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3,461 results returned

  1. Title: Freight Analysis Framework Network: United States, 2010

    Contributors:

    Summary: 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. United States. Department of Transportation. Research and Innovative Technology Administration. (2014). Freight Analysis Framework Network: United States, 2010. National Transportation Atlas Database 2014. Available at: http://purl.stanford.edu/np510vw8315.

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  2. Title: Future Erosion, San Mateo County Sea Level Rise Vulnerability Assessment Project, 2008

    Contributors:

    Summary: This polygon shapefile represents the projected extent of coastal erosion expected with 4.6 feet of sea level rise. The points depicted represent the highest estimated 100 yr tide elevation for locations surrounding the San Francisco Bay. Source: Pacific Institute. Source Date: 2008. This layer is part of the San Mateo County Sea Level Rise Vulnerability Assessment Project. These data are 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. County of San Mateo Information Services Department and Pacific Institute. (2019). Future Erosion, San Mateo County Sea Level Rise Vulnerability Assessment Project, 2008. County of San Mateo Information Services Department. Available at: http://purl.stanford.edu/qq750jp2957. 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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  3. Title: Xin shanghai ditu (Raster Image)

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    Summary: This layer is a georeferenced image of a map of Shanghai (originally titled '新上海地圖 /Xin shanghai ditu') that was published in 1931. It shows historical city data such as roads and parks. This map was originally created byt he Jihyun geographical institute. Virtual Shanghai is a research and resource platform on the history of Shanghai from the mid-nineteenth century to nowadays. It incorporates various sets of documents: essays, original documents, photographs, maps, quantitative data, etc. The objective of the project is to write a history of the city through the combined mobilization of these various types of documents. The implementation of this approach relies on the use of digital and GIS technologies. On the research side, the platform offers various ways to step into the history of the city and follow its course at different levels over time. On the resource side, apart from providing original textual and visual documents, it develops a powerful cartographic tool for spatial analysis and real-time mapping. The authors of the present project subscribe to the idea of sharing scholarship and research tools for the benefit of scholars, students, and citizens at large. Jihyun Geographical Institute and Virtual Shanghai Project. (2018). Xin shanghai ditu (Raster Image). Virtual Shanghai Project. Available at: http://purl.stanford.edu/xx177jt7908 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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  4. Title: Edinburgh, Scotland, United Kingdom, 1921 (Raster Image)

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    Summary: This layer is a georeferenced raster image of the historic paper map entitled: Bartholomew's pocket plan of Edinburgh. It was published by J. Bartholomew & Son, Ltd. in 1921. Scale [ca. 1:18,500]. Covers Edinburgh, Scotland, United Kingdom.The image inside the map neatline is georeferenced to the surface of the earth and fit to the 'British National Grid' coordinate system. All map collar and inset information is also available as part of the raster image, including any inset maps, profiles, statistical tables, directories, text, illustrations, index maps, legends, or other information associated with the principal map.This map shows features such as roads, railroads, drainage, built-up areas and selected buildings, parks, and more. Relief shown by hachures. Includes also indexes and inset: Map showing full extent of greater Edinburgh.This layer is part of a selection of digitally scanned and georeferenced historic maps from The Harvard Map Collection as part of the Imaging the Urban Environment project. Maps selected for this project represent major urban areas and cities of the world, at various time periods. These maps typically portray both natural and manmade features at a large scale. The selection represents a range of regions, originators, ground condition dates, scales, and purposes.

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  5. Title: Liverpool, England, 1900 (Raster Image)

    Contributors:

    Summary: This layer is a georeferenced raster image of the historic paper map entitled: City of Liverpool : area 14,909 acres (exclusive of half of River Mersey). It was published by George Philip & Son L[td], The London Geographical Institute in 1900. Scale [ca. 1:15,400]. Covers portions of Liverpool and Birkenhead, England. The image inside the map neatline is georeferenced to the surface of the earth and fit to the 'British National Grid' coordinate system. All map collar and inset information is also available as part of the raster image, including any inset maps, profiles, statistical tables, directories, text, illustrations, index maps, legends, or other information associated with the principal map. This map shows features such as roads, railroads and stations, drainage, built-up areas and selected buildings and industries, canals, docks, wharves, parks, administrative boundaries, and more. Includes index to public parks, gardens, and recreation grounds, and inset: Extension of the Garston on same scale. This layer is part of a selection of digitally scanned and georeferenced historic maps from The Harvard Map Collection as part of the Imaging the Urban Environment project. Maps selected for this project represent major urban areas and cities of the world, at various time periods. These maps typically portray both natural and manmade features at a large scale. The selection represents a range of regions, originators, ground condition dates, scales, and purposes.

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  6. Title: Annual Projected Water Balance by Subdrainage Area in Kenya, 2000 and 2010

    Contributors:

    Summary: This polygon shapefile portrays annual projected water balance by subdrainage area in Kenya for 2000 and 2010. The data was taken from the Kenya National Water Master Plan (1992) and joined by sub-basin. Land areas with negative water balances (where water supply is outstripped by demand) will require investment in water resource infrastructure to cover their needs. This data was used in Map 3.7 in Nature's Benefits in Kenya: An Atlas of Ecosystems and Human Well-Being. World Resources Institute. (2007). Annual Projected Water Balance by Subdrainage Area in Kenya, 2000 and 2010. World Resources Institute. Data set is not for use in litigation. While efforts have been made to ensure that these data are accurate and reliable within the state of the art, WRI, cannot assume liability for any damages, or misrepresentations, caused by any inaccuracies in the data, or as a result of the data to be used on a particular system. WRI makes no warranty, expressed or implied, nor does the fact of distribution constitute such a warranty.

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  7. Title: Syria Landuse, 2008

    Contributors:

    Summary: Syria Landuse is a raster theme representing landuse classifications throughout Syria at a resolution of 30 arc seconds. This layer is a component of the Global Map a 1:1,000,000 scale framework dataset of the world. It consists of vector and raster layers of transport, administrative boundaries, drainage, elevation, vegetation, land use and land cover data. The data were prepared from information provided by national mapping and other organizations worldwide.

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  8. Title: Syria Landcover, 2008

    Contributors:

    Summary: Syria Landcover is a raster theme representing land cover classifications throughout Syria at a resolution of 30 arc seconds. This layer is a component of the Global Map a 1:1,000,000 scale framework dataset of the world. It consists of vector and raster layers of transport, administrative boundaries, drainage, elevation, vegetation, land use and land cover data. The data were prepared from information provided by national mapping and other organizations worldwide.

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  9. Title: Bhutan Landcover, 2008

    Contributors:

    Summary: Bhutan Landcover is a raster theme representing land cover classifications throughout Bhutan at a resolution of 30 arc seconds. This layer is a component of the Global Map a 1:1,000,000 scale framework dataset of the world. It consists of vector and raster layers of transport, administrative boundaries, drainage, elevation, vegetation, land use and land cover data. The data were prepared from information provided by national mapping and other organizations worldwide.

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  10. Title: Oman Landuse, 2008

    Contributors:

    Summary: Oman Landuse is a raster theme representing landuse classifications throughout Oman at a resolution of 30 arc seconds. This layer is a component of the Global Map a 1:1,000,000 scale framework dataset of the world. It consists of vector and raster layers of transport, administrative boundaries, drainage, elevation, vegetation, land use and land cover data. The data were prepared from information provided by national mapping and other organizations worldwide.

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  11. Title: Georgia Landcover, 2008

    Contributors:

    Summary: Georgia Landcover is a raster theme representing land cover classifications throughout Georgia at a resolution of 30 arc seconds. This layer is a component of the Global Map a 1:1,000,000 scale framework dataset of the world. It consists of vector and raster layers of transport, administrative boundaries, drainage, elevation, vegetation, land use and land cover data. The data were prepared from information provided by national mapping and other organizations worldwide.

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  12. Title: Syria Vegetation, 2008

    Contributors:

    Summary: Syria Vegetation is a raster theme representing percentages of tree cover throughout Syria at a resolution of 30 arc seconds. This layer is a component of the Global Map a 1:1,000,000 scale framework dataset of the world. It consists of vector and raster layers of transport, administrative boundaries, drainage, elevation, vegetation, land use and land cover data. The data were prepared from information provided by national mapping and other organizations worldwide.

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  13. Title: Tourist Accommodations in Kenya, 1993

    Contributors:

    Summary: This point shapefile cotains locations of tourist accomodations (including campsites, tented camps, hotels and lodges) in Kenya. This data was used in Map 6.6 in Nature's Benefits in Kenya: An Atlas of Ecosystems and Human Well-Being. World Resources Institute. (2007). Tourist Accommodations in Kenya, 1993. World Resources Institute. Available at: http://purl.stanford.edu/mv083dr3691 Data set is not for use in litigation. While efforts have been made to ensure that these data are accurate and reliable within the state of the art, WRI, cannot assume liability for any damages, or misrepresentations, caused by any inaccuracies in the data, or as a result of the data to be used on a particular system. WRI makes no warranty, expressed or implied, nor does the fact of distribution constitute such a warranty. 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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  14. Title: Average Water Consumption by Livestock and Wildlife by Subdrainage Area, Kenya, 1994-1996

    Contributors:

    Summary: This polygon shapefile represents the average water consumption of livestock and wildlife by sub-basin in Kenya for 1994-96. The greatest water demand from livestock occurs in the surveyed subdrainages of the Lake Victoria drainage area near Tanzania. Wildlife demand for water is also high in this area, mostly because of the number of animals within and close to a large protected area (Masai Mara). The subdrainages north of Mount Kenya (Ewaso Ngiro North drainage) also have significant water demand because of the high number of wildlife species. This data was used in Map 3.13 in Nature's Benefits in Kenya: An Atlas of Ecosystems and Human Well-Being. World Resources Institute. (2007). Average Water Consumption by Livestock and Wildlife by Subdrainage Area, Kenya, 1994-1996. World Resources Institute. Available at: http://purl.stanford.edu/jw872tw3200 Data set is not for use in litigation. While efforts have been made to ensure that these data are accurate and reliable within the state of the art, WRI, cannot assume liability for any damages, or misrepresentations, caused by any inaccuracies in the data, or as a result of the data to be used on a particular system. WRI makes no warranty, expressed or implied, nor does the fact of distribution constitute such a warranty. 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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  15. Title: Proposed Micro-Hydropower Sites: Kenya, 2005

    Contributors:

    Summary: This point shapefile contains locations of proposed small micro-hydropower sites in Kenya. This data was used in Map 3.11 in Nature's Benefits in Kenya: An Atlas of Ecosystems and Human Well-Being. World Resources Institute. (2007). Proposed Micro-Hydropower Sites: Kenya, 2005. World Resources Institute. Available at: http://purl.stanford.edu/wt416gc6757 Data set is not for use in litigation. While efforts have been made to ensure that these data are accurate and reliable within the state of the art, WRI, cannot assume liability for any damages, or misrepresentations, caused by any inaccuracies in the data, or as a result of the data to be used on a particular system. WRI makes no warranty, expressed or implied, nor does the fact of distribution constitute such a warranty.

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  16. Title: Dams: Mombasa, Kenya, 1992

    Contributors:

    Summary: This point shapefile shows the locations of dams serving Mombasa, Kenya. Data were compiled by WRI from 1:50,000 topographic maps and other sources. This data was used in Map 3.10 in Nature's Benefits in Kenya: An Atlas of Ecosystems and Human Well-Being. World Resources Institute. (2007). Dams: Mombasa, Kenya, 1992. World Resources Institute. Available at: http://purl.stanford.edu/vx841dt1251 Data set is not for use in litigation. While efforts have been made to ensure that these data are accurate and reliable within the state of the art, WRI, cannot assume liability for any damages, or misrepresentations, caused by any inaccuracies in the data, or as a result of the data to be used on a particular system. WRI makes no warranty, expressed or implied, nor does the fact of distribution constitute such a warranty.

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  17. Title: Percentage of Woodlots in Sampled Croplands of Central and Western Kenya, 1997

    Contributors:

    Summary: This polygon shapefile shows the percentage of woodlots in sampled cropland in central and western Kenya, 1997. Areas with higher percentages of woodlots cluster more extensively in the foothills of the Aberdare Range and Mount Kenya, and in most communities of Central Kisii, Nyamira, and Buret Districts. A relatively large area of the upper parts of Maragua and Muranga Districts is covered by cropland where woodlots cover more than 12 percent of the land. Close proximity to densely settled rural and urban areas, as well as other centers of high wood demand (for example, tea production) are among the factors behind these spatial patterns. The share of woodlots is much lower in the western parts of the country. Farmers also do not plant woodlots in the more marginal cropping areas with lower rainfall, such as Makueni, Kitui, Mbeere, or Tharaka Districts. Note that these farmers may still plant trees for other purposes and that woodlots are only one of many sources for firewood (other sources include vegetation used to demarcate boundaries, or vegetation on cropland). This data was used in Map 7.3 in Nature's Benefits in Kenya: An Atlas of Ecosystems and Human Well-Being. World Resources Institute. (2007). Percentage of Woodlots in Sampled Croplands of Central and Western Kenya, 1997. World Resources Institute. Available at: http://purl.stanford.edu/ms622nd8468 These data combine detailed crop information from 5,747 aerial photos for a growing season in 1997, each providing a sample point of detailed crop information. These samples were averaged to spatial units (polygons) of croplands from Kenya's most recent land-cover map (FAO 2000). Data set is not for use in litigation. While efforts have been made to ensure that these data are accurate and reliable within the state of the art, WRI, cannot assume liability for any damages, or misrepresentations, caused by any inaccuracies in the data, or as a result of the data to be used on a particular system. WRI makes no warranty, expressed or implied, nor does the fact of distribution constitute such a warranty.

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  18. Title: Food Crops as a Percentage of all Cropland in Central and Western Kenya, 2001

    Contributors:

    Summary: This polygon shapefile depicts the share of cropland that is dedicated to food crops, irrespective of the overall intensity of cultivation. By using only two categories (food and nonfood) and grouping the data into four broad data ranges, the map is relatively robust to the seasonal changes in specific crop choices caused by differences in rainfall, prices, demand, and labor availability. Spatial patterns of food cropping do not necessarily mirror those of cropland intensity. Areas where more than 75 percent of farmers’ cropland is dedicated to food crop are concentrated in high-potential Districts such as Trans Nzoia, Uasin Gishu, Lugari, upper Nandi, and Nakuru (maize and other cereals); Narok (wheat); and lower Kirinyaga (rice). High food-crop shares also occur in more marginal cropping areas such as the Districts bordering Lake Victoria and large parts of Machakos and Makueni Districts (but here low-yielding maize is the major contributor). The lowest shares of food crops (25 percent) cover the tea-growing areas along the Aberdare Range; Mount Kenya; and parts of eastern Bomet, Buret, Kericho, and Nyamira Districts. Areas with a food share of 25-50 percent include the coffee-growing zones of the Aberdare Range and Mount Kenya in Central Province. In the west, for example, in Siaya, Kakamega, and Migori Districts, low shares of food crops are typically paired with sugarcane or tobacco crops. Areas with low shares of food crops in Kitui District may be temporary, reflecting large shares of fallow cropland during the 1997 season of the aerial surveys. This data was used in Map 4.4 in Nature's Benefits in Kenya: An Atlas of Ecosystems and Human Well-Being. World Resources Institute. (2007). Food Crops as a Percentage of all Cropland in Central and Western Kenya, 1997. World Resources Institute. Available at: http://purl.stanford.edu/rk193ch4837 These data combine detailed crop information from 5,747 aerial photos for a growing season in 1997, each providing a sample point of detailed crop information. These samples were averaged to spatial units (polygons) of croplands from Kenya's most recent land-cover map (FAO 2000). Data set is not for use in litigation. While efforts have been made to ensure that these data are accurate and reliable within the state of the art, WRI, cannot assume liability for any damages, or misrepresentations, caused by any inaccuracies in the data, or as a result of the data to be used on a particular system. WRI makes no warranty, expressed or implied, nor does the fact of distribution constitute such a warranty.

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  19. Title: Average Number of Crops Grown in Croplands of Central and Western Kenya, 2001

    Contributors:

    Summary: This polygon shapefile represents the average number of crops grown in croplands of central and western Kenya, 1997. This map combines detailed crop information from 5,747 aerial photos for a growing season in 1997, each providing a sample point of detailed crop information. These samples are averaged to spatial units (polygons) of crop - lands from Kenya’s most recent land cover map (FAO 2000). These averages represent conservative estimates. The raw data indicate that in some sample points farmers grow up to eight different crop species simultaneously. This data was used in Map 5.5 in Nature's Benefits in Kenya: An Atlas of Ecosystems and Human Well-Being. World Resources Institute. (2007). Average Number of Crops Grown in Croplands of Central and Western Kenya, 2001. World Resources Institute. Available at: http://purl.stanford.edu/nj950zc2179 These data combine detailed crop information from 5,747 aerial photos for a growing season in 1997, each providing a sample point of detailed crop information. These samples were averaged to spatial units (polygons) of croplands from Kenya's most recent land-cover map (FAO 2000). These averages represent conservative estimates. The raw data indicate that in some sample points farmers grow up to eight different crop species simultaneously, especially in Kirinyaga, Meru Central, and Gucha Districts. Data set is not for use in litigation. While efforts have been made to ensure that these data are accurate and reliable within the state of the art, WRI, cannot assume liability for any damages, or misrepresentations, caused by any inaccuracies in the data, or as a result of the data to be used on a particular system. WRI makes no warranty, expressed or implied, nor does the fact of distribution constitute such a warranty.

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  20. Title: Large Hydropower Dams: Kenya, 2006

    Contributors:

    Summary: This polygon shapefile shows the locations of large hydropower dams in Kenya. Dams were located based on existing data from the Geological Survey of Kenya (1971) and KenGen (2006). Hydropower dams, although contributing significantly to economic development and human well-being, can have negative impacts on populations and ecosystems as well. Dams can affect downstream water supply, displace people, ruin aesthetic and sometimes spiritual landmarks such as waterfalls, and increase threats to fish and other species that depend on rivers for their habitat. This data was used in Map 3.11 in Nature's Benefits in Kenya: An Atlas of Ecosystems and Human Well-Being. World Resources Institute. (2007). Large Hydropower Dams: Kenya, 2006. World Resources Institute. Available at: http://purl.stanford.edu/db503vt9549 Data set is not for use in litigation. While efforts have been made to ensure that these data are accurate and reliable within the state of the art, WRI, cannot assume liability for any damages, or misrepresentations, caused by any inaccuracies in the data, or as a result of the data to be used on a particular system. WRI makes no warranty, expressed or implied, nor does the fact of distribution constitute such a warranty.

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