This line shapefile represents Matadero, Deer, and Barron creeks in Santa Clara County, California. This layer is part of a collection of GIS data created and managed by the Jasper Ridge Biological Preserve.
This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.
This line shapefile represents Matadero, Deer, and Barron creeks in Santa Clara County, California. This layer is part of a collection of GIS data created and managed by the Jasper Ridge Biological Preserve.
This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

This polygon shapefile represents Searsville Lake in the Jasper Ridge Biological Preserve (JRBP) as of 2010. This layer is part of a collection of GIS data created and managed by the Jasper Ridge Biological Preserve.
This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.
This polygon shapefile represents Searsville Lake in the Jasper Ridge Biological Preserve (JRBP) as of 2010. This layer is part of a collection of GIS data created and managed by the Jasper Ridge Biological Preserve.
This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

This polygon shapefile represents the boundaries of Searsville Marsh in the Jasper Ridge Biological Preserve (JRBP). This layer is part of a collection of GIS data created and managed by the Jasper Ridge Biological Preserve.
This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.
This polygon shapefile represents the boundaries of Searsville Marsh in the Jasper Ridge Biological Preserve (JRBP). This layer is part of a collection of GIS data created and managed by the Jasper Ridge Biological Preserve.
This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

This polygon shapefile containing 2010 U.S. Census blocks for San Joaquin County, California was extracted from the U.S. Census Bureau's Master Address File/Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census Blocks are statistical areas bounded on all sides by visible features, such as streets, roads, streams, and railroad tracks, and/or by nonvisible boundaries such as city, town, township, and county limits, and short line-of-sight extensions of streets and roads. Census blocks are relatively small in area; for example, a block in a city bounded by streets. However, census blocks in remote areas are often large and irregular and may even be many square miles in area. A common misunderstanding is that data users think census blocks are used geographically to build all other census geographic areas, rather all other census geographic areas are updated and then used as the primary constraints, along with roads and water features, to delineate the tabulation blocks. As a result, all 2010 Census blocks nest within every other 2010 Census geographic area, so that Census Bureau statistical data can be tabulated at the block level and aggregated up to the appropriate geographic areas. Census blocks cover all territory in the United States, Puerto Rico, and the Island Areas (American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands). Blocks are the smallest geographic areas for which the Census Bureau publishes data from the decennial census. A block may consist of one or more faces.
In order for others to use the information in the Census MAF/TIGER database in a geographic information system (GIS) or for other geographic applications, the Census Bureau releases to the public extracts of the database in the form of TIGER/Line Shapefiles.
This polygon shapefile containing 2010 U.S. Census blocks for San Joaquin County, California was extracted from the U.S. Census Bureau's Master Address File/Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census Blocks are statistical areas bounded on all sides by visible features, such as streets, roads, streams, and railroad tracks, and/or by nonvisible boundaries such as city, town, township, and county limits, and short line-of-sight extensions of streets and roads. Census blocks are relatively small in area; for example, a block in a city bounded by streets. However, census blocks in remote areas are often large and irregular and may even be many square miles in area. A common misunderstanding is that data users think census blocks are used geographically to build all other census geographic areas, rather all other census geographic areas are updated and then used as the primary constraints, along with roads and water features, to delineate the tabulation blocks. As a result, all 2010 Census blocks nest within every other 2010 Census geographic area, so that Census Bureau statistical data can be tabulated at the block level and aggregated up to the appropriate geographic areas. Census blocks cover all territory in the United States, Puerto Rico, and the Island Areas (American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands). Blocks are the smallest geographic areas for which the Census Bureau publishes data from the decennial census. A block may consist of one or more faces.
In order for others to use the information in the Census MAF/TIGER database in a geographic information system (GIS) or for other geographic applications, the Census Bureau releases to the public extracts of the database in the form of TIGER/Line Shapefiles.

This polygon shapefile represents 2010 census designated places (CDP) in Monterey County, California. Places, for the reporting of decennial census data, include census designated places, consolidated cities, and incorporated places. Each place is assigned a five-digit Federal Information Processing Standards (FIPS) code, based on the alphabetical order of the place name within each state. If place names are duplicated within a state, and they represent distinctly different areas, a separate code is assigned to each place name alphabetically by primary county in which each place is located, or if both places are in the same county, alphabetically by their legal description (for example, ''city'' before ''village''). Census designated places (CDPs) are delineated for each decennial census as the statistical counterparts of incorporated places. CDPs are delineated to provide census data for concentrations of population, housing, and commercial structures that are identifiable by name but are not within an incorporated place. CDP boundaries usually are defined in cooperation with state, local, and tribal officials. These boundaries, which usually coincide with visible features or the boundary of an adjacent incorporated place or other legal entity boundary, have no legal status, nor do these places have officials elected to serve traditional municipal functions. CDP boundaries may change from one decennial census to the next with changes in the settlement pattern; a CDP with the same name as in an earlier census does not necessarily have the same boundary.Consolidated City. An incorporated place that has combined its governmental functions with a county or subcounty entity but contains one or more other incorporated places that continue to function as local governments within the consolidated government. This layer is part of a collection of GIS data for Monterey County in California.
This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.
This polygon shapefile represents 2010 census designated places (CDP) in Monterey County, California. Places, for the reporting of decennial census data, include census designated places, consolidated cities, and incorporated places. Each place is assigned a five-digit Federal Information Processing Standards (FIPS) code, based on the alphabetical order of the place name within each state. If place names are duplicated within a state, and they represent distinctly different areas, a separate code is assigned to each place name alphabetically by primary county in which each place is located, or if both places are in the same county, alphabetically by their legal description (for example, ''city'' before ''village''). Census designated places (CDPs) are delineated for each decennial census as the statistical counterparts of incorporated places. CDPs are delineated to provide census data for concentrations of population, housing, and commercial structures that are identifiable by name but are not within an incorporated place. CDP boundaries usually are defined in cooperation with state, local, and tribal officials. These boundaries, which usually coincide with visible features or the boundary of an adjacent incorporated place or other legal entity boundary, have no legal status, nor do these places have officials elected to serve traditional municipal functions. CDP boundaries may change from one decennial census to the next with changes in the settlement pattern; a CDP with the same name as in an earlier census does not necessarily have the same boundary.Consolidated City. An incorporated place that has combined its governmental functions with a county or subcounty entity but contains one or more other incorporated places that continue to function as local governments within the consolidated government. This layer is part of a collection of GIS data for Monterey County in California.
This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

This polygon shapefile contains residential and commercial land use as well as density as of 2010 in Monterey County, California. This layer is part of a collection of GIS data for Monterey County in California.
This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.
This polygon shapefile contains residential and commercial land use as well as density as of 2010 in Monterey County, California. This layer is part of a collection of GIS data for Monterey County in California.
This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

This point shapefile depicts Amtrak inter-city railroad passenger terminals in the United States and Canada. Attribute data include services and passenger amenities provided at the station. This data is collected and maintained by the Federal Railroad Administration (FRA) and was last updated in 2010. This layer is part of the 2014 National Transportation Atlas Database.
The National Transportation Atlas Databases 2014 (NTAD2014) is a set of nationwide geographic datasets of transportation facilities, transportation networks, associated infrastructure and other political and administrative entities. These datasets include spatial information for transportation modal networks and intermodal terminals, as well as the re¬lated attribute information for these features. This data supports research, analysis, and decision-making across all transportation modes. It is most useful at the national level, but has major applications at regional, state and local scales throughout the transportation community. The data used to compile NTAD2014 was provided by our partners within the United States Department of Transportation (USDOT) and by other agencies throughout the United States Federal Government. These contributors are the actual data stewards and are ultimately responsible for the maintenance and accuracy of their data. This data provides an updated version of the location and attribute information for all Amtrak stations in the United States and Canada. This data is used for national and network analysis applications.
This point shapefile depicts Amtrak inter-city railroad passenger terminals in the United States and Canada. Attribute data include services and passenger amenities provided at the station. This data is collected and maintained by the Federal Railroad Administration (FRA) and was last updated in 2010. This layer is part of the 2014 National Transportation Atlas Database.
The National Transportation Atlas Databases 2014 (NTAD2014) is a set of nationwide geographic datasets of transportation facilities, transportation networks, associated infrastructure and other political and administrative entities. These datasets include spatial information for transportation modal networks and intermodal terminals, as well as the re¬lated attribute information for these features. This data supports research, analysis, and decision-making across all transportation modes. It is most useful at the national level, but has major applications at regional, state and local scales throughout the transportation community. The data used to compile NTAD2014 was provided by our partners within the United States Department of Transportation (USDOT) and by other agencies throughout the United States Federal Government. These contributors are the actual data stewards and are ultimately responsible for the maintenance and accuracy of their data. This data provides an updated version of the location and attribute information for all Amtrak stations in the United States and Canada. This data is used for national and network analysis applications.

This polygon shapefile depicts eelgrass beds on the central California coast that provide foraging, breeding, or nursery areas for invertebrates, fish and birds. The most common type of seagrass in California is Zostera, or eelgrass, which grows under water in estuaries and in shallow coastal bays of the ecoregion. This flowering plant, not an alga, occurs in dense beds and helps prevent erosion and maintain stability near shore by anchoring sediment with its spreading rhizomes and slowing water flow. This file aggregates data from several sources across multiple years. Features shown here should not be considered a representation of condition for any given year, but represent the maximum extent of eelgrass found across multiple surveys over multiple years.
This coverage represents eelgrass distribution on the central California coast.
This polygon shapefile depicts eelgrass beds on the central California coast that provide foraging, breeding, or nursery areas for invertebrates, fish and birds. The most common type of seagrass in California is Zostera, or eelgrass, which grows under water in estuaries and in shallow coastal bays of the ecoregion. This flowering plant, not an alga, occurs in dense beds and helps prevent erosion and maintain stability near shore by anchoring sediment with its spreading rhizomes and slowing water flow. This file aggregates data from several sources across multiple years. Features shown here should not be considered a representation of condition for any given year, but represent the maximum extent of eelgrass found across multiple surveys over multiple years.
This coverage represents eelgrass distribution on the central California coast.

This line shapefile represents fault lines within the offshore area of Bodega Head in California. Onshore investigations indicate that this section of the San Andreas Fault onland has an estimated slip rate of about 17 to 25 mm/yr (Bryant and Lundberg, 2002). Emergent marine terraces at Bodega Bay record uplift rates associated with the fault system of about 0.1 mm/yr (Landis and others, 2004). The devastating Great 1906 California earthquake (M 7.8) is thought to have nucleated on the San Andreas Fault about 70 kilometers south of this map area offshore of San Francisco (e.g., Bolt, 1968; Lomax, 2005), with the rupture extending northward through the Offshore of Bodega Head map area to the south flank of Cape Mendocino. Faults were primarily mapped by interpretation of seismic reflection profile data (see field activity S-15-10-NC). The seismic reflection profiles were collected in 2010. This layer is part of USGS Data Series 781.
In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP) to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats and geology within the 3-nautical-mile limit of California's State Waters. CSMP has divided coastal California into 110 map blocks, each to be published individually as United States Geological Survey Open-File Reports (OFRs) or Scientific Investigations Maps (SIMs) at a scale of 1:24,000. Maps display seafloor morphology and character, identify potential marine benthic habitats and illustrate both the seafloor geology and shallow (to about 100 m) subsurface geology. Data layers for bathymetry, bathymetric contours, acoustic backscatter, seafloor character, potential benthic habitat and offshore geology were created for each map block, as well as regional-scale data layers for sediment thickness, depth to transition, transgressive contours, isopachs, predicted distributions of benthic macro-invertebrates and visual observations of benthic habitat from video cruises over the entire state. This coverage can be used to aid in assessments and mitigation of geologic hazards in the coastal region and to provide sufficient geologic information for land-use and land-management decisions both onshore and offshore. These data are intended for science researchers, students, policy makers, and the general public. This information is not intended for navigational purposes.The data can be used with geographic information systems (GIS) software to display geologic and oceanographic information. Additionally, this coverage can provide a geologic map for the public and geoscience community to aid in assessments and mitigation of geologic hazards in the coastal region and sufficient geologic information for land-use and land-management decisions both onshore and offshore. This information is not intended for navigational purposes.
This line shapefile represents fault lines within the offshore area of Bodega Head in California. Onshore investigations indicate that this section of the San Andreas Fault onland has an estimated slip rate of about 17 to 25 mm/yr (Bryant and Lundberg, 2002). Emergent marine terraces at Bodega Bay record uplift rates associated with the fault system of about 0.1 mm/yr (Landis and others, 2004). The devastating Great 1906 California earthquake (M 7.8) is thought to have nucleated on the San Andreas Fault about 70 kilometers south of this map area offshore of San Francisco (e.g., Bolt, 1968; Lomax, 2005), with the rupture extending northward through the Offshore of Bodega Head map area to the south flank of Cape Mendocino. Faults were primarily mapped by interpretation of seismic reflection profile data (see field activity S-15-10-NC). The seismic reflection profiles were collected in 2010. This layer is part of USGS Data Series 781.
In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP) to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats and geology within the 3-nautical-mile limit of California's State Waters. CSMP has divided coastal California into 110 map blocks, each to be published individually as United States Geological Survey Open-File Reports (OFRs) or Scientific Investigations Maps (SIMs) at a scale of 1:24,000. Maps display seafloor morphology and character, identify potential marine benthic habitats and illustrate both the seafloor geology and shallow (to about 100 m) subsurface geology. Data layers for bathymetry, bathymetric contours, acoustic backscatter, seafloor character, potential benthic habitat and offshore geology were created for each map block, as well as regional-scale data layers for sediment thickness, depth to transition, transgressive contours, isopachs, predicted distributions of benthic macro-invertebrates and visual observations of benthic habitat from video cruises over the entire state. This coverage can be used to aid in assessments and mitigation of geologic hazards in the coastal region and to provide sufficient geologic information for land-use and land-management decisions both onshore and offshore. These data are intended for science researchers, students, policy makers, and the general public. This information is not intended for navigational purposes.The data can be used with geographic information systems (GIS) software to display geologic and oceanographic information. Additionally, this coverage can provide a geologic map for the public and geoscience community to aid in assessments and mitigation of geologic hazards in the coastal region and sufficient geologic information for land-use and land-management decisions both onshore and offshore. This information is not intended for navigational purposes.

This line shapefile contains geologic folds of the offshore area of Bodega Head in California. Onshore investigations indicate that this section of the San Andreas Fault onland has an estimated slip rate of about 17 to 25 mm/yr (Bryant and Lundberg, 2002). Emergent marine terraces at Bodega Bay record uplift rates associated with the fault system of about 0.1 mm/yr (Landis and others, 2004). The devastating Great 1906 California earthquake (M 7.8) is thought to have nucleated on the San Andreas Fault about 70 kilometers south of this map area offshore of San Francisco (e.g., Bolt, 1968; Lomax, 2005), with the rupture extending northward through the Offshore of Bodega Head map area to the south flank of Cape Mendocino. Folds were primarily mapped by interpretation of seismic reflection profile data (see field activity S-15-10-NC). The seismic reflection profiles were collected in 2010. This layer is part of USGS Data Series 781.
In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP) to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats and geology within the 3-nautical-mile limit of California's State Waters. CSMP has divided coastal California into 110 map blocks, each to be published individually as United States Geological Survey Open-File Reports (OFRs) or Scientific Investigations Maps (SIMs) at a scale of 1:24,000. Maps display seafloor morphology and character, identify potential marine benthic habitats and illustrate both the seafloor geology and shallow (to about 100 m) subsurface geology. Data layers for bathymetry, bathymetric contours, acoustic backscatter, seafloor character, potential benthic habitat and offshore geology were created for each map block, as well as regional-scale data layers for sediment thickness, depth to transition, transgressive contours, isopachs, predicted distributions of benthic macro-invertebrates and visual observations of benthic habitat from video cruises over the entire state. This coverage can be used to aid in assessments and mitigation of geologic hazards in the coastal region and to provide sufficient geologic information for land-use and land-management decisions both onshore and offshore. These data are intended for science researchers, students, policy makers, and the general public. This information is not intended for navigational purposes.The data can be used with geographic information systems (GIS) software to display geologic and oceanographic information. Additionally, this coverage can provide a geologic map for the public and geoscience community to aid in assessments and mitigation of geologic hazards in the coastal region and sufficient geologic information for land-use and land-management decisions both onshore and offshore. This information is not intended for navigational purposes.
This line shapefile contains geologic folds of the offshore area of Bodega Head in California. Onshore investigations indicate that this section of the San Andreas Fault onland has an estimated slip rate of about 17 to 25 mm/yr (Bryant and Lundberg, 2002). Emergent marine terraces at Bodega Bay record uplift rates associated with the fault system of about 0.1 mm/yr (Landis and others, 2004). The devastating Great 1906 California earthquake (M 7.8) is thought to have nucleated on the San Andreas Fault about 70 kilometers south of this map area offshore of San Francisco (e.g., Bolt, 1968; Lomax, 2005), with the rupture extending northward through the Offshore of Bodega Head map area to the south flank of Cape Mendocino. Folds were primarily mapped by interpretation of seismic reflection profile data (see field activity S-15-10-NC). The seismic reflection profiles were collected in 2010. This layer is part of USGS Data Series 781.
In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP) to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats and geology within the 3-nautical-mile limit of California's State Waters. CSMP has divided coastal California into 110 map blocks, each to be published individually as United States Geological Survey Open-File Reports (OFRs) or Scientific Investigations Maps (SIMs) at a scale of 1:24,000. Maps display seafloor morphology and character, identify potential marine benthic habitats and illustrate both the seafloor geology and shallow (to about 100 m) subsurface geology. Data layers for bathymetry, bathymetric contours, acoustic backscatter, seafloor character, potential benthic habitat and offshore geology were created for each map block, as well as regional-scale data layers for sediment thickness, depth to transition, transgressive contours, isopachs, predicted distributions of benthic macro-invertebrates and visual observations of benthic habitat from video cruises over the entire state. This coverage can be used to aid in assessments and mitigation of geologic hazards in the coastal region and to provide sufficient geologic information for land-use and land-management decisions both onshore and offshore. These data are intended for science researchers, students, policy makers, and the general public. This information is not intended for navigational purposes.The data can be used with geographic information systems (GIS) software to display geologic and oceanographic information. Additionally, this coverage can provide a geologic map for the public and geoscience community to aid in assessments and mitigation of geologic hazards in the coastal region and sufficient geologic information for land-use and land-management decisions both onshore and offshore. This information is not intended for navigational purposes.

This polyline shapefile depicts the spatial component of the FAF network derived from National Highway System Version 2009.11 and contains state primary and secondary roads, National Highway System (NHS), National Network (NN) and several intermodal connectors as appropriate for the freight network modeling. The network consists of over 447,808 miles of equivalent road mileage. The data set covers the 48 contiguous States plus the District of Columbia, Alaska and Hawaii. The nominal scale of the data set is 1:100,000 with a maximal positional error of ±80 meters. This layer is part of the 2014 National Transportation Atlas Database.
The National Transportation Atlas Databases 2014 (NTAD2014) is a set of nationwide geographic datasets of transportation facilities, transportation networks, associated infrastructure and other political and administrative entities. These datasets include spatial information for transportation modal networks and intermodal terminals, as well as the re¬lated attribute information for these features. This data supports research, analysis, and decision-making across all transportation modes. It is most useful at the national level, but has major applications at regional, state and local scales throughout the transportation community. The data used to compile NTAD2014 was provided by our partners within the United States Department of Transportation (USDOT) and by other agencies throughout the United States Federal Government. These contributors are the actual data stewards and are ultimately responsible for the maintenance and accuracy of their data. The Freight Analysis Framework (FAF) integrates data from a variety of sources to create a comprehensive picture of freight movement among states and major metropolitan areas by all modes of transportation. The FAF region boundaries are a geographic database of state and metropolitan boundaries. The database includes boundaries for all 123 regions, including Washington D.C.
This polyline shapefile depicts the spatial component of the FAF network derived from National Highway System Version 2009.11 and contains state primary and secondary roads, National Highway System (NHS), National Network (NN) and several intermodal connectors as appropriate for the freight network modeling. The network consists of over 447,808 miles of equivalent road mileage. The data set covers the 48 contiguous States plus the District of Columbia, Alaska and Hawaii. The nominal scale of the data set is 1:100,000 with a maximal positional error of ±80 meters. This layer is part of the 2014 National Transportation Atlas Database.
The National Transportation Atlas Databases 2014 (NTAD2014) is a set of nationwide geographic datasets of transportation facilities, transportation networks, associated infrastructure and other political and administrative entities. These datasets include spatial information for transportation modal networks and intermodal terminals, as well as the re¬lated attribute information for these features. This data supports research, analysis, and decision-making across all transportation modes. It is most useful at the national level, but has major applications at regional, state and local scales throughout the transportation community. The data used to compile NTAD2014 was provided by our partners within the United States Department of Transportation (USDOT) and by other agencies throughout the United States Federal Government. These contributors are the actual data stewards and are ultimately responsible for the maintenance and accuracy of their data. The Freight Analysis Framework (FAF) integrates data from a variety of sources to create a comprehensive picture of freight movement among states and major metropolitan areas by all modes of transportation. The FAF region boundaries are a geographic database of state and metropolitan boundaries. The database includes boundaries for all 123 regions, including Washington D.C.

This polygon shapefile depicts nonattainment and maintenance areas for the United States and its Territories for the enforcement of the sulfur dioxide (SO2). There are 3 NAAQS for SO2: an annual arithmetic mean of 0.03 ppm (80 ug/m3); a 24 hour level of 0.14 ppm (365 ug/m3); and a 3 hour level of 0.50 ppm (1300 ug/m3). High concentrations of sulfur dioxide (SO2) affect breathing and may aggravate existing respiratory and cardiovascular disease. Sensitive populations include asthmatics, individuals with bronchitis or emphysema, children and the elderly. SO2 is also a primary contributor to acid deposition, or acid rain, which causes acidification of lakes and streams and can damage trees, crops, historic buildings and statues. In addition, sulfur compounds in the air contribute to visibility impairment in large parts of the country. This is especially noticeable in national parks. Ambient SO2 results largely from stationary sources such as coal and oil combustion, steel mills, refineries, pulp and paper mills and from nonferrous smelters. There are three NAAQS for SO2: . an annual arithmetic mean of 0.03 ppm (80 ug/m3); . a 24-hour level of 0.14 ppm (365 ug/m3); and . a 3-hour level of 0.50 ppm (1300 ug/m3). The first two standards are primary (health-related) standards, while the 3-hour NAAQS is a secondary (welfare-related) standard. The annual mean standard is not to be exceeded, while the short-term standards are not to be exceeded more than once per year. Title 40, Part 50 of the Code of the Federal Regulations lists the ambient air quality standard for sulfur dioxide. This layer is part of the 2014 National Transportation Atlas Database.
The National Transportation Atlas Databases 2014 (NTAD2014) is a set of nationwide geographic datasets of transportation facilities, transportation networks, associated infrastructure and other political and administrative entities. These datasets include spatial information for transportation modal networks and intermodal terminals, as well as the re¬lated attribute information for these features. This data supports research, analysis, and decision-making across all transportation modes. It is most useful at the national level, but has major applications at regional, state and local scales throughout the transportation community. The data used to compile NTAD2014 was provided by our partners within the United States Department of Transportation (USDOT) and by other agencies throughout the United States Federal Government. These contributors are the actual data stewards and are ultimately responsible for the maintenance and accuracy of their data. In United States environmental law, a nonattainment area is an area considered to have air quality worse than the National Ambient Air Quality Standards (NAAQS) as defined in the Clean Air Act Amendments of 1970 (P.L. 91-604, Sec. 109). Nonattainment areas must have and implement a plan to meet the standard or risk losing some forms of federal financial assistance or other consequences, such as industrial facilities being required to install pollution control equipment, enforce limits on their production and otherwise offset their emissions. An area may be a nonattainment area for one pollutant and an attainment area for others. This dataset establishes the spatial boundaries of each nonattainment and maintenance area.
This polygon shapefile depicts nonattainment and maintenance areas for the United States and its Territories for the enforcement of the sulfur dioxide (SO2). There are 3 NAAQS for SO2: an annual arithmetic mean of 0.03 ppm (80 ug/m3); a 24 hour level of 0.14 ppm (365 ug/m3); and a 3 hour level of 0.50 ppm (1300 ug/m3). High concentrations of sulfur dioxide (SO2) affect breathing and may aggravate existing respiratory and cardiovascular disease. Sensitive populations include asthmatics, individuals with bronchitis or emphysema, children and the elderly. SO2 is also a primary contributor to acid deposition, or acid rain, which causes acidification of lakes and streams and can damage trees, crops, historic buildings and statues. In addition, sulfur compounds in the air contribute to visibility impairment in large parts of the country. This is especially noticeable in national parks. Ambient SO2 results largely from stationary sources such as coal and oil combustion, steel mills, refineries, pulp and paper mills and from nonferrous smelters. There are three NAAQS for SO2: . an annual arithmetic mean of 0.03 ppm (80 ug/m3); . a 24-hour level of 0.14 ppm (365 ug/m3); and . a 3-hour level of 0.50 ppm (1300 ug/m3). The first two standards are primary (health-related) standards, while the 3-hour NAAQS is a secondary (welfare-related) standard. The annual mean standard is not to be exceeded, while the short-term standards are not to be exceeded more than once per year. Title 40, Part 50 of the Code of the Federal Regulations lists the ambient air quality standard for sulfur dioxide. This layer is part of the 2014 National Transportation Atlas Database.
The National Transportation Atlas Databases 2014 (NTAD2014) is a set of nationwide geographic datasets of transportation facilities, transportation networks, associated infrastructure and other political and administrative entities. These datasets include spatial information for transportation modal networks and intermodal terminals, as well as the re¬lated attribute information for these features. This data supports research, analysis, and decision-making across all transportation modes. It is most useful at the national level, but has major applications at regional, state and local scales throughout the transportation community. The data used to compile NTAD2014 was provided by our partners within the United States Department of Transportation (USDOT) and by other agencies throughout the United States Federal Government. These contributors are the actual data stewards and are ultimately responsible for the maintenance and accuracy of their data. In United States environmental law, a nonattainment area is an area considered to have air quality worse than the National Ambient Air Quality Standards (NAAQS) as defined in the Clean Air Act Amendments of 1970 (P.L. 91-604, Sec. 109). Nonattainment areas must have and implement a plan to meet the standard or risk losing some forms of federal financial assistance or other consequences, such as industrial facilities being required to install pollution control equipment, enforce limits on their production and otherwise offset their emissions. An area may be a nonattainment area for one pollutant and an attainment area for others. This dataset establishes the spatial boundaries of each nonattainment and maintenance area.

This point shapefile depicts cities and towns in the United States, Puerto Rico, and the United States Virgin Islands. A city or town is a place with a recorded population, usually with at least one central area that provides commercial activities. Cities are generally larger than towns; no distinction is made between cities and towns in this map layer. This layer is part of the 2014 National Transportation Atlas Database.
The National Transportation Atlas Databases 2014 (NTAD2014) is a set of nationwide geographic datasets of transportation facilities, transportation networks, associated infrastructure and other political and administrative entities. These datasets include spatial information for transportation modal networks and intermodal terminals, as well as the re¬lated attribute information for these features. This data supports research, analysis, and decision-making across all transportation modes. It is most useful at the national level, but has major applications at regional, state and local scales throughout the transportation community. The data used to compile NTAD2014 was provided by our partners within the United States Department of Transportation (USDOT) and by other agencies throughout the United States Federal Government. These contributors are the actual data stewards and are ultimately responsible for the maintenance and accuracy of their data. These data are intended for geographic display and analysis at the national level and for large regional areas. The data should be displayed and analyzed at scales appropriate for 1:1,000,000 scale data. No responsibility is assumed by the National Atlas of the United States in the use of these data.
This point shapefile depicts cities and towns in the United States, Puerto Rico, and the United States Virgin Islands. A city or town is a place with a recorded population, usually with at least one central area that provides commercial activities. Cities are generally larger than towns; no distinction is made between cities and towns in this map layer. This layer is part of the 2014 National Transportation Atlas Database.
The National Transportation Atlas Databases 2014 (NTAD2014) is a set of nationwide geographic datasets of transportation facilities, transportation networks, associated infrastructure and other political and administrative entities. These datasets include spatial information for transportation modal networks and intermodal terminals, as well as the re¬lated attribute information for these features. This data supports research, analysis, and decision-making across all transportation modes. It is most useful at the national level, but has major applications at regional, state and local scales throughout the transportation community. The data used to compile NTAD2014 was provided by our partners within the United States Department of Transportation (USDOT) and by other agencies throughout the United States Federal Government. These contributors are the actual data stewards and are ultimately responsible for the maintenance and accuracy of their data. These data are intended for geographic display and analysis at the national level and for large regional areas. The data should be displayed and analyzed at scales appropriate for 1:1,000,000 scale data. No responsibility is assumed by the National Atlas of the United States in the use of these data.

This polygon shapefile represents 2010 U.S. Census Tracts with a 39% Poverty Rate or Greater. Data Current as of: November 10, 2014.
This layer is intended for researchers, students, policy makers, and the general public for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production. This layer will provide a basemap for layers related to socio-political analysis, statistical enumeration and analysis, or to support graphical overlays and analysis with other spatial data. More advanced user applications may focus on demographics, urban and rural land use planning, socio-economic analysis and related areas (including defining boundaries, managing assets and facilities, integrating attribute databases with geographic features, spatial analysis, and presentation output.)
This polygon shapefile represents 2010 U.S. Census Tracts with a 39% Poverty Rate or Greater. Data Current as of: November 10, 2014.
This layer is intended for researchers, students, policy makers, and the general public for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production. This layer will provide a basemap for layers related to socio-political analysis, statistical enumeration and analysis, or to support graphical overlays and analysis with other spatial data. More advanced user applications may focus on demographics, urban and rural land use planning, socio-economic analysis and related areas (including defining boundaries, managing assets and facilities, integrating attribute databases with geographic features, spatial analysis, and presentation output.)

This point shapefile contains annual average daily truck traffic volume (also known as traffic counts) on California's state highway network for 2010. Annual average daily truck traffic is the total truck traffic volume divided by 365 days. Truck counting is done throughout the state in a program of continuous truck count sampling. The sampling includes a partial day, 24-hour, 7-day and continuous vehicle classification counts. The partial day and 24-hour counts are usually made on high volume, urban highways. The 7-day counts are made on low volume, rural highways. The counts are usually taken only once in the year. About one-sixth of the locations are counted annually. The resulting counts are adjusted to an estimate of annual average daily truck traffic by compensating for seasonal influence, weekly variation, and other variables that may be present. Annual average daily truck traffic is necessary for presenting a statewide picture of truck flow, evaluating truck trends, planning and designing highways and for other purposes. Truck traffic is classified by number of axles. The two-axle class includes 11/2-ton trucks with dual rear tires and excludes pickups and vans with only four tires. ). This layer is part of a collection of GIS data created by the California Department of Transportation (Caltrans).
This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.
This point shapefile contains annual average daily truck traffic volume (also known as traffic counts) on California's state highway network for 2010. Annual average daily truck traffic is the total truck traffic volume divided by 365 days. Truck counting is done throughout the state in a program of continuous truck count sampling. The sampling includes a partial day, 24-hour, 7-day and continuous vehicle classification counts. The partial day and 24-hour counts are usually made on high volume, urban highways. The 7-day counts are made on low volume, rural highways. The counts are usually taken only once in the year. About one-sixth of the locations are counted annually. The resulting counts are adjusted to an estimate of annual average daily truck traffic by compensating for seasonal influence, weekly variation, and other variables that may be present. Annual average daily truck traffic is necessary for presenting a statewide picture of truck flow, evaluating truck trends, planning and designing highways and for other purposes. Truck traffic is classified by number of axles. The two-axle class includes 11/2-ton trucks with dual rear tires and excludes pickups and vans with only four tires. ). This layer is part of a collection of GIS data created by the California Department of Transportation (Caltrans).
This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

This polygon shapefile represents adjusted urban area boundaries for California derived from the 2010 Census urban area boundaries, the preceding 2000 Caltrans urban area boundaries, and approved district and headquarters urban area adjustments. The three urban area boundaries were merged and smoothed to create the 170 adjusted urban area boundaries for California. This layer is part of a collection of GIS data created by the California Department of Transportation (Caltrans).
The 2010 adjusted urban area file represents the current Federal Highway Administration (FHWA) approved Caltrans adjusted urban area boundaries in California.
This polygon shapefile represents adjusted urban area boundaries for California derived from the 2010 Census urban area boundaries, the preceding 2000 Caltrans urban area boundaries, and approved district and headquarters urban area adjustments. The three urban area boundaries were merged and smoothed to create the 170 adjusted urban area boundaries for California. This layer is part of a collection of GIS data created by the California Department of Transportation (Caltrans).
The 2010 adjusted urban area file represents the current Federal Highway Administration (FHWA) approved Caltrans adjusted urban area boundaries in California.

This point shapefile contains the locations of highway railroad crossings in California. These data were created using the California Public Utilities Commission (PUC) inventory database of highway-rail crossings. The PUC data contains a [grade] attribute that allows for the isolation of at-grade crossings. There are 7,719 at-grade records in the most recent PUC file available. The PUC data also includes a crossing numbering system that should be maintained as part of the development of this feature to facilitate easy update and linkage to subsequent releases. There are approximately 10,000 open crossings included in the state inventory as of November 2010. The fields included in this table are copied directly from the CPUC data set without any changes. This layer is part of a collection of GIS data created by the California Department of Transportation (Caltrans).
This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.
This point shapefile contains the locations of highway railroad crossings in California. These data were created using the California Public Utilities Commission (PUC) inventory database of highway-rail crossings. The PUC data contains a [grade] attribute that allows for the isolation of at-grade crossings. There are 7,719 at-grade records in the most recent PUC file available. The PUC data also includes a crossing numbering system that should be maintained as part of the development of this feature to facilitate easy update and linkage to subsequent releases. There are approximately 10,000 open crossings included in the state inventory as of November 2010. The fields included in this table are copied directly from the CPUC data set without any changes. This layer is part of a collection of GIS data created by the California Department of Transportation (Caltrans).
This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

This line shapefile represents traffic bottlenecks, or heavy congestion during peak a.m. and p.m. travel periods, on California's State Highway system during 2010. These data were derived from the California Department of Transportation (Caltrans) HQ Traffic Operations Asset Management Branch and prepared by Caltrans as part of the Mobility Performance Report (MPR). The MPR is prepared by Caltrans and district staff providing detailed data about highway system performance related to congestion and mobility. Caltrans collects vehicle counts and calculates speeds at all hours of the days and the week of major metropolitan areas throughout California via the Caltrans Performance Measurement System. This information helps identify congestion bottlenecks and results in more cost-effective investments to improve performance of the State Highway System. This layer is part of a collection of GIS data created by the California Department of Transportation (Caltrans).
This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.
This line shapefile represents traffic bottlenecks, or heavy congestion during peak a.m. and p.m. travel periods, on California's State Highway system during 2010. These data were derived from the California Department of Transportation (Caltrans) HQ Traffic Operations Asset Management Branch and prepared by Caltrans as part of the Mobility Performance Report (MPR). The MPR is prepared by Caltrans and district staff providing detailed data about highway system performance related to congestion and mobility. Caltrans collects vehicle counts and calculates speeds at all hours of the days and the week of major metropolitan areas throughout California via the Caltrans Performance Measurement System. This information helps identify congestion bottlenecks and results in more cost-effective investments to improve performance of the State Highway System. This layer is part of a collection of GIS data created by the California Department of Transportation (Caltrans).
This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

This layer is a georeferenced raster image displaying elevation data for Alaska using an Albers projection. The elevation data were derived from National Elevation Dataset (NED) and show the terrain at a resolution of 100 meters. The NED is a raster product assembled by the U.S. Geological Survey, designed to provide national elevation data in a seamless form with a consistent datum, elevation unit, and projection. Data corrections made in the NED assembly process minimize artifacts, permit edge matching, and fill sliver areas of missing data. The NED is updated on a bi-monthly basis. This data set was derived from NED data released in December, 2010, This layer is part of the 1997-2014 edition National Atlas of the United States.
The 100-meter resolution elevation data were developed to portray the terrain of the United States at 1:1,000,000 scale. They are intended primarily for visual purposes. The original NED data should be used for conducting analysis and determining the most accurate elevation values. No responsibility is assumed by the National Atlas of the United States in the use of these data.
This layer is a georeferenced raster image displaying elevation data for Alaska using an Albers projection. The elevation data were derived from National Elevation Dataset (NED) and show the terrain at a resolution of 100 meters. The NED is a raster product assembled by the U.S. Geological Survey, designed to provide national elevation data in a seamless form with a consistent datum, elevation unit, and projection. Data corrections made in the NED assembly process minimize artifacts, permit edge matching, and fill sliver areas of missing data. The NED is updated on a bi-monthly basis. This data set was derived from NED data released in December, 2010, This layer is part of the 1997-2014 edition National Atlas of the United States.
The 100-meter resolution elevation data were developed to portray the terrain of the United States at 1:1,000,000 scale. They are intended primarily for visual purposes. The original NED data should be used for conducting analysis and determining the most accurate elevation values. No responsibility is assumed by the National Atlas of the United States in the use of these data.

This layer is a georeferenced raster image displaying elevation data for Hawaii, using an Albers Equal-Area Conic projection. These data were derived from the National Elevation Dataset (NED) released in December, 2010, The elevation data show the terrain at a resolution of 100 meters. The NED is a raster product assembled by the U.S. Geological Survey, designed to provide national elevation data in a seamless form with a consistent datum, elevation unit, and projection. Data corrections made in the NED assembly process minimize artifacts, permit edge matching, and fill sliver areas of missing data. The NED is updated on a bi-monthly basis. This layer is part of the 1997-2014 edition National Atlas of the United States.
The 100-meter resolution elevation data were developed to portray the terrain of the United States at 1:1,000,000 scale. They are intended primarily for visual purposes. The original NED data should be used for conducting analysis and determining the most accurate elevation values. No responsibility is assumed by the National Atlas of the United States in the use of these data.
This layer is a georeferenced raster image displaying elevation data for Hawaii, using an Albers Equal-Area Conic projection. These data were derived from the National Elevation Dataset (NED) released in December, 2010, The elevation data show the terrain at a resolution of 100 meters. The NED is a raster product assembled by the U.S. Geological Survey, designed to provide national elevation data in a seamless form with a consistent datum, elevation unit, and projection. Data corrections made in the NED assembly process minimize artifacts, permit edge matching, and fill sliver areas of missing data. The NED is updated on a bi-monthly basis. This layer is part of the 1997-2014 edition National Atlas of the United States.
The 100-meter resolution elevation data were developed to portray the terrain of the United States at 1:1,000,000 scale. They are intended primarily for visual purposes. The original NED data should be used for conducting analysis and determining the most accurate elevation values. No responsibility is assumed by the National Atlas of the United States in the use of these data.