This webmap displays the percent of population 25 years and over whose highest education completed is associate's degree. This webmap also contains the following layers: City of Corona Limits, State Boundary, County Boundary and Tract Boundary.

This map shows locations that provide ADN (associate degree nursing), AE-MSN (alternate entry master of science in nursing), Diploma, BSN (bachelor of science in nursing), DE-MSN (direct entry master of science in nursing), and LVN (licensed vocation nursing) certifications. The data includes information on pass rates from 2020 through 2024.This map was created with data from Texas Center for Nursing Workforce Studies and last updated in May 2025.

In this course, you will explore a variety of open-source technologies for working with geosptial data, performing spatial analysis, and undertaking general data science. The first component of the class focuses on the use of QGIS and associated technologies (GDAL, PROJ, GRASS, SAGA, and Orfeo Toolbox). The second component of the class introduces Python and associated open-source libraries and modules (NumPy, Pandas, Matplotlib, Seaborn, GeoPandas, Rasterio, WhiteboxTools, and Scikit-Learn) used by geospatial scientists and data scientists. We also provide an introduction to Structured Query Language (SQL) for performing table and spatial queries. This course is designed for individuals that have a background in GIS, such as working in the ArcGIS environment, but no prior experience using open-source software and/or coding. You will be asked to work through a series of lecture modules and videos broken into several topic areas, as outlined below. Fourteen assignments and the required data have been provided as hands-on opportunites to work with data and the discussed technologies and methods. If you have any questions or suggestions, feel free to contact us. We hope to continue to update and improve this course. This course was produced by West Virginia View (http://www.wvview.org/) with support from AmericaView (https://americaview.org/). This material is based upon work supported by the U.S. Geological Survey under Grant/Cooperative Agreement No. G18AP00077. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey. After completing this course you will be able to: apply QGIS to visualize, query, and analyze vector and raster spatial data. use available resources to further expand your knowledge of open-source technologies. describe and use a variety of open data formats. code in Python at an intermediate-level. read, summarize, visualize, and analyze data using open Python libraries. create spatial predictive models using Python and associated libraries. use SQL to perform table and spatial queries at an intermediate-level.

In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California’s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map California’s State Waters with consistent methods at a consistent scale. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. This approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of “lands” from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth. This data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013). The California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bay’s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Service–Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey. These web services for the Point Sur to Point Arguello map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://walrus.wr.usgs.gov/mapping/csmp/index.html. Each published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and photographic imagery; these “ground-truth” surveying data are available from the CSMP Video and Photograph Portal at https://doi.org/10.5066/F7J1015K. The “seafloor character” data layer shows classifications of the seafloor on the basis of depth, slope, rugosity (ruggedness), and backscatter intensity and which is further informed by the ground-truth-survey imagery. The “potential habitats” polygons are delineated on the basis of substrate type, geomorphology, seafloor process, or other attributes that may provide a habitat for a specific species or assemblage of organisms. Representative seismic-reflection profile data from the map area is also include and provides information on the subsurface stratigraphy and structure of the map area. The distribution and thickness of young sediment (deposited over the past about 21,000 years, during the most recent sea-level rise) is interpreted on the basis of the seismic-reflection data. The geologic polygons merge onshore geologic mapping (compiled from existing maps by the California Geological Survey) and new offshore geologic mapping that is based on integration of high-resolution bathymetry and backscatter imagery seafloor-sediment and rock samplesdigital camera and video imagery, and high-resolution seismic-reflection profiles. The information provided by the map sheets, pamphlet, and data catalog has a broad range of applications. High-resolution bathymetry, acoustic backscatter, ground-truth-surveying imagery, and habitat mapping all contribute to habitat characterization and ecosystem-based management by providing essential data for delineation of marine protected areas and ecosystem restoration. Many of the maps provide high-resolution baselines that will be critical for monitoring environmental change associated with climate change, coastal development, or other forcings. High-resolution bathymetry is a critical component for modeling coastal flooding caused by storms and tsunamis, as well as inundation associated with longer term sea-level rise. Seismic-reflection and bathymetric data help characterize earthquake and tsunami sources, critical for natural-hazard assessments of coastal zones. Information on sediment distribution and thickness is essential to the understanding of local and regional sediment transport, as well as the development of regional sediment-management plans. In addition, siting of any new offshore infrastructure (for example, pipelines, cables, or renewable-energy facilities) will depend on high-resolution mapping. Finally, this mapping will both stimulate and enable new scientific research and also raise public awareness of, and education about, coastal environments and issues. Web services were created using an ArcGIS service definition file. The ArcGIS REST service and OGC WMS service include all Point Sur to Point Arguello map area data layers. Data layers are symbolized as shown on the associated map sheets.

IntroductionBuilt environment attributes have been linked to cardiovascular disease (CVD) risk. Therefore, identifying built environment attributes that are associated with CVD risk is relevant for facilitating effective public health interventions.ObjectiveTo conduct a systematic review of literature to examine the influence of built environmental attributes on CVD risks.Data SourceMultiple database searches including Science direct, CINAHL, Masterfile Premier, EBSCO and manual scan of reference lists were conducted.Inclusion CriteriaStudies published in English between 2005 and April 2015 were included if they assessed one or more of the neighborhood environmental attributes in relation with any major CVD outcomes and selected risk factors among adults.Data ExtractionAuthor(s), country/city, sex, age, sample size, study design, tool used to measure neighborhood environment, exposure and outcome assessments and associations were extracted from eligible studies.ResultsEighteen studies met the inclusion criteria. Most studies used both cross-sectional design and Geographic Information System (GIS) to assess the neighborhood environmental attributes. Neighborhood environmental attributes were significantly associated with CVD risk and CVD outcomes in the expected direction. Residential density, safety from traffic, recreation facilities, street connectivity and high walkable environment were associated with physical activity. High walkable environment, fast food restaurants, supermarket/grocery stores were associated with blood pressure, body mass index, diabetes mellitus and metabolic syndrome. High density traffic, road proximity and fast food restaurants were associated with CVDs outcomes.ConclusionThis study confirms the relationship between neighborhood environment attributes and CVDs and risk factors. Prevention programs should account for neighborhood environmental attributes in the communities where people live.

Parcels and Land Ownership dataset current as of 2007. The showcase GIS layer associated with the Tennessee Base Mapping Program, the cadastral layer is maintained by local property assessors and the State Comptroller of the Treasury..

This data is provided by Gallatin College, Southwest Montana's two-year college that offers two-year associate degrees and one-year professional certificates. This data describes the demographics for both race and ethnicity as well as gender identity for all of the programs offered by Gallatin College.Data DictionaryRace/Ethinicity:A: AsianAIAN: American Indian or Alaska NativeBAA: Black or African AmericanHL: Hispanic or LatinoNHPI: Native Hawaiian or other Pacific IslanderW: WhiteOther: Some other raceTwo: Two or more racesGender IdentityFemaleMaleGNI: Gender not identifiedFind out more about Gallatin College

CrimeStat III is a spatial statistics program for the analysis of crime incident locations, developed by Ned Levine and Associates under the direction of Ned Levine, PhD, that was funded by grants from the National Institute of Justice (grants 1997-IJ-CX-0040, 1999-IJ-CX-0044, 2002-IJ-CX-0007, and 2005-IJ-CX-K037). The program is Windows-based and interfaces with most desktop GIS programs. The purpose is to provide supplemental statistical tools to aid law enforcement agencies and criminal justice researchers in their crime mapping efforts. CrimeStat is being used by many police departments around the country as well as by criminal justice and other researchers. The program inputs incident locations (e.g., robbery locations) in 'dbf', 'shp', ASCII or ODBC-compliant formats using either spherical or projected coordinates. It calculates various spatial statistics and writes graphical objects to ArcGIS, MapInfo, Surfer for Windows, and other GIS packages. CrimeStat is organized into five sections: Data Setup Primary file - this is a file of incident or point locations with X and Y coordinates. The coordinate system can be either spherical (lat/lon) or projected. Intensity and weight values are allowed. Each incident can have an associated time value. Secondary file - this is an associated file of incident or point locations with X and Y coordinates. The coordinate system has to be the same as the primary file. Intensity and weight values are allowed. The secondary file is used for comparison with the primary file in the risk-adjusted nearest neighbor clustering routine and the duel kernel interpolation. Reference file - this is a grid file that overlays the study area. Normally, it is a regular grid though irregular ones can be imported. CrimeStat can generate the grid if given the X and Y coordinates for the lower-left and upper-right corners. Measurement parameters - This page identifies the type of distance measurement (direct, indirect or network) to be used and specifies parameters for the area of the study region and the length of the street network. CrimeStat III has the ability to utilize a network for linking points. Each segment can be weighted by travel time, travel speed, travel cost or simple distance. This allows the interaction between points to be estimated more realistically. Spatial Description Spatial distribution - statistics for describing the spatial distribution of incidents, such as the mean center, center of minimum distance, standard deviational ellipse, the convex hull, or directional mean. Spatial autocorrelation - statistics for describing the amount of spatial autocorrelation between zones, including general spatial autocorrelation indices - Moran's I , Geary's C, and the Getis-Ord General G, and correlograms that calculate spatial autocorrelation for different distance separations - the Moran, Geary, Getis-Ord correlograms. Several of these routines can simulate confidence intervals with a Monte Carlo simulation. Distance analysis I - statistics for describing properties of distances between incidents including nearest neighbor analysis, linear nearest neighbor analysis, and Ripley's K statistic. There is also a routine that assigns the primary points to the secondary points, either on the basis of nearest neighbor or point-in-polygon, and then sums the results by the secondary point values. Distance analysis II - calculates matrices representing the distance between points for the primary file, for the distance between the primary and secondary points, and for the distance between either the primary or secondary file and the grid. 'Hot spot' analysis I - routines for conducting 'hot spot' analysis including the mode, the fuzzy mode, hierarchical nearest neighbor clustering, and risk-adjusted nearest neighbor hierarchical clustering. The hierarchical nearest neighbor hot spots can be output as ellipses or convex hulls. 'Hot spot' analysis II - more routines for conducting hot spot analysis including the Spatial and Temporal Analysis of Crime (STAC), K-means clustering, Anselin's local Moran, and the Getis-Ord local G statistics. The STAC and K-means hot spots can be output as ellipses or convex hulls. All of these routines can simulate confidence intervals with a Monte Carlo simulation. Spatial Modeling Interpolation I - a single-variable kernel density estimation routine for producin

In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California’s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map California’s State Waters with consistent methods at a consistent scale. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. This approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of “lands” from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth. This data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013). The California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bay’s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Service–Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey. These web services for the Offshore of Coal Oil Point map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://walrus.wr.usgs.gov/mapping/csmp/index.html. Each published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and photographic imagery; these “ground-truth” surveying data are available from the CSMP Video and Photograph Portal at https://doi.org/10.5066/F7J1015K. The “seafloor character” data layer shows classifications of the seafloor on the basis of depth, slope, rugosity (ruggedness), and backscatter intensity and which is further informed by the ground-truth-survey imagery. The “potential habitats” polygons are delineated on the basis of substrate type, geomorphology, seafloor process, or other attributes that may provide a habitat for a specific species or assemblage of organisms. Representative seismic-reflection profile data from the map area is also include and provides information on the subsurface stratigraphy and structure of the map area. The distribution and thickness of young sediment (deposited over the past about 21,000 years, during the most recent sea-level rise) is interpreted on the basis of the seismic-reflection data. The geologic polygons merge onshore geologic mapping (compiled from existing maps by the California Geological Survey) and new offshore geologic mapping that is based on integration of high-resolution bathymetry and backscatter imagery seafloor-sediment and rock samplesdigital camera and video imagery, and high-resolution seismic-reflection profiles. The information provided by the map sheets, pamphlet, and data catalog has a broad range of applications. High-resolution bathymetry, acoustic backscatter, ground-truth-surveying imagery, and habitat mapping all contribute to habitat characterization and ecosystem-based management by providing essential data for delineation of marine protected areas and ecosystem restoration. Many of the maps provide high-resolution baselines that will be critical for monitoring environmental change associated with climate change, coastal development, or other forcings. High-resolution bathymetry is a critical component for modeling coastal flooding caused by storms and tsunamis, as well as inundation associated with longer term sea-level rise. Seismic-reflection and bathymetric data help characterize earthquake and tsunami sources, critical for natural-hazard assessments of coastal zones. Information on sediment distribution and thickness is essential to the understanding of local and regional sediment transport, as well as the development of regional sediment-management plans. In addition, siting of any new offshore infrastructure (for example, pipelines, cables, or renewable-energy facilities) will depend on high-resolution mapping. Finally, this mapping will both stimulate and enable new scientific research and also raise public awareness of, and education about, coastal environments and issues. Web services were created using an ArcGIS service definition file. The ArcGIS REST service and OGC WMS service include all Offshore Coal Oil Point map area data layers. Data layers are symbolized as shown on the associated map sheets.

Lake County, Illinois Demographic Data. Explanation of field attributes: Total Population – The entire population of Lake County. White – Individuals who are of Caucasian race. This is a percent.African American – Individuals who are of African American race. This is a percent.Asian – Individuals who are of Asian race. This is a percent. Hispanic – Individuals who are of Hispanic ethnicity. This is a percent. Does not Speak English- Individuals who speak a language other than English in their household. This is a percent. Under 5 years of age – Individuals who are under 5 years of age. This is a percent. Under 18 years of age – Individuals who are under 18 years of age. This is a percent. 18-64 years of age – Individuals who are between 18 and 64 years of age. This is a percent. 65 years of age and older – Individuals who are 65 years old or older. This is a percent. Male – Individuals who are male in gender. This is a percent. Female – Individuals who are female in gender. This is a percent. High School Degree – Individuals who have obtained a high school degree. This is a percent. Associate Degree – Individuals who have obtained an associate degree. This is a percent. Bachelor’s Degree or Higher – Individuals who have obtained a bachelor’s degree or higher. This is a percent. Utilizes Food Stamps – Households receiving food stamps/ part of SNAP (Supplemental Nutrition Assistance Program). This is a percent. Median Household Income - A median household income refers to the income level earned by a given household where half of the homes in the area earn more and half earn less. This is a dollar amount. No High School – Individuals who have not obtained a high school degree. This is a percent. Poverty – Poverty refers to families and people whose income in the past 12 months is below the poverty level. This is a percent.

The Bureau of Indian Affairs (BIA) Bison Project will serve a variety of purposes that are designed to uphold the best of Tribal bison herd expansion interests, including a focus on ecosystem restoration through bison conservation. The Bison Project will foster practices that are traditional and culturally attentive to the historical coevolved relationship with bison to support the Tribe’s own self-determined well-being. Furthermore, the projects will work to foster the intent of the Department of the Interior Secretary’s Order 3410, the purpose of which is to restore wild and healthy populations of American bison and the prairie grassland ecosystem through collaboration among the Department’s Bureaus and partners such as other Federal agencies, states, Tribes, and landowners using the best available science and Indigenous Knowledge. The analysis in this dashboard is based on data provided to the BIA from a multitude of resources including but not limited to the BIA, Federally Recognized Tribes, and partner organizations. All data was current as of time of collection for this project, data will be updated as determined by the BIA. Data displayed within this application can vary at different times of the year as external factors may affect herd sizes and will not reflect changes in real time. Herd numbers can also decrease between data updates due to range management practices performed on the local level, not managed by the BIA. The Bison Program Application’s data is made up of a polygon feature layer and a point feature layer hosted on the BIA online portal. The Bison Polygon layer features the geospatial extent of known Bison ranches as provided to the BIA. Tribes without any GIS data on ranch boundaries will only be featured in the Bison points feature layer. Both feature layers contain data including, name of Tribe, herd size, rangeland acres, and a link to their Bison website (if available). The Bison Program Application will focus on ecosystem restoration through bison conservation and expansion and improved management of existing herds on Tribal trust lands, individual Indian allotment lands, or in areas managed by Tribes through treaties or agreements. The Bison Project will focus on bison conservation and expansion and improved management of existing herds on Tribal trust resources and describe the role of Tribal bison on ecosystem restoration on Tribal landscapes and altered Tribal environmental conditions. This can cover bison as indicator keystone species on agricultural pasture, grassland, and rangeland settings. The Bison Program Application also features data from partner organizations who focus on promoting the restoration of Bison. These organizations include The Nature Conservancy (TNC). For more information on data contributors, follow the links below. The feature layers used in this application from partner organizations are not managed by the BIA. The Nature Conservancy (TNC): https://www.nature.org/en-us/. Disclaimer: The analysis in this dashboard is based on the analysis of available data provided to the BIA from a multitude of resources including but not limited to the BIA, Federally Recognized Tribes, and partner organizations. All data was current as of time of collection for this project, data will be updated as determined by the BIA. Data displayed within this application can vary at different times of the year as external factors may affect available foliage due to weather or other uncontrollable circumstances. The number of Bison within herds may also change throughout the year and might not be accounted for within this application. Herd numbers can also decrease between data updates due to outside factors or range management practices performed on the local level, not managed by the BIA.This application also uses data provided from other sources such as The Nature Conservancy (TNC). This data is owned and maintained by their respective owners. These data sources have been developed from the best available sources. Although efforts have been made to ensure that the data are accurate and reliable, errors and variable conditions originating from source documents and/or the translation of information from source documents to the systems of record continue to exist. Users must be aware of these conditions and bear responsibility for the appropriate use of the information with respect to possible errors, scale, resolution, rectification, positional accuracy, development methodology, time period, environmental and climatic conditions and other circumstances specific to these data. The user is responsible for understanding the accuracy limitations of the data provided herein. The burden for determining fitness for use lies entirely with the user. The user should refer to the accompanying metadata notes for a description of the data and data development procedures.

In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California’s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map California’s State Waters with consistent methods at a consistent scale. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. This approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of “lands” from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth. This data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013). The California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bay’s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Service–Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey. These web services for the Offshore of Ventura map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://walrus.wr.usgs.gov/mapping/csmp/index.html. Each published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and photographic imagery; these “ground-truth” surveying data are available from the CSMP Video and Photograph Portal at https://doi.org/10.5066/F7J1015K. The “seafloor character” data layer shows classifications of the seafloor on the basis of depth, slope, rugosity (ruggedness), and backscatter intensity and which is further informed by the ground-truth-survey imagery. The “potential habitats” polygons are delineated on the basis of substrate type, geomorphology, seafloor process, or other attributes that may provide a habitat for a specific species or assemblage of organisms. Representative seismic-reflection profile data from the map area is also include and provides information on the subsurface stratigraphy and structure of the map area. The distribution and thickness of young sediment (deposited over the past about 21,000 years, during the most recent sea-level rise) is interpreted on the basis of the seismic-reflection data. The geologic polygons merge onshore geologic mapping (compiled from existing maps by the California Geological Survey) and new offshore geologic mapping that is based on integration of high-resolution bathymetry and backscatter imagery seafloor-sediment and rock samplesdigital camera and video imagery, and high-resolution seismic-reflection profiles. The information provided by the map sheets, pamphlet, and data catalog has a broad range of applications. High-resolution bathymetry, acoustic backscatter, ground-truth-surveying imagery, and habitat mapping all contribute to habitat characterization and ecosystem-based management by providing essential data for delineation of marine protected areas and ecosystem restoration. Many of the maps provide high-resolution baselines that will be critical for monitoring environmental change associated with climate change, coastal development, or other forcings. High-resolution bathymetry is a critical component for modeling coastal flooding caused by storms and tsunamis, as well as inundation associated with longer term sea-level rise. Seismic-reflection and bathymetric data help characterize earthquake and tsunami sources, critical for natural-hazard assessments of coastal zones. Information on sediment distribution and thickness is essential to the understanding of local and regional sediment transport, as well as the development of regional sediment-management plans. In addition, siting of any new offshore infrastructure (for example, pipelines, cables, or renewable-energy facilities) will depend on high-resolution mapping. Finally, this mapping will both stimulate and enable new scientific research and also raise public awareness of, and education about, coastal environments and issues. Web services were created using an ArcGIS service definition file. The ArcGIS REST service and OGC WMS service include all Offshore of Ventura map area data layers. Data layers are symbolized as shown on the associated map sheets.

This item is in mature support and is no longer updated. Available for historical reference only.This dataset contains the campus boundaries of Colleges and Universities of New Jersey mapped by parcel boundaries.The Colleges and Universities dataset is composed of any type of Post Secondary Education such as: colleges, universities, technical schools, trade schools, business schools, satellite (branch) campuses, etc. that grant First Professional, Associate, Bachelors, Masters, or Doctoral degrees. Secondary education facilities, Administrative offices, or Post Secondary Education facilities that are non degree granting schools are intended to be excluded from this dataset, but a few may be included. All data is non license restricted data that has been added from TGS research.

Dataset, GDB, and Online Map created by Renee Haley, NMCDC, May 2023 DATA ACQUISITION PROCESS

Scope and purpose of project: New Mexico is struggling to maintain its healthcare workforce, particularly in Rural areas. This project was undertaken with the intent of looking at flows of healthcare workers into and out of New Mexico at the most granular geographic level possible. This dataset, in combination with others (such as housing cost and availability data) may help us understand where our healthcare workforce is relocating and why.

The most relevant and detailed data on workforce indicators in the United States is housed by the Census Bureau's Longitudinal Employer-Household Dynamics, LEHD, System. Information on this system is available here:

https://lehd.ces.census.gov/

The Job-to-Job flows explorer within this system was used to download the data. Information on the J2J explorer can ve found here:

https://j2jexplorer.ces.census.gov/explore.html#1432012

The dataset was built from data queried with the LED Extraction Tool, which allows for the query of more intersectional and detailed data than the explorer. This is a link to the LED extraction tool:

https://ledextract.ces.census.gov/

The geographies used are US Metro areas as determined by the Census, (N=389). The shapefile is named lehd_shp_gb.zip, and can be downloaded under this section of the following webpage: 5.5. Job-to-Job Flow Geographies, 5.5.1. Metropolitan (Complete). A link to the download site is available below:

https://lehd.ces.census.gov/data/schema/j2j_latest/lehd_shapefiles.html

DATA CLEANING PROCESS

This dataset was built from 8 non intersectional datasets downloaded from the LED Extraction Tool.

Separate datasets were downloaded in order to obtain detailed information on the race, ethnicity, and educational attainment levels of healthcare workers and where they are migrating.

Datasets included information for the four separate quarters of 2021. It was not possible to download annual data, only quarterly. Quarterly data was summed in a later step to derive annual totals for 2021.

4 datasets for healthcare workers moving OUT OF New Mexico, with details on race, ethnicity, and educational attainment, were downloaded. 1 contained information on educational attainment, 2 contained information on 7 racial categories identifying as non- Hispanic, 3 contained information on those same 7 categories also identifying as Hispanic, and 4 contained information for workers identifying as white and Hispanic.

4 datasets for healthcare worker moving INTO New Mexico, with details on race, ethnicity, and educational attainment, were downloaded with the same details outlined above.

Each dataset was cleaned according to Data Template which kept key attributes and discarded excess information. Within each dataset, the J2J Indicators reflecting 6 different types of job migration were totaled in order to simplify analysis, as this information was not needed in detail.

After cleaning, each set of 4 datasets for workers moving INTO New Mexico were joined. The process was repeated for workers moving OUT OF New Mexico. This resulted 2 main datasets.

These 2 main datasets still listed all of the variables by each quarter of 2021. Because of this the data was split in JMP, so that attributes of educational attainment, race and ethnicity, of workers migrating by quarter were moved from rows to columns. After this, summary columns for the year of 2021 were derived. This resulted in totals columns for workers identifying as: 6 separate races and all ethnicities, all races and Hispanic, white-Hispanic, and workers of 6 different education levels, reflecting how many workers of each indicator migrated to and from metro areas in New Mexico in 2021.

The data split transposed duplicate rows reflecting differing worker attributes within the same metro area, resulting in one row for each metro area and reflecting the attributes in columns, thus resulting in a mappable dataset.

The 2 datasets were joined (on Metro Area) resulting in one master file containing information on healthcare workers entering and leaving New Mexico.

Rows (N=389) reflect all of the metro areas across the US, and each state. Rows include the 5 metro areas within New Mexico, and New Mexico State.

Columns (N=99) contain information on worker race, ethnicity and educational attainment, specific to each metro area in New Mexico.

78 of these rows reflect workers of specific attributes moving OUT OF the 5 specific Metro Areas in New Mexico and totals for NM State. This level of detail is intended for analyzing who is leaving what area of New Mexico, where they are going to, and why.

13 Columns reflect each worker attribute for healthcare workers moving INTO New Mexico by race, ethnicity and education level. Because all 5 metro areas and New Mexico state are contained in the rows, this information for incoming workers is available by metro area and at the state level - there is less possability for mapping these attributes since it was not realistic or possible to create a dataset reflecting all of these variables for every healthcare worker from every metro area in the US also coming into New Mexico (that dataset would have over 1,000 columns and be unmappable). Therefore this dataset is easier to utilize in looking at why workers are leaving the state but also includes detailed information on who is coming in.

The remaining 8 columns contain geographic information.

GIS AND MAPPING PROCESS

The master file was opened in Arc GIS Pro and the Shapefile of US Metro Areas was also imported

The excel file was joined to the shapefile by Metro Area Name as they matched exactly

The resulting layer was exported as a GDB in order to retain null values which would turn to zeros if exported as a shapefile.

This GDB was uploaded to Arc GIS Online, Aliases were inserted as column header names, and the layer was visualized as desired.

SYSTEMS USED

MS Excel was used for data cleaning, summing NM state totals, and summing quarterly to annual data.

JMP was used to transpose, join, and split data.

ARC GIS Desktop was used to create the shapefile uploaded to NMCDC's online platform.

VARIABLE AND RECODING NOTES

Summary of variables selected for datasets downloaded focused on educational attainment:

J2J Flows by Educational Attainment

Summary of variables selected for datasets downloaded focused on race and ethnicity:

J2J Flows by Race and Ethnicity

Note: Variables in Datasets 1 through 4 downloaded twice, once for workers coming into New Mexico and once for those leaving NM. VARIABLE: LEHD VARIABLE DEFINITION LEHD VARIABLE NOTES DETAILS OR URL FOR RAW DATA DOWNLOAD

Geography Type - State Origin and Destination State

Data downloaded for worker migration into and out of all US States

Geography Type - Metropolitan Areas Origin and Dest Metro Area

Data downloaded for worker migration into and out of all US Metro Areas

NAICS sectors North American Industry Classification System Under Firm Characteristics Only downloaded for Healthcare and Social Assistance Sectors

Other Firm Characteristics No Firm Age / Size Detail Under Firm Characteristics Downloaded data on all firm ages, sizes, and other details.

Worker Characteristics Education, Race, Ethnicity

Non Intersectional data aside from Race / Ethnicity data.

Sex Gender

0 - All Sexes Selected

Age Age

A00 All Ages (14-99)

Education Education Level E0, E1, E2, E3, 34, E5 E0 - All Education Categories, E1 - Less than high school, E2 - High school or equivalent, no college, E3 - Some college or Associate’s degree, E4 - Bachelor's degree or advanced degree, E5 - Educational attainment not available (workers aged 24 or younger)

Dataset 1 All Education Levels, E1, E2, E3, E4, and E5

RACE

A0, A1, A2, A3, A4, A5 OPTIONS: A0 All Races, A1 White Alone, A2 Black or African American Alone, A3 American Indian or Alaska Native Alone, A4 Asian Alone, A5 Native Hawaiian or Other Pacific Islander Alone, SDA7 Two or More Race Groups

ETHNICITY

A0, A1, A2 OPTIONS: A0 All Ethnicities, A1 Not Hispanic or Latino, A2 Hispanic or Latino

Dataset 2 All Races (A0) and All Ethnicities (A0)

Dataset 3 6 Races (A1 through A5) and All Ethnicities (A0)

Dataset 4 White (A1) and Hispanic or Latino (A1)

Quarter Quarter and Year

Data from all quarters of 2021 to sum into annual numbers; yearly data was not available

Employer type Sector: Private or Governmental

Query included all healthcare sector workflows from all employer types and firm sizes from every quarter of 2021

J2J indicator categories Detailed types of job migration

All options were selected for all datasets and totaled: AQHire, AQHireS, EE, EES, J2J, J2JS. Counts were selected vs. earnings, and data was not seasonally adjusted (unavailable).

NOTES AND RESOURCES

The following resources and documentation were used to navigate the LEHD and J2J Worker Flows system and to answer questions about variables:

https://lehd.ces.census.gov/data/schema/j2j_latest/lehd_public_use_schema.html

https://www.census.gov/history/www/programs/geography/metropolitan_areas.html

https://lehd.ces.census.gov/data/schema/j2j_latest/lehd_csv_naming.html

Statewide (New

Explore global climatic data to analyze the factors that constrain and enable agriculture. The GeoInquiry activity is available here.Educational standards addressed:APHG: V.B. Identify major agricultural production regions.APHG: V.B1. Associate major agricultural regions with bioclimatic zones. APHG: V.C. Identify rural land use and settlement patterns.This map is part of a Human Geography GeoInquiry activity. Learn more about GeoInquiries.

DEFINITION:Tax Law POINT is a generalized point representation of lands enrolled in the Managed Forest and Forest Crop Law Programs, collectively referred to as Tax Law Layers. Points are located at the center point of each 40-acre quarter-quarter section in which land is enrolled. Points do not identify specific enrollment location. Acreage enrolled from fractional or government lots are located either to the most approximate QQ, Q or S as possible. (Enrolled parcels are represented by the PLSS shape they lie within; however, the actual size of the enrolled property may be as small as 0.1 acres). The GIS layer was last updated March 5, 2025 to reflect conditions as of January 1, 2025. Corrections are made to the data throughout the year that may not be reflected in this snapshot.FEATURE TYPE(S):PointGEOGRAPHIC EXTENT:StatewideSOURCE SCALE:VariedPROJECTION:Wisconsin Transverse Mercator NAD 1983/1991 (WTM83/91)WKID: 3071PURPOSE/BACKGROUND:Wisconsin’s forest tax laws encourage sustainable forest management on private lands by providing a property tax incentive to landowners. Both the Managed Forest Law (MFL) and Forest Crop Law (FCL) encourage proper management of woodlands not only in their purposes and policies, but through a written management plan for a landowner’s property. The management plan incorporates landowner objectives, timber management, wildlife management, water quality and the environment as a whole to create healthy and productive forest. In exchange for following a written management plan and program rules, landowners pay forest tax law program rates in lieu of regular property taxes.FCL lands are open to the public for the following activities: hunting and fishing.MFL lands enrolled as open are open to the public for the following activities: hunting, fishing, hiking, sight-seeing, and cross-country skiing.Additional rules regarding public access may be reviewed here: https://dnr.wisconsin.gov/topic/forestlandowners/mflThe GIS feature class was created to be used in the Open Private Forest Lands web mapping application (Private Forest Lands Open to Public Recreation).Open Private Forest Lands (OPFL) Project Background:Provide a simple GIS web mapping application to display the approximate representations of over 1.3 million acres of Forest Tax Law lands (Managed Forest and Forest Crop Law) open to the public for hunting, hiking, fishing, cross-country skiing, and sightseeing. Display information to allow the public to access the lands without spending a lot of time cross checking plat books or contacting local county offices or the county Land Information Offices.Update Frequency:Semi-Annual (January, September). Edits to Tax Law entries can occur throughout the year, but most changes are not effective until January 1 except for landowner information. Landowner information edits are updated in the spatial views on a weekly basis. In addition, Forestry will re-generate taxlaw shapes as significant improvements to the data are completed. In January of each year, the feature class is re-generated to reflect new entries, changes to access, etc. effective January 1. January update: Update to reflect enrollments as of January.September update: Pre-hunting season update.The GIS layer was last updated March 5, 2025 to reflect conditions as of January 1, 2025. Corrections are made to the data throughout the year that may not be reflected in this snapshot.ATTRIBUTES:Field Descriptions:ORDER_NO: (c, 12) The Forestry property code of the feature. (Use as join field for if linking to landowner table information.)Format: 2-digit cnty – 3 digit seq no – 4 digit year of entryEx. 11-234-2013DNR_CTY_NO: (n, 2) The 2-digit DNR county code representing the predominant county in which the DTRSQQ falls.Format: Numbers, No commasEx: 37 (Marathon County)CNTY_NAME: (T, 11) County name of the predominant county in which the DTRSQQ falls.Ex: MarathonENTRY_YEAR: (t, 4) The year in which the order number was entered into the taxlaw program.Format: YYYYEx: 1999TAX_TYPE: (t, 3) Indicates whether the polygon is enrolled in MFL or FCL.Format: ALL CAPSPossible Values:MFL: Managed Forest LawFCL: Forest Crop LawAC_OP_PLS: (double) Acres, associated to the identified order number, that are enrolled in the identified PLSS as open. Additional polygons (DTRSQQ records) may contain additional acreage for the associated order number (see AC_OP_ORD for total open acreage associated with this order number). NOTE: This is not the total number of acres open with this DTRSQQ record. Any other order numbers with acreage in this DTRSQQ are identified in separate records by order number.AC_CL_PLS: (double) Acres, associated to the identified order number, that are enrolled in the identified PLSS as closed. Additional polygons (DTRSQQ records) may contain additional acreage for the associated order number (see AC_CL_ORD for total closed acreage associated with this order number). NOTE: This is not the total number of acres closed with this DTRSQQ record. Any other order numbers with acreage in this DTRSQQ are identified in separate records by order number.AC_TOT_PLS: (double) Total acres, associated to the identified order number, that are enrolled in the identified PLSS. Additional polygons (DTRSQQ records) may contain additional acreage for the associated order number (see AC_TOT_ORD for total acreage associated with this order number). NOTE: This is not the total number of acres enrolled within this DTRSQQ record. Any other order numbers with acreage in this DTRSQQ are identified in separate records by order number.ORDER_YRS: (t, 2) Total number of years the order will be enrolled in the program (under the associated order number). Format: Plan or order lengths are either 25 or 50 yearsEx: 50ORDER_EXP: (t, 20) Date that order number expires. All orders end on December 31. Format: December 31, YYYYEx: December 31, 2015OWNER_TEXT: (t, 30) Type of ownership. Ownership could be: Individual, Joint, Corporation, LLC, Partnership, LLP, Trust, etc.ACCNT_TYPE: (t, 1) Type of account.Possible Values:S: Small Account – landowners generally have less than 1,000 acres of forest land and the accounts are managed by DNR field foresters.L: Large Account – landowners generally have 1,000 acres or more of forest land and the accounts are managed by DNR Forest TaxAC_OP_ORD: (double) Total open acreage associated with the order number. AC_CL_ORD: (double) Total closed acreage associated with the order number.AC_TOT_ORD: (double) Total acreage associated with the order number.DTRSQQ_CO: (long) A concatenation of direction, township, range, section, quarter section, and quarter-quarter section used to approximate the location of the order number (or part of the order number). Each order number has separate records for each DTRSQQ where the order number resides. (Data source: 24K Landnet Spatial Database Technical Documentation)Format:1st Digit = Direction2nd & 3rd Digits = Township4th & 5th Digits = Range6th & 7th Digits = Section8th Digit = Quarter9th Digit = Quarter-QuarterEx: 441012812LEGAL_D_CO: (t, 5) Code describing legal description identified by order number.Format: 1st character:Blank = Entire (Govt Lot)D = Entire (PLSS)P = Part ofE = Entire Excluding ROW2nd character:L = Govt LotBlank = PLSSCharacters 3-5:If PLSS, 001-016 are StandardIf PLSS, 017-060 are FractionalIf Govt Lot, this is the Govt Lot #Ex: PL003LEGAL_DESC: (t, 100) Translated legal description code. Ex: GOV LOT 3, PART OFDTRSLD_TXT: (t, 2380) Field generated to convert DTRSQQ and legal description codes to a text description of the PLSS where the enrollment is located. Includes a note indicating if a record includes a fractional correction.Ex: T02-R01W-S05, Part of the NE of the NW (fractional correction)PARCEL_NO: (t, 255) County created parcel number. (Parcel level information not yet available for all records.)Format: Varies by countyEx: 07-04-59MCD_NAME: (t, 50) Municipal Civil Division (MCD) name.Ex: Solon SpringsMCD_TYPE_C: (t, 1) Type of Municipal Civil Division (MCD). Format: ALL CAPSPossible Values:T: TownV: VillageC: CityPLSS_LEVEL: (t, 2) PLSS level to which the record is located. Format: ALL CAPSPossible Values:QQ: Quarter-quarterQ: QuarterS: SectionCHNG_BY: (c, 30) The user who last updated the record.Ex: klauscCHNG_DATE: (date) Date the record was last changed.Format: MM/DD/YYYY Ex: 10/23/2012ACCESS: (t, 1) Indicates whether the quarter-quarter contains areas which are open to the public, closed to the public, or both.Format: ALL CAPSPossible Values:O: QQ contains areas that are Open to the publicC: QQ contains areas that are Closed to the publicB: QQ contains Both open and closed areas.ADDITIONAL INFORMATION:Tax law programs: https://dnr.wisconsin.gov/topic/forestlandowners/mflWeb mapping application: https://dnr.wisconsin.gov/topic/forestlandowners/opentopublicappCONTACT PERSON(S):GIS contact: Laura Waddle - GIS Specialist, (608) 320-4648, Laura.Waddle@wisconsin.govResource contact: <>R.J. Wickham - Tax Law Section Chief, (920) 369-6248, Richard.Wickham@wisconsin.govCOPYRIGHT:The material is for the noncommercial use of the general public. The fair use guidelines of the U.S. copyright statutes apply to all material on the Department of Natural Resources Webpages and linked agency Webpages. The Department of Natural Resources shall remain the sole and exclusive owner of all rights, title and interest in and to all specifically copyrighted information created and posted for inclusion in this system. Photographs and graphics on the Department website are either the property of the state department or the state agency that holds a license to use and display the material. For copy or use of information on the Department website that is outside of the fair use provisions of copyright law, please seek permission from the individual listed as responsible for the page. If you have any questions on using material on the Department web pages please e-mail the specific

Click here to downloadClick for metadataService URL: https://gis.dnr.wa.gov/site2/rest/services/Public_Forest_Practices/WADNR_PUBLIC_FP_Rule/MapServer/7This feature class was developed as a cooperative project between the Department of Natural Resources Forest Practices Division, the Department of Ecology, and the Olympic National Forest. The data set was designed as a polygon coverage, delineated on 1:250,000- scale map overlays and digitized in 1991. It was plotted and proofed, but not completed at that time. Beginning in August, 1994 the coverage STRMTEMP was edited, corrected, and proofed. The data set is now a polygon feature class and shows only Class AA, A, and B polygons. It does not address Lake Class completely (some lakes are delineated, some not); see notes below for explanation. Specific conditions of certain stream segments are also not addressed by the feature class. The WAC MUST be referred to whenever this data set is used. The 1991 MPL coverage was used in delineating the extent of AA polygons. Changes through time in Federal land boundaries may affect the classification of waters in those lands. SUMMARY OF TEMPERATURE CLASSIFICATIONS DESIGNATED IN CHAPTER 173-201A WAC WATERQUALITY STANDARDS FOR SURFACE WATERS OF THE STATE OF WASHINGTON WAC 173-201A-030 General water use and criteria classes (1) Class AA (extraordinary) Temperature shall not exceed 16.0 degrees C (freshwater) or 13.0 degrees C (marine water) due to human activities. (2) Class A (excellent) Temperature shall not exceed 18.0 degrees C (freshwater) or 16.0 degrees C (marine water) due to human activities. (3) Class B (good) Temperature shall not exceed 21.0 degrees C (freshwater) or 19.0 degrees C (marine water) due to human activities. (4) Class C (fair) Temperature shall not exceed 22.0 degrees C due to human activities. For all of the above classes, when natural conditions exceed the listed temperature, no temperature increases will be allowed which will raise the receiving water temperature by greater than 0.3 degrees C. (5) Lake Class Temperature - no measurable change from natural conditions. *** Notes regarding WAC 173-201A sections 130 and 140 - Specific classifications for fresh and marine waters: All lakes and their feeder streams are classified as Lake Class. In this data set some large lakes and their feeder streams have had polygons created around them. These are shown as Class AA. Many lakes too small to be separately delineated may be contained within Class B, A, or AA polygons. Some stream segments have special conditions applied to their temperature standards. These conditions are listed in the WAC but are not delineated in the polygon coverage. These include the Columbia River, Duwamish River, Grande Ronde River, Hoquiam River, Lake Washington Ship Canal, Mill Creek (near Walla Walla), Palouse River, Pend Oreille River, Puyallup River, Skagit River, Snake River, Spokane River, Walla Walla River, Wishkah River, Yakima River, and Tacoma city waterway in Commencement Bay. These Special temperature designations are for the stream waters in the listed segment only. Temperature standards for all waters feeding that segment are as shown in the polygon coverage. This data set is a polygon coverage, and does not contain information on all surface waters listed in the WAC. All questions pertaining to temperature classification of surface waters in Washington must be clarified by referring to the WAC.

This service is available to all ArcGIS Online users with organizational accounts. For more information on this service, including the terms of use, visit us online at http://goto.arcgisonline.com/landscape6/World_Bioclimates.Climate plays a major role in determining the distribution of plants and animals. Bioclimatology, the study of climate as it affects and is affected by living organisms, is key to understanding the patterns of forests and deserts on the landscape, where productive agricultural lands may be found, and how changes in the climate will affect rare species. This layer is part of the Ecophysiographic Project and is one of the four input layers used to create the World Ecological Land Units Map.Dataset Summary This layer provides access to a 250m cell-sized raster with a bioclimatic stratification. The source dataset was a 30-arcsecond resolution raster (equivalent to 0.86 km2 at the equator or about a 920m pixel size). The layer has the following attributes: Temperature Description - Seven classes based on the number of growing degree days (the monthly mean temperature multiplied by number of days in the month summed for all months). The 1950 to 2000 monthly average temperature was used to calculate growing degree days. Values in this field and associated number of growing degree days are:Temperature DescriptionGrowing Degree DaysVery Hot9,000 – 13,500Hot7,000 – 9,000Warm4,500 – 7,000Cool2,500 – 4,500Cold1,000 – 2,500Very Cold300 – 1,000Arctic0 - 300Aridity Description - Six classes based on an index of aridity calculated by dividing precipitation by evapotranspiration. Precipitation and evapotranspiration are average values from 1950 to 2000.Aridity DescriptionAridity IndexVery Wet1.5 – 70Wet1.0 – 1.5Moist0.6 – 1.0Semi-dry0.3 – 0.6Dry0.1 – 0.3Very Dry0.01 – 0.1Bioclimate Class - a 2-part description that combines the value of the Temperature Description field and the Aridity Description field. The alias for this field is ELU Bioclimate Reclass. This layer was created by modifying the dataset documented in the publication: Metzger and others. 2012. A high-resolution bioclimate map of the world: a unifying framework for global biodiversity research and monitoring. What can you do with this layer? This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop.This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometers on a side or an area approximately the size of Europe. A service is available providing access to the data table associated with this layer. The data table services can be used by developers to quickly and efficiently query the data and to create custom applications. For more information see the World Ecophysiographic Tables.This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

Climate plays a major role in determining the distribution of plants and animals. Bioclimatology, the study of climate as it affects and is affected by living organisms, is key to understanding the patterns of forests and deserts on the landscape, where productive agricultural lands may be found, and how changes in the climate will affect rare species. This layer is part of the Ecophysiographic Project and is one of the four input layers used to create the World Ecological Land Units Map.Dataset Summary This layer provides access to a 250m cell-sized raster with a bioclimatic stratification. The source dataset was a 30-arcsecond resolution raster (equivalent to 0.86 km2 at the equator or about a 920m pixel size). The layer has the following attributes: Temperature Description - Seven classes based on the number of growing degree days (the monthly mean temperature multiplied by number of days in the month summed for all months). The 1950 to 2000 monthly average temperature was used to calculate growing degree days. Values in this field and associated number of growing degree days are:Temperature DescriptionGrowing Degree DaysVery Hot9,000 – 13,500Hot7,000 – 9,000Warm4,500 – 7,000Cool2,500 – 4,500Cold1,000 – 2,500Very Cold300 – 1,000Arctic0 - 300Aridity Description - Six classes based on an index of aridity calculated by dividing precipitation by evapotranspiration. Precipitation and evapotranspiration are average values from 1950 to 2000.Aridity DescriptionAridity IndexVery Wet1.5 – 70Wet1.0 – 1.5Moist0.6 – 1.0Semi-dry0.3 – 0.6Dry0.1 – 0.3Very Dry0.01 – 0.1Bioclimate Class - a 2-part description that combines the value of the Temperature Description field and the Aridity Description field. The alias for this field is ELU Bioclimate Reclass. This layer was created by modifying the dataset documented in the publication: Metzger and others. 2012. A high-resolution bioclimate map of the world: a unifying framework for global biodiversity research and monitoring. What can you do with this layer? This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop.This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometers on a side or an area approximately the size of Europe. A service is available providing access to the data table associated with this layer. The data table services can be used by developers to quickly and efficiently query the data and to create custom applications. For more information see the World Ecophysiographic Tables.This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.