplicative product of the spring, summer, and winter HSI surfaces. Summary of steps to create Habitat Categories: HABITAT SUITABILITY INDEX: The HSI was derived from a generalized linear mixed model (specified by binomial distribution and created using ArcGIS 10.2.2) that contrasted data from multiple environmental factors at used sites (telemetry locations) and available sites (random locations). Predictor variables for the model represented vegetation communities at multiple spatial scales, water resources, habitat configuration, urbanization, roads, elevation, ruggedness, and slope. Vegetation data was derived from various mapping products, which included NV SynthMap (Petersen 2008, SageStitch (Comer et al. 2002, LANDFIRE (Landfire 2010), and the CA Fire and Resource Assessment Program (CFRAP 2006). The analysis was updated to include high resolution percent cover within 30 x 30 m pixels for Sagebrush, non-sagebrush, herbaceous vegetation, and bare ground (C. Homer, unpublished; based on the methods of Homer et al. 2014, Xian et al. 2015 ) and conifer (primarily pinyon-juniper, P. Coates, unpublished). The pool of telemetry data included the same data from 1998 - 2013 used by Coates et al. (2014) as well as additional telemetry location data from field sites in 2014. The dataset was then split according to calendar date into three seasons. Spring included telemetry locations (n = 14,058) from mid-March to June; summer included locations (n = 11,743) from July to mid-October; winter included locations (n = 4862) from November to March. All age and sex classes of marked grouse were used in the analysis. Sufficient data (i.e., a minimum of 100 locations from at least 20 marked Sage-grouse) for modeling existed in 10 subregions for spring and summer, and seven subregions in winter, using all age and sex classes of marked grouse. It is important to note that although this map is composed of HSI values derived from the seasonal data, it does not explicitly represent habitat suitability for reproductive females (i.e., nesting and with broods). Insufficient data were available to allow for estimation of this habitat type for all seasons throughout the study area extent. A Resource Selection Function (RSF) was calculated for each subregion using R software (v 3.13) and season using generalized linear models to derive model-averaged parameter estimates for each covariate across a set of additive models. For each season, subregional RSFs were transformed into Habitat Suitability Indices, and averaged together to produce an overall statewide HSI whereby a relative probability of occurrence was calculated for each raster cell. The three seasonal HSI rasters were then multiplied to create a composite HSI. In order to account for discrepancies in HSI values caused by varying ecoregions within Nevada, the HSI was divided into north and south extents using a slightly modified flood region boundary (Mason 1999) that was designed to represent respective mesic and xeric regions of the state. North and south HSI rasters were each relativized according to their maximum value to rescale between zero and one, then mosaicked once more into a state-wide extent. HABITAT CATEGORIZATION: Using the same ecoregion boundaries described above, the habitat classification dataset (an independent data set comprising 10% of the total telemetry location sample) was split into locations falling within respective north and south regions. HSI values from the composite and relativized statewide HSI surface were then extracted to each classification dataset location within the north and south region. The distribution of these values were used to identify class break values corresponding to 0.5 (high), 1.0 (moderate), and 1.5 (low) standard deviations (SD) from the mean HSI. These class breaks were used to classify the HSI surface into four discrete categories of habitat suitability: High, Moderate, Low, and Non-Habitat. In terms of percentiles, High habitat comprised greater than 30.9 % of the HSI values, Moderate comprised 15 – 30.9%, Low comprised 6.7 – 15%, and Non-Habitat comprised less than 6.7%.The classified north and south regions were then clipped by the boundary layer and mosaicked to create a statewide categorical surface for habitat selection. Each habitat suitability category was converted to a vector output where gaps within polygons less than 1.2 million square meters were eliminated, polygons within 500 meters of each other were connected to create corridors and polygons less than 1.2 million square meters in one category were incorporated to the adjacent category. The final step was to mask major roads that were buffered by 50m (Census, 2014), lakes (Peterson, 2008) and urban areas, and place those masked areas into the non-habitat category. The existing urban layer (Census 2010) was not sufficient for our needs because it excluded towns with a population lower than 1,500. Hence, we masked smaller towns (populations of 100 to 1500) and development with Census Block polygons (Census 2015) that had at least 50% urban development within their boundaries when viewed with reference imagery (ArcGIS World Imagery Service Layer). REFERENCES: California Forest and Resource Assessment Program (CFRAP). 2006. Statewide Land Use / Land Cover Mosaic. [Geospatial data.] California Department of Forestry and Fire Protection, http://frap.cdf.ca.gov/data/frapgisdata-sw-rangeland-assessment_data.php Census 2010. TIGER/Line Shapefiles. Urban Areas [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2014. TIGER/Line Shapefiles. Roads [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2015. TIGER/Line Shapefiles. Blocks [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson, K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., Gardner, S., Espinosa, S., and Delehanty, D.J. 2014, Spatially explicit modeling of greater sage-grouse (Centrocercus urophasianus) habitat in Nevada and northeastern California—A decision-support tool for management: U.S. Geological Survey Open-File Report 2014-1163, 83 p., http://dx.doi.org/10.3133/ofr20141163. ISSN 2331-1258 (online) Comer, P., Kagen, J., Heiner, M., and Tobalske, C. 2002. Current distribution of sagebrush and associated vegetation in the western United States (excluding NM). [Geospatial data.] Interagency Sagebrush Working Group, http://sagemap.wr.usgs.gov Homer, C.G., Aldridge, C.L., Meyer, D.K., and Schell, S.J. 2014. Multi-Scale Remote Sensing Sagebrush Characterization with Regression Trees over Wyoming, USA; Laying a Foundation for Monitoring. International Journal of Applied Earth Observation and Geoinformation 14, Elsevier, US. LANDFIRE. 2010. 1.2.0 Existing Vegetation Type Layer. [Geospatial data.] U.S. Department of the Interior, Geological Survey, http://landfire.cr.usgs.gov/viewer/ Mason, R.R. 1999. The National Flood-Frequency Program—Methods For Estimating Flood Magnitude And Frequency In Rural Areas In Nevada U.S. Geological Survey Fact Sheet 123-98 September, 1999, Prepared by Robert R. Mason, Jr. and Kernell G. Ries III, of the U.S. Geological Survey; and Jeffrey N. King and Wilbert O. Thomas, Jr., of Michael Baker, Jr., Inc. http://pubs.usgs.gov/fs/fs-123-98/ Peterson, E. B. 2008. A Synthesis of Vegetation Maps for Nevada (Initiating a 'Living' Vegetation Map). Documentation and geospatial data, Nevada Natural Heritage Program, Carson City, Nevada, http://www.heritage.nv.gov/gis Xian, G., Homer, C., Rigge, M., Shi, H., and Meyer, D. 2015. Characterization of shrubland ecosystem components as continuous fields in the northwest United States. Remote Sensing of Environment 168:286-300. NOTE: This file does not include habitat areas for the Bi-State management area and the spatial extent is modified in comparison to Coates et al. 2014

This shapefile represents habitat suitability categories (High, Moderate, Low, and Non-Habitat) derived from a composite, continuous surface of sage-grouse habitat suitability index (HSI) values for Nevada and northeastern California during the winter season, and is a surrogate for habitat conditions during periods of cold and snow. Summary of steps to create Habitat Categories: HABITAT SUITABILITY INDEX: The HSI was derived from a generalized linear mixed model (specified by binomial distribution and created using ArcGIS 10.2.2) that contrasted data from multiple environmental factors at used sites (telemetry locations) and available sites (random locations). Predictor variables for the model represented vegetation communities at multiple spatial scales, water resources, habitat configuration, urbanization, roads, elevation, ruggedness, and slope. Vegetation data was derived from various mapping products, which included NV SynthMap (Petersen 2008, SageStitch (Comer et al. 2002, LANDFIRE (Landfire 2010), and the CA Fire and Resource Assessment Program (CFRAP 2006). The analysis was updated to include high resolution percent cover within 30 x 30 m pixels for Sagebrush, non-sagebrush, herbaceous vegetation, and bare ground (C. Homer, unpublished; based on the methods of Homer et al. 2014, Xian et al. 2015 ) and conifer (primarily pinyon-juniper, P. Coates, unpublished). The pool of telemetry data included the same data from 1998 - 2013 used by Coates et al. (2014); additional telemetry location data from field sites in 2014 were added to the dataset. The dataset was then split according calendar date into three seasons (spring, summer, winter). Winter included telemetry locations (n = 4862) from November to March. All age and sex classes of marked grouse were used in the analysis. Sufficient data (i.e., a minimum of 100 locations from at least 20 marked Sage-grouse) for modeling existed in 10 subregions for spring and summer, and seven subregions in winter, using all age and sex classes of marked grouse. It is important to note that although this map is composed of HSI values derived from the seasonal data, it does not explicitly represent habitat suitability for reproductive females (i.e., nesting and with broods). Insufficient data were available to allow for estimation of this habitat type for all seasons throughout the study area extent. A Resource Selection Function (RSF) was calculated for each subregion using R software (v 3.13) and using generalized linear models to derive model-averaged parameter estimates for each covariate across a set of additive models. Subregional RSFs were transformed into Habitat Suitability Indices, and averaged together to produce an overall statewide HSI whereby a relative probability of occurrence was calculated for each raster cell during the spring season. In order to account for discrepancies in HSI values caused by varying ecoregions within Nevada, the HSI was divided into north and south extents using a slightly modified flood region boundary (Mason 1999) that was designed to represent respective mesic and xeric regions of the state. North and south HSI rasters were each relativized according to their maximum value to rescale between zero and one, then mosaicked once more into a state-wide extent. HABITAT CATEGORIZATION: Using the same ecoregion boundaries described above, the habitat classification dataset (an independent data set comprising 10% of the total telemetry location sample) was split into locations falling within respective north and south regions. HSI values from the composite and relativized statewide HSI surface were then extracted to each classification dataset location within the north and south region. The distribution of these values were used to identify class break values corresponding to 0.5 (high), 1.0 (moderate), and 1.5 (low) standard deviations (SD) from the mean HSI. These class breaks were used to classify the HSI surface into four discrete categories of habitat suitability: High, Moderate, Low, and Non-Habitat. In terms of percentiles, High habitat comprised greater than 30.9 % of the HSI values, Moderate comprised 15 – 30.9%, Low comprised 6.7 – 15%, and Non-Habitat comprised less than 6.7%.The classified north and south regions were then clipped by the boundary layer and mosaicked to create a statewide categorical surface for habitat selection . Each habitat suitability category was converted to a vector output where gaps within polygons less than 1.2 million square meters were eliminated, polygons within 500 meters of each other were connected to create corridors and polygons less than 1.2 million square meters in one category were incorporated to the adjacent category. The final step was to mask major roads that were buffered by 50m (Census, 2014), lakes (Peterson, 2008) and urban areas, and place those masked areas into the non-habitat category. The existing urban layer (Census 2010) was not sufficient for our needs because it excluded towns with a population lower than 1,500. Hence, we masked smaller towns (populations of 100 to 1500) and development with Census Block polygons (Census 2015) that had at least 50% urban development within their boundaries when viewed with reference imagery (ArcGIS World Imagery Service Layer). REFERENCES: California Forest and Resource Assessment Program (CFRAP). 2006. Statewide Land Use / Land Cover Mosaic. [Geospatial data.] California Department of Forestry and Fire Protection, http://frap.cdf.ca.gov/data/frapgisdata-sw-rangeland-assessment_data.php Census 2010. TIGER/Line Shapefiles. Urban Areas [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2014. TIGER/Line Shapefiles. Roads [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2015. TIGER/Line Shapefiles. Blocks [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson, K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., Gardner, S., Espinosa, S., and Delehanty, D.J. 2014, Spatially explicit modeling of greater sage-grouse (Centrocercus urophasianus) habitat in Nevada and northeastern California—A decision-support tool for management: U.S. Geological Survey Open-File Report 2014-1163, 83 p., http://dx.doi.org/10.3133/ofr20141163. ISSN 2331-1258 (online) Comer, P., Kagen, J., Heiner, M., and Tobalske, C. 2002. Current distribution of sagebrush and associated vegetation in the western United States (excluding NM). [Geospatial data.] Interagency Sagebrush Working Group, http://sagemap.wr.usgs.gov Homer, C.G., Aldridge, C.L., Meyer, D.K., and Schell, S.J. 2014. Multi-Scale Remote Sensing Sagebrush Characterization with Regression Trees over Wyoming, USA; Laying a Foundation for Monitoring. International Journal of Applied Earth Observation and Geoinformation 14, Elsevier, US. LANDFIRE. 2010. 1.2.0 Existing Vegetation Type Layer. [Geospatial data.] U.S. Department of the Interior, Geological Survey, http://landfire.cr.usgs.gov/viewer/ Mason, R.R. 1999. The National Flood-Frequency Program—Methods For Estimating Flood Magnitude And Frequency In Rural Areas In Nevada U.S. Geological Survey Fact Sheet 123-98 September, 1999, Prepared by Robert R. Mason, Jr. and Kernell G. Ries III, of the U.S. Geological Survey; and Jeffrey N. King and Wilbert O. Thomas, Jr., of Michael Baker, Jr., Inc. http://pubs.usgs.gov/fs/fs-123-98/ Peterson, E. B. 2008. A Synthesis of Vegetation Maps for Nevada (Initiating a 'Living' Vegetation Map). Documentation and geospatial data, Nevada Natural Heritage Program, Carson City, Nevada, http://www.heritage.nv.gov/gis Xian, G., Homer, C., Rigge, M., Shi, H., and Meyer, D. 2015. Characterization of shrubland ecosystem components as continuous fields in the northwest United States. Remote Sensing of Environment 168:286-300. NOTE: This file does not include habitat areas for the Bi-State management area and the spatial extent is modified in comparison to Coates et al. 2014

This shapefile represents habitat suitability categories (High, Moderate, Low, and Non-Habitat) derived from a composite, continuous surface of sage-grouse habitat suitability index (HSI) values for Nevada and northeastern California during spring, which is a surrogate for habitat conditions during the sage-grouse breeding and nesting period. Summary of steps to create Habitat Categories: HABITAT SUITABILITY INDEX: The HSI was derived from a generalized linear mixed model (specified by binomial distribution) that contrasted data from multiple environmental factors at used sites (telemetry locations) and available sites (random locations). Predictor variables for the model represented vegetation communities at multiple spatial scales, water resources, habitat configuration, urbanization, roads, elevation, ruggedness, and slope. Vegetation data was derived from various mapping products, which included NV SynthMap (Petersen 2008, SageStitch (Comer et al. 2002, LANDFIRE (Landfire 2010), and the CA Fire and Resource Assessment Program (CFRAP 2006). The analysis was updated to include high resolution percent cover within 30 x 30 m pixels for Sagebrush, non-sagebrush, herbaceous vegetation, and bare ground (C. Homer, unpublished; based on the methods of Homer et al. 2014, Xian et al. 2015 ) and conifer (primarily pinyon-juniper, P. Coates, unpublished). The pool of telemetry data included the same data from 1998 - 2013 used by Coates et al. (2014); additional telemetry location data from field sites in 2014 were added to the dataset. The dataset was then split according calendar date into three seasons (spring, summer, winter). Spring included telemetry locations (n = 14,058) from mid-March to June, and is a surrogate for habitat conditions during the sage-grouse breeding and nesting period. All age and sex classes of marked grouse were used in the analysis. Sufficient data (i.e., a minimum of 100 locations from at least 20 marked Sage-grouse) for modeling existed in 10 subregions for spring and summer, and seven subregions in winter, using all age and sex classes of marked grouse. It is important to note that although this map is composed of HSI values derived from the seasonal data, it does not explicitly represent habitat suitability for reproductive females (i.e., nesting). Insufficient data were available to allow for estimation of this habitat type for all seasons throughout the study area extent. A Resource Selection Function (RSF) was calculated for each subregion and using generalized linear models to derive model-averaged parameter estimates for each covariate across a set of additive models. Subregional RSFs were transformed into Habitat Suitability Indices, and averaged together to produce an overall statewide HSI whereby a relative probability of occurrence was calculated for each raster cell during the spring season. In order to account for discrepancies in HSI values caused by varying ecoregions within Nevada, the HSI was divided into north and south extents using a slightly modified flood region boundary (Mason 1999) that was designed to represent respective mesic and xeric regions of the state. North and south HSI rasters were each relativized according to their maximum value to rescale between zero and one, then mosaicked once more into a state-wide extent. HABITAT CATEGORIZATION: Using the same ecoregion boundaries described above, the habitat classification dataset (an independent data set comprising 10% of the total telemetry location sample) was split into locations falling within respective north and south regions. HSI values from the composite and relativized statewide HSI surface were then extracted to each classification dataset location within the north and south region. The distribution of these values were used to identify class break values corresponding to 0.5 (high), 1.0 (moderate), and 1.5 (low) standard deviations (SD) from the mean HSI. These class breaks were used to classify the HSI surface into four discrete categories of habitat suitability: High, Moderate, Low, and Non-Habitat. In terms of percentiles, High habitat comprised greater than 30.9 % of the HSI values, Moderate comprised 15 – 30.9%, Low comprised 6.7 – 15%, and Non-Habitat comprised less than 6.7%.The classified north and south regions were then clipped by the boundary layer and mosaicked to create a statewide categorical surface for habitat selection. Each habitat suitability category was converted to a vector output where gaps within polygons less than 1.2 million square meters were eliminated, polygons within 500 meters of each other were connected to create corridors and polygons less than 1.2 million square meters in one category were incorporated to the adjacent category. The final step was to mask major roads that were buffered by 50m (Census, 2014), lakes (Peterson, 2008) and urban areas, and place those masked areas into the non-habitat category. The existing urban layer (Census 2010) was not sufficient for our needs because it excluded towns with a population lower than 1,500. Hence, we masked smaller towns (populations of 100 to 1500) and development with Census Block polygons (Census 2015) that had at least 50% urban development within their boundaries when viewed with reference imagery (ArcGIS World Imagery Service Layer). REFERENCES: California Forest and Resource Assessment Program (CFRAP). 2006. Statewide Land Use / Land Cover Mosaic. [Geospatial data.] California Department of Forestry and Fire Protection, http://frap.cdf.ca.gov/data/frapgisdata-sw-rangeland-assessment_data.php Census 2010. TIGER/Line Shapefiles. Urban Areas [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2014. TIGER/Line Shapefiles. Roads [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2015. TIGER/Line Shapefiles. Blocks [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson, K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., Gardner, S., Espinosa, S., and Delehanty, D.J. 2014, Spatially explicit modeling of greater sage-grouse (Centrocercus urophasianus) habitat in Nevada and northeastern California—A decision-support tool for management: U.S. Geological Survey Open-File Report 2014-1163, 83 p., http://dx.doi.org/10.3133/ofr20141163. ISSN 2331-1258 (online) Comer, P., Kagen, J., Heiner, M., and Tobalske, C. 2002. Current distribution of sagebrush and associated vegetation in the western United States (excluding NM). [Geospatial data.] Interagency Sagebrush Working Group, http://sagemap.wr.usgs.gov Homer, C.G., Aldridge, C.L., Meyer, D.K., and Schell, S.J. 2014. Multi-Scale Remote Sensing Sagebrush Characterization with Regression Trees over Wyoming, USA; Laying a Foundation for Monitoring. International Journal of Applied Earth Observation and Geoinformation 14, Elsevier, US. LANDFIRE. 2010. 1.2.0 Existing Vegetation Type Layer. [Geospatial data.] U.S. Department of the Interior, Geological Survey, http://landfire.cr.usgs.gov/viewer/ Mason, R.R. 1999. The National Flood-Frequency Program—Methods For Estimating Flood Magnitude And Frequency In Rural Areas In Nevada U.S. Geological Survey Fact Sheet 123-98 September, 1999, Prepared by Robert R. Mason, Jr. and Kernell G. Ries III, of the U.S. Geological Survey; and Jeffrey N. King and Wilbert O. Thomas, Jr., of Michael Baker, Jr., Inc. http://pubs.usgs.gov/fs/fs-123-98/ Peterson, E. B. 2008. A Synthesis of Vegetation Maps for Nevada (Initiating a 'Living' Vegetation Map). Documentation and geospatial data, Nevada Natural Heritage Program, Carson City, Nevada, http://www.heritage.nv.gov/gis Xian, G., Homer, C., Rigge, M., Shi, H., and Meyer, D. 2015. Characterization of shrubland ecosystem components as continuous fields in the northwest United States. Remote Sensing of Environment 168:286-300. NOTE: This file does not include habitat areas for the Bi-State management area and the spatial extent is modified in comparison to Coates et al. 2014

Part of the American Dream is owning your own home. This map shows the percentage of occupied housing that has a mortgage or loan in the U.S., by state, county, tract and block group. The data shown is from the U.S. Census Bureau's SF1 and TIGER data sets for 2010. See the map package for this data. The map is multi-scale – it switches from state, to county, to tract, to block group data as the map zooms in. The polygon data was shorelined and selected rivers and lakes were also erased from the boundaries to give a more familiar look at specific scales. At each scale, a simple popup shares a few key statistics in a paragraph, and displays a chart of housing by type of ownership.The thematic classifications are centered around the U.S. average for housing with mortgages (about 40%). The center classification is characterized as “Average.” Its break points are based on one-half standard deviation around the mean. Breakpoints for the “Low” and “High” classes are also determined from one-half standard deviation (9.7%). “Very Low” and “Very High” classes capture the remaining values.The thematic colors use colors chosen to emphasize the “high” end of the values. Darker colors are used to represent high values, while lighter colors represent low values. The “Average” class color is neutral. As you zoom into the map, a stroke is added to the polygon boundaries to delineate the county, tract and block group boundaries without allowing them to dominate the map (as is the case with black, white or other strong colors for boundaries).The light gray canvas basemap was selected for this web map to draw attention to the thematic content.

A polygon feature class based on zip code boundaries showing the percentage of babies born in Miami-Dade County in 2010 with low birth weights. Low birth weight is classified as under 2501 grams.Updated: Not Planned The data was created using: Projected Coordinate System: WGS_1984_Web_Mercator_Auxiliary_SphereProjection: Mercator_Auxiliary_Sphere

A feature layer containing Hydro-Corrected Drainage Basin boundaries for drainage areas that impact Tallahassee and Leon County, Florida. This feature layer was generated from a 2009 digital elevation model of Leon County, Florida.Drainage Basins: Local level basins are the largest drainage unit tracked by Tallahassee-Leon County government and reflect final destinations of surface water drainage. Lake Jackson is, for example, a natural lake where a substantial amount of surface water runoff ultimately collects. Ames Sink (Lake Munson Basin) is a natural swallet (a sinkhole where surface water enters the aquifer) where surface watercourses terminate into the Floridan Aquifer. Lake Miccosukee is a water feature in part created from infrastructure that controls water elevation (weir/spillway) and a berm that prevents the surface water from draining into the aquifer by way of a swallet. 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.