3,129 results returned
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Title: United States Alternative Fueling Facilities, 2008
Contributors:- Point data
- 2008
Summary: United States Alternative Fueling Facilities is a point theme representing fueling facilities that offer fuels other than gasoline in the United States.
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Title: Photovoltaic Power (Tilted) Contour Lines, 100-Meter Intervals: United States, 1998-2005
Contributors:- Line data
- 2015
Summary: This line shapefile contains tilted photovoltaic solar power resource levels of watt hours per meter squared (Wh/m2) in the contiguous United States using 100-meter interval contour lines. Tilted photovoltaic panels are those that are angled in order to maximize exposure to direct sunlight. Tilt angles are often equal to the site's latitude but may vary throughout the year. This layer is a part of a collection of GIS data containing renewable and electric energy information for the U.S., including data on transmission lines, power plants and electricity substations. This layer can be used for estimates of solar resource potential. Hart Energy Publishing. (2015). Photovoltaic Power (Horizontal) Contour Lines, 100-Meter Intervals: United States, 1998-2005. Hart Energy Publishing. Available at: http://purl.stanford.edu/tt266jx1921 This data provides monthly average and annual average daily total solar resource averaged over surface cells of 0.1 degrees in both latitude and longitude, or about 10 km in size. This data was developed using the State University of New York/Albany satellite radiation model. This model was developed by Dr. Richard Perez and collaborators at the National Renewable Energy Laboratory and other universities for the U.S. Department of Energy. Specific information about this model can be found in Perez, et al. (2002). This model uses hourly radiance images from geostationary weather satellites, daily snow cover data, and monthly averages of atmospheric water vapor, trace gases, and the amount of aerosols in the atmosphere to calculate the hourly total insolation (sun and sky) falling on a horizontal surface. Atmospheric water vapor, trace gases, and aerosols are derived from a variety of sources. A modified Bird model is used to calculate clear sky direct normal (DNI). This is then adjusted as a function of the ratio of clear sky global horizontal (GHI) and the model predicted GHI. Where possible, existing ground measurement stations are used to validate the data. Nevertheless, there is uncertainty associated with the meterological input to the model, since some of the input parameters are not avalable at a 10km resolution. As a result, it is believed that the modeled values are accurate to approximately 15% of a true measured value within the grid cell. Due to terrain effects and other microclimate influences, the local cloud cover can vary significantly even within a single grid cell. Furthermore, the uncertainty of the modeled estimates increase with distance from reliable measurement sources and with the complexity of the terrain. This layer is presented in the WGS84 coordinate system for web display purposes. Downloadable data are provided in native coordinate system or projection.
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Title: Alternative Fuel Filling Stations: United States, Fall 2014
Contributors:- Point data
- 2015
Summary: This point dataset shows the locations of alternative fuel filling stations in the United States. This layer is part of a collection of GIS data for renewable and electric energy in the U.S. This layer is from the 2014 Fall Quarter update (Update 1017). This shapefile can be used to locate alternative fuel filling stations in the United States. point dataset shows the locations of oil seed processing plants in the United States for 2013. This layer is a part of a collection of GIS data containing renewable and electric energy information for the U.S., including data on transmission lines, power plants and electricity substations. Hart Energy Publishing (2015). Alternative Fuel Filling Stations: United States, Fall 2014. Hart Energy Publishing. Available at: http://purl.stanford.edu/yh448wy2698. This layer is presented in the WGS84 coordinate system for web display purposes. Downloadable data are provided in native coordinate system or projection.
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Title: Photovoltaic Power (Tilted) Contour Lines, 50-Meter Intervals: United States, 1998-2005
Contributors:- Line data
- 2015
Summary: This line shapefile contains tilted photovoltaic solar power resource levels of watt hours per meter squared (Wh/m2) in the contiguous United States using 50-meter interval contour lines. Tilted photovoltaic panels are those that are angled in order to maximize exposure to direct sunlight. Tilt angles are often equal to the site's latitude but may vary throughout the year. This layer is a part of a collection of GIS data containing renewable and electric energy information for the U.S., including data on transmission lines, power plants and electricity substations. This layer can be used for estimates of solar resource potential. Hart Energy Publishing. (2015). Photovoltaic Power (Horizontal) Contour Lines, 50-Meter Intervals: United States, 1998-2005. Hart Energy Publishing. Available at: http://purl.stanford.edu/gz526ym8943 This data provides monthly average and annual average daily total solar resource averaged over surface cells of 0.1 degrees in both latitude and longitude, or about 10 km in size. This data was developed using the State University of New York/Albany satellite radiation model. This model was developed by Dr. Richard Perez and collaborators at the National Renewable Energy Laboratory and other universities for the U.S. Department of Energy. Specific information about this model can be found in Perez, et al. (2002). This model uses hourly radiance images from geostationary weather satellites, daily snow cover data, and monthly averages of atmospheric water vapor, trace gases, and the amount of aerosols in the atmosphere to calculate the hourly total insolation (sun and sky) falling on a horizontal surface. Atmospheric water vapor, trace gases, and aerosols are derived from a variety of sources. A modified Bird model is used to calculate clear sky direct normal (DNI). This is then adjusted as a function of the ratio of clear sky global horizontal (GHI) and the model predicted GHI. Where possible, existing ground measurement stations are used to validate the data. Nevertheless, there is uncertainty associated with the meterological input to the model, since some of the input parameters are not avalable at a 10km resolution. As a result, it is believed that the modeled values are accurate to approximately 15% of a true measured value within the grid cell. Due to terrain effects and other microclimate influences, the local cloud cover can vary significantly even within a single grid cell. Furthermore, the uncertainty of the modeled estimates increase with distance from reliable measurement sources and with the complexity of the terrain. This layer is presented in the WGS84 coordinate system for web display purposes. Downloadable data are provided in native coordinate system or projection.
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Title: Photovoltaic Power (Tilted) Contour Lines, 500-Meter Intervals: United States, 1998-2005
Contributors:- Line data
- 2015
Summary: This line shapefile contains tilted photovoltaic solar power resource levels of watt hours per meter squared (Wh/m2) in the contiguous United States using 500-meter interval contour lines. Tilted photovoltaic panels are those that are angled in order to maximize exposure to direct sunlight. Tilt angles are often equal to the site's latitude but may vary throughout the year. This layer is a part of a collection of GIS data containing renewable and electric energy information for the U.S., including data on transmission lines, power plants and electricity substations. This layer can be used for estimates of solar resource potential. Hart Energy Publishing. (2015). Photovoltaic Power (Tilted) Contour Lines, 500-Meter Intervals: United States, 1998-2005. Hart Energy Publishing. Available at: http://purl.stanford.edu/vx760hv6666 This data provides monthly average and annual average daily total solar resource averaged over surface cells of 0.1 degrees in both latitude and longitude, or about 10 km in size. This data was developed using the State University of New York/Albany satellite radiation model. This model was developed by Dr. Richard Perez and collaborators at the National Renewable Energy Laboratory and other universities for the U.S. Department of Energy. Specific information about this model can be found in Perez, et al. (2002). This model uses hourly radiance images from geostationary weather satellites, daily snow cover data, and monthly averages of atmospheric water vapor, trace gases, and the amount of aerosols in the atmosphere to calculate the hourly total insolation (sun and sky) falling on a horizontal surface. Atmospheric water vapor, trace gases, and aerosols are derived from a variety of sources. A modified Bird model is used to calculate clear sky direct normal (DNI). This is then adjusted as a function of the ratio of clear sky global horizontal (GHI) and the model predicted GHI. Where possible, existing ground measurement stations are used to validate the data. Nevertheless, there is uncertainty associated with the meterological input to the model, since some of the input parameters are not avalable at a 10km resolution. As a result, it is believed that the modeled values are accurate to approximately 15% of a true measured value within the grid cell. Due to terrain effects and other microclimate influences, the local cloud cover can vary significantly even within a single grid cell. Furthermore, the uncertainty of the modeled estimates increase with distance from reliable measurement sources and with the complexity of the terrain. This layer is presented in the WGS84 coordinate system for web display purposes. Downloadable data are provided in native coordinate system or projection.
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Title: Photovoltaic Power (Horizontal) Contour Lines, 100-Meter Intervals: United States, 1998-2005
Contributors:- Line data
- 2015
Summary: This line shapefile contains horizontal photovoltaic solar power resource levels of watt hours per meter squared (Wh/m2) in the contiguous United States using 100-meter interval contour lines. Horizontal photovoltaic panels are those that are positioned on a flat surface. This layer is a part of a collection of GIS data containing renewable and electric energy information for the U.S., including data on transmission lines, power plants and electricity substations. This layer can be used for estimates of solar resource potential. Hart Energy Publishing. (2015). Photovoltaic Power (Horizontal) Contour Lines, 100-Meter Intervals: United States, 1998-2005. Hart Energy Publishing. Available at: http://purl.stanford.edu/gj625pt1990 This data provides monthly average and annual average daily total solar resource averaged over surface cells of 0.1 degrees in both latitude and longitude, or about 10 km in size. This data was developed using the State University of New York/Albany satellite radiation model. This model was developed by Dr. Richard Perez and collaborators at the National Renewable Energy Laboratory and other universities for the U.S. Department of Energy. Specific information about this model can be found in Perez, et al. (2002). This model uses hourly radiance images from geostationary weather satellites, daily snow cover data, and monthly averages of atmospheric water vapor, trace gases, and the amount of aerosols in the atmosphere to calculate the hourly total insolation (sun and sky) falling on a horizontal surface. Atmospheric water vapor, trace gases, and aerosols are derived from a variety of sources. A modified Bird model is used to calculate clear sky direct normal (DNI). This is then adjusted as a function of the ratio of clear sky global horizontal (GHI) and the model predicted GHI. Where possible, existing ground measurement stations are used to validate the data. Nevertheless, there is uncertainty associated with the meterological input to the model, since some of the input parameters are not avalable at a 10km resolution. As a result, it is believed that the modeled values are accurate to approximately 15% of a true measured value within the grid cell. Due to terrain effects and other microclimate influences, the local cloud cover can vary significantly even within a single grid cell. Furthermore, the uncertainty of the modeled estimates increase with distance from reliable measurement sources and with the complexity of the terrain. This layer is presented in the WGS84 coordinate system for web display purposes. Downloadable data are provided in native coordinate system or projection.
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Title: Alternative Fuel Filling Stations: United States, Summer 2014
Contributors:- Point data
- 2014
Summary: This point dataset shows the locations of alternative fuel filling stations in the United States. This layer is part of a collection of GIS data for renewable and electric energy in the U.S. This layer is from the 2014 Summer Quarter update (Update 1016). This shapefile can be used to locate alternative fuel filling stations in the United States. point dataset shows the locations of oil seed processing plants in the United States for 2013. This layer is a part of a collection of GIS data containing renewable and electric energy information for the U.S., including data on transmission lines, power plants and electricity substations. Hart Energy Publishing (2016). Alternative Fuel Filling Stations: United States, Summer 2014. Hart Energy Publishing. Available at: http://purl.stanford.edu/js268xr6074. This layer is presented in the WGS84 coordinate system for web display purposes. Downloadable data are provided in native coordinate system or projection.
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Title: Alternative Fuel Filling Stations: United States, Spring 2013
Contributors:- Point data
- 2013
Summary: This point dataset shows the locations of alternative fuel filling stations in the United States. This layer is part of a collection of GIS data for renewable and electric energy in the U.S. This layer is from the 2013 Spring Quarter update (Update 1011). This shapefile can be used to locate alternative fuel filling stations in the United States. point dataset shows the locations of oil seed processing plants in the United States for 2013. This layer is a part of a collection of GIS data containing renewable and electric energy information for the U.S., including data on transmission lines, power plants and electricity substations. Hart Energy Publishing (2013). Alternative Fuel Filling Stations: United States, Spring 2013. Hart Energy Publishing. Available at: http://purl.stanford.edu/km958vb4793. This layer is presented in the WGS84 coordinate system for web display purposes. Downloadable data are provided in native coordinate system or projection.
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Title: United States Alternative Fuels Stations, 2013
Contributors:- Point data
- 2013
Summary: United States Alternative Fuels Stations, 2013 is a point theme representing alternative fuels stations. Through a nationwide network of local coalitions, Clean Citiesprovides project assistance to help stakeholders in the public and private sectors deploy alternative and renewable fuels, idle-reduction measures, fuel economy improvements, and emerging transportation technologies.Department of Energy collects this data as part of the Projects undertaken by Clean Cities coalitions and stakeholders to ensure customers access to clean alternative energy. This data can be found at the Department of Energy Alternative Fuels Data Center Web Feature Service: http://www.afdc.energy.gov/locator/stations/Clean Cities is the deployment arm of the U.S. Department of Energy's (DOE) Vehicle Technologies Office.
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Title: Offfshore Wind Speed 90-Meters, Atlantic Coast, United States, 2011
Contributors:- Raster data
- 2011
Summary: This raster data layer contains the annual average offshore wind speed for the Atlantic Coast (Connecticut, Delaware, Georgia, Massachusetts, Maine, Maryland, New Hampshire, New Jersey, New York, North Carolina, Rhode Island, South Carolina, and Virginia) at a 90 meter height.These data provide information on the wind resource development potential for the Atlantic Coast.
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Title: USA (Alternative Fuels, 2008)
Contributors:- Point data
- 2008
Summary: The Alternative Fuels database is a geographic point database of fueling facilities that offer fuels other than gasoline in the United States.
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Title: USA (Alternative Fuel Stations, 2006)
Contributors:- Point data
- 2006
Summary: Biodiesel, CNG, electric, 85% ethanol, hydrogen, LNG, LPG/Propane stations in the United States.
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Title: BOEM Wind Planning Areas, Atlantic Region, 2013
Contributors:- Polygon data
- 2013
Summary: This polygon shapefile represents the most recent changes for the Bureau of Ocean Energy Management (BOEM) Wind Development Planning Areas in the Atlantic. Wind Planning Areas in this layer represent up to six different types of announcements within the US Federal Register that can be used to show the current status of an area that is being considered for Wind Power Development. The Smart from the Start Wind Energy Areas (WEAs) are represented in a separate layer. Not all areas will start out as WEAs. Since the WEAs were the first announcement for some of these areas, we have decided to repost the WEA layer to show its original areas prior to any reshaping by BOEM after the original announcement since the reshaped areas are really not the WEAs but one of the other six types of areas represented here. For those areas that started out as WEAs, the user may see the changes that have taken place over time. For the areas represented here, once an area has changed from one type of area to another, it will be replaced entirely by the new area. The types of areas and their descriptions can be found in the attributes section of the metadata record. To let those that are interested in Wind Energy Planning and Development know of the most recent updates to areas of interest for Wind Energy Development off the United States Coast within Federal Waters. U.S. Bureau of Ocean Energy Management. Office of Renewable Energy Programs. (2013). BOEM Wind Planning Areas, Atlantic Region, 2013. BOEM. Available at: http://purl.stanford.edu/yj142kb3734. Not for navigational use. Not to be used for leasing purposes. For visual purposes only. This layer is presented in the WGS84 coordinate system for web display purposes. Downloadable data are provided in native coordinate system or projection.
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Title: LandScan 2019 global population database
Contributors:- Not specified
- 2020
Summary: At approximately 1 km resolution (30" X 30"), LandScan is the finest resolution global population distribution data available and represents an ambient population (average over 24 hours). The LandScan algorithm uses spatial data and imagery analysis technologies and a multi-variable dasymetric modeling approach to disaggregate census counts within an administrative boundary. Since no single population distribution model can account for the differences in spatial data availability, quality, scale, and accuracy as well as the differences in cultural settlement practices, LandScan population distribution models are tailored to match the data conditions and geographical nature of each individual country and region. Title from disk surface Developed by Oak Ridge National Laboratory.
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Title: LandScan Global Population Database 2019
Contributors:- Raster data
- 2020
Summary: This raster dataset contains population counts at 30 arc second resolution (1 km. or finer) for 2019. This release represents the 2019 edition of LandScan and succeeds all previous versions. Using an innovative approach with Geographic Information System and Remote Sensing, ORNL's LandScan is the community standard for global population distribution. At approximately 1 km resolution (30" X 30"), LandScan is the finest resolution global population distribution data available and represents an ambient population (average over 24 hours). The LandScan algorithm, an R&D 100 Award Winner, uses spatial data and imagery analysis technologies and a multi-variable dasymetric modeling approach to disaggregate census counts within an administrative boundary. Since no single population distribution model can account for the differences in spatial data availability, quality, scale, and accuracy as well as the differences in cultural settlement practices. This dataset is part of the LandScan 2019 global population database. Developed for the U. S. Department of Defense. Allows for quick and easy assessment, estimation, and visualization of populations-at-risk. Oak Ridge National Laboratory. (2018) LandScan Global Population Database 2018. Oak Ridge National Laboratory, UT-Battelle, LLC. Available at: http://purl.stanford.edu/qf389cd0263 IMPORTANT: For correct population analysis using ESRI products assure that the following parameters are set:- Use ONLY Geographic, WGS84 projection parameters.- Spatial Analysis cell size is 0.008333333333333 (double precision)- Spatial Analysis extent should be set to an exact multiple of the cell size (for example 35.25, 35.50, 35.0)Converting (including on-the-fly projections) a grid to other projections or coordinate systems causes population cells to be re-sampled, and hence population counts will be incorrect.In ESRI ArcMap, load the LandScan grid first in order to maintain the original geographic (lat-lon) projection."The dataset has a spatial resolution of 30 arc-seconds and is output in a geographical coordinate system - World Geodetic System (WGS) 84 datum. The 30 arc-second cell, or 0.008333333 decimal degrees, represents approximately 1 km2 near the equator. Since the data is in a spherical coordinate system, cell width decreases in a relationship that varies with the cosine of the latitude of the cell. Thus a cell at 60 degrees latitude would have a width that is half that of a cell at the equator (cos60 = 0.5). The height of the cells does not vary. The values of the cells are integer population counts, not population density, since the cells vary in size. Population counts are normalized to sum to each sub-national administrative unit estimate. For this reason, projecting the data in a raster format to a different coordinate system (including on-the-fly projections) will result in a re-sampling of the data and the integrity of normalized population counts will be compromised. Also prior to all spatial analysis, users should ensure that extents are set to an exact multiple of the cell size (for example 35.25, 35.50, 35.0) to avoid 'shifting' of the dataset." --from the Oak Ridge National Laboratory LandScan Web site, Sept. 12, 2018. This layer is presented in the WGS84 coordinate system for web display purposes. Downloadable data are provided in native coordinate system or projection.
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Title: LandScan Global Population Database 2018
Contributors:- Raster data
- 2017
Summary: This raster dataset contains population counts at 30 arc second resolution (1 km. or finer) for 2018. This release represents the 2018 edition of LandScan and succeeds all previous versions. Using an innovative approach with Geographic Information System and Remote Sensing, ORNL's LandScan is the community standard for global population distribution. At approximately 1 km resolution (30" X 30"), LandScan is the finest resolution global population distribution data available and represents an ambient population (average over 24 hours). The LandScan algorithm, an R&D 100 Award Winner, uses spatial data and imagery analysis technologies and a multi-variable dasymetric modeling approach to disaggregate census counts within an administrative boundary. Since no single population distribution model can account for the differences in spatial data availability, quality, scale, and accuracy as well as the differences in cultural settlement practices. This dataset is part of the LandScan 2018 global population database. Developed for the U. S. Department of Defense. Allows for quick and easy assessment, estimation, and visualization of populations-at-risk. Oak Ridge National Laboratory. (2018) LandScan Global Population Database 2018. Oak Ridge National Laboratory, UT-Battelle, LLC. Available at: http://purl.stanford.edu/cb552gf9863. IMPORTANT: For correct population analysis using ESRI products assure that the following parameters are set:- Use ONLY Geographic, WGS84 projection parameters.- Spatial Analysis cell size is 0.008333333333333 (double precision)- Spatial Analysis extent should be set to an exact multiple of the cell size (for example 35.25, 35.50, 35.0)Converting (including on-the-fly projections) a grid to other projections or coordinate systems causes population cells to be re-sampled, and hence population counts will be incorrect.In ESRI ArcMap, load the LandScan grid first in order to maintain the original geographic (lat-lon) projection."The dataset has a spatial resolution of 30 arc-seconds and is output in a geographical coordinate system - World Geodetic System (WGS) 84 datum. The 30 arc-second cell, or 0.008333333 decimal degrees, represents approximately 1 km2 near the equator. Since the data is in a spherical coordinate system, cell width decreases in a relationship that varies with the cosine of the latitude of the cell. Thus a cell at 60 degrees latitude would have a width that is half that of a cell at the equator (cos60 = 0.5). The height of the cells does not vary. The values of the cells are integer population counts, not population density, since the cells vary in size. Population counts are normalized to sum to each sub-national administrative unit estimate. For this reason, projecting the data in a raster format to a different coordinate system (including on-the-fly projections) will result in a re-sampling of the data and the integrity of normalized population counts will be compromised. Also prior to all spatial analysis, users should ensure that extents are set to an exact multiple of the cell size (for example 35.25, 35.50, 35.0) to avoid 'shifting' of the dataset." --from the Oak Ridge National Laboratory LandScan Web site, Sept. 12, 2018. This layer is presented in the WGS84 coordinate system for web display purposes. Downloadable data are provided in native coordinate system or projection.
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Title: LandScan Global Population Database 2017
Contributors:- Raster data
- 2017
Summary: This raster dataset contains population counts at 30 arc second resolution (1 km. or finer) for 2017. This release represents the 2017 edition of LandScan and succeeds all previous versions. Using an innovative approach with Geographic Information System and Remote Sensing, ORNL's LandScan is the community standard for global population distribution. At approximately 1 km resolution (30" X 30"), LandScan is the finest resolution global population distribution data available and represents an ambient population (average over 24 hours). The LandScan algorithm, an R&D 100 Award Winner, uses spatial data and imagery analysis technologies and a multi-variable dasymetric modeling approach to disaggregate census counts within an administrative boundary. Since no single population distribution model can account for the differences in spatial data availability, quality, scale, and accuracy as well as the differences in cultural settlement practices. This dataset is part of the LandScan 2017 global population database. Developed for the U. S. Department of Defense. Allows for quick and easy assessment, estimation, and visualization of populations-at-risk. Oak Ridge National Laboratory. (2017) LandScan Global Population Database 2017. Oak Ridge National Laboratory, UT-Battelle, LLC. Available at: http://purl.stanford.edurg696cc8418 IMPORTANT: For correct population analysis using ESRI products assure that the following parameters are set:- Use ONLY Geographic, WGS84 projection parameters.- Spatial Analysis cell size is 0.008333333333333 (double precision)- Spatial Analysis extent should be set to an exact multiple of the cell size (for example 35.25, 35.50, 35.0)Converting (including on-the-fly projections) a grid to other projections or coordinate systems causes population cells to be re-sampled, and hence population counts will be incorrect.In ESRI ArcMap, load the LandScan grid first in order to maintain the original geographic (lat-lon) projection."The dataset has a spatial resolution of 30 arc-seconds and is output in a geographical coordinate system - World Geodetic System (WGS) 84 datum. The 30 arc-second cell, or 0.008333333 decimal degrees, represents approximately 1 km2 near the equator. Since the data is in a spherical coordinate system, cell width decreases in a relationship that varies with the cosine of the latitude of the cell. Thus a cell at 60 degrees latitude would have a width that is half that of a cell at the equator (cos60 = 0.5). The height of the cells does not vary. The values of the cells are integer population counts, not population density, since the cells vary in size. Population counts are normalized to sum to each sub-national administrative unit estimate. For this reason, projecting the data in a raster format to a different coordinate system (including on-the-fly projections) will result in a re-sampling of the data and the integrity of normalized population counts will be compromised. Also prior to all spatial analysis, users should ensure that extents are set to an exact multiple of the cell size (for example 35.25, 35.50, 35.0) to avoid 'shifting' of the dataset." --from the Oak Ridge National Laboratory LandScan Web site, Sept. 12, 2017. This layer is presented in the WGS84 coordinate system for web display purposes. Downloadable data are provided in native coordinate system or projection.
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Title: LandScan 2018 global population database
Contributors:- Not specified
- 2017
Summary: At approximately 1 km resolution (30" X 30"), LandScan is the finest resolution global population distribution data available and represents an ambient population (average over 24 hours). The LandScan algorithm uses spatial data and imagery analysis technologies and a multi-variable dasymetric modeling approach to disaggregate census counts within an administrative boundary. Since no single population distribution model can account for the differences in spatial data availability, quality, scale, and accuracy as well as the differences in cultural settlement practices, LandScan population distribution models are tailored to match the data conditions and geographical nature of each individual country and region. Title from disk surface Developed by Oak Ridge National Laboratory.
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Title: LandScan Global Population Database 2016
Contributors:- Raster data
- 2017
Summary: This raster dataset contains population counts at 30 arc second resolution (1 km. or finer) for 2016. This release represents the 2016 edition of LandScan and succeeds all previous versions. Using an innovative approach with Geographic Information System and Remote Sensing, ORNL's LandScan is the community standard for global population distribution. At approximately 1 km resolution (30" X 30"), LandScan is the finest resolution global population distribution data available and represents an ambient population (average over 24 hours). The LandScan algorithm, an R&D 100 Award Winner, uses spatial data and imagery analysis technologies and a multi-variable dasymetric modeling approach to disaggregate census counts within an administrative boundary. Since no single population distribution model can account for the differences in spatial data availability, quality, scale, and accuracy as well as the differences in cultural settlement practices. This dataset is part of the LandScan 2016 global population database. Developed for the U. S. Department of Defense. Allows for quick and easy assessment, estimation, and visualization of populations-at-risk. Oak Ridge National Laboratory. (2016). LandScan Global Population Database 2016. Oak Ridge National Laboratory, UT-Battelle, LLC. Available at: http://purl.stanford.edu/mx198zx3638. IMPORTANT: For correct population analysis using ESRI products assure that the following parameters are set:- Use ONLY Geographic, WGS84 projection parameters.- Spatial Analysis cell size is 0.008333333333333 (double precision)- Spatial Analysis extent should be set to an exact multiple of the cell size (for example 35.25, 35.50, 35.0)Converting (including on-the-fly projections) a grid to other projections or coordinate systems causes population cells to be re-sampled, and hence population counts will be incorrect.In ESRI ArcMap, load the LandScan grid first in order to maintain the original geographic (lat-lon) projection."The dataset has a spatial resolution of 30 arc-seconds and is output in a geographical coordinate system - World Geodetic System (WGS) 84 datum. The 30 arc-second cell, or 0.008333333 decimal degrees, represents approximately 1 km2 near the equator. Since the data is in a spherical coordinate system, cell width decreases in a relationship that varies with the cosine of the latitude of the cell. Thus a cell at 60 degrees latitude would have a width that is half that of a cell at the equator (cos60 = 0.5). The height of the cells does not vary. The values of the cells are integer population counts, not population density, since the cells vary in size. Population counts are normalized to sum to each sub-national administrative unit estimate. For this reason, projecting the data in a raster format to a different coordinate system (including on-the-fly projections) will result in a re-sampling of the data and the integrity of normalized population counts will be compromised. Also prior to all spatial analysis, users should ensure that extents are set to an exact multiple of the cell size (for example 35.25, 35.50, 35.0) to avoid 'shifting' of the dataset." --from the Oak Ridge National Laboratory LandScan Web site, Sept. 12, 2016. This layer is presented in the WGS84 coordinate system for web display purposes. Downloadable data are provided in native coordinate system or projection.
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Title: LandScan 2016 global population database
Contributors:- Not specified
- 2017
Summary: At approximately 1 km resolution (30" X 30"), LandScan is the finest resolution global population distribution data available and represents an ambient population (average over 24 hours). The LandScan algorithm uses spatial data and imagery analysis technologies and a multi-variable dasymetric modeling approach to disaggregate census counts within an administrative boundary. Since no single population distribution model can account for the differences in spatial data availability, quality, scale, and accuracy as well as the differences in cultural settlement practices, LandScan population distribution models are tailored to match the data conditions and geographical nature of each individual country and region. Title from disk surface Developed by Oak Ridge National Laboratory.