This EnviroAtlas dataset estimates tree cover along walkable roads. The road width is estimated for each road and percent tree cover is calculated in a 8.5 meter strip beginning at the estimated road edge. Percent tree cover is calculated for each block between road intersections. In this community, tree cover is defined as Trees & Forest, and Woody Wetlands. Tree cover provides valuable benefits to neighborhood residents and walkers by providing shade, improved aesthetics, and outdoor gathering spaces. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This map is part of the series that covers the whole of Australia at a scale of 1:250 000 (1cm on a map represents 2.5 km on the ground) and comprises 513 maps. This is the largest scale at which published topographic maps cover the entire continent. Each standard map covers an area of 1.5 degrees longitude by 1 degree latitude or about 150 kilometres from east to west and 110 kilometres from north to south. There are about 50 special maps in the series and these maps cover a non-standard area. Typically, where a map produced on standard sheet lines is largely ocean it is combined with its landward neighbour. These maps contain natural and constructed features including road and rail infrastructure, vegetation, hydrography, contours (interval 50m), localities and some administrative boundaries. The topographic map and data index shows coverage of the sheets. Product Specifications Coverage: The series covers the whole of Australia with 513 maps. Currency: Ranges from 1995 to 2009. 95% of maps have a reliability date of 1994 or later. Coordinates: Geographical and either AMG or MGA (post-1993) Datum: AGD66, GDA94, AHD. Projection: Universal Traverse Mercator (UTM) Medium: Paper, flat and folded copies.

This EnviroAtlas dataset estimates green space along walkable roads. Green space within 25 meters of the road centerline is included and the percentage is based on the total area between street intersections. In this community, green space is defined as Trees & Forest, Shrubs, Grass & Herbaceous, Woody Wetlands, and Emergent Wetlands. In this metric, water is also included in green space. Green space provides valuable benefits to neighborhood residents and walkers by providing shade, improved aesthetics, and outdoor gathering spaces. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

Last Update: 08/29/2024The statewide roads dataset is a multi-purpose statewide roads dataset for cartography and range based-address location. This dataset is also used as the base geometry for deriving the GIS-representation of UDOT's highway linear referencing system (LRS). A network analysis dataset for route-finding can also be derived from this dataset. This dataset utilizes a data model based on Next-Generation 911 standards and the Federal Highway Administration's All Roads Network Of Linear-referenced Data (ARNOLD) reporting requirements for state DOTs. UGRC adopted this data model on September 13th, 2017.The statewide roads dataset is maintained by UGRC in partnership with local governments, the Utah 911 Committee, and UDOT. This dataset is updated monthly with Davis, Salt Lake, Utah, Washington and Weber represented every month, along with additional counties based on an annual update schedule. UGRC obtains the data from the authoritative data source (typically county agencies), projects the data and attributes into the current data model, spatially assigns polygon-based fields based on the appropriate SGID boundary, and then standardizes the attribute values to ensure statewide consistency. UGRC also generates a UNIQUE_ID field based on the segment's location in the US National Grid, with the street name then tacked on. The UNIQUE_ID field is static and is UGRC's current, ad hoc solution to a persistent global id. More information about the data model can be found here: https://docs.google.com/spreadsheets/d/1jQ_JuRIEtzxj60F0FAGmdu5JrFpfYBbSt3YzzCjxpfI/edit#gid=811360546 More information about the data model transition can be found here: https://gis.utah.gov/major-updates-coming-to-roads-data-model/We are currently working with US Forest Service to improve the Forest Service roads in this dataset, however, for the most up-to-date and complete set of USFS roads, please visit their data portal where you can download the "National Forest System Roads" dataset.More information can be found on the UGRC data page for this layer:https://gis.utah.gov/data/transportation/roads-system/

This map was designed as an overview map of the Lake Tanganika Basin. Many of the data are of coarse resolution and should be verified before used in an research or planning efforts.Sources by Layer GroupsAdmin: Populations retrieved from worldpopulationreview.com.Town and village names and locations retrieved the NGA GEOnet Names Server (GNS) http://geonames.nga.mil/gns/html/. These data may be incomplete or show incorrect spellings. Refugee camp names and locations provided by Frankfurt Zoological Society. TNC Tuungane Project Villages GPS point locations collected by TNC staff. For more information about the Tuungane Project please visit: https://www.nature.org/ourinitiatives/regions/africa/wherewework/tuungane-project.xml.Interntaional Boundaries retrieved from GADM database (www.gadm.org).Admin Level 1 & 2 subnational boundaries below the country level. This varies by country. Infrastructure:Liemba stops: Derived from https://en.wikivoyage.org/wiki/MV_Liemba\Airport names: Derived from NGA GEOnet Names Server (GNS) http://geonames.nga.mil/gns/html/Roads: The Global Roads Open Access Data Set, Version 1 (gROADSv1) was developed under the auspices of the CODATA Global Roads Data Development Task Group. The data set combines the best available roads data by country into a global roads coverage, using the UN Spatial Data Infrastructure Transport (UNSDI-T) version 2 as a common data model. All country road networks have been joined topologically at the borders, and many countries have been edited for internal topology. Source data for each country are provided in the documentation, and users are encouraged to refer to the readme file for use constraints that apply to a small number of countries. Because the data are compiled from multiple sources, the date range for road network representations ranges from the 1980s to 2010 depending on the country (most countries have no confirmed date), and spatial accuracy varies. The baseline global data set was compiled by the Information Technology Outreach Services (ITOS) of the University of Georgia. Updated data for 27 countries and 6 smaller geographic entities were assembled by Columbia University's Center for International Earth Science Information Network (CIESIN), with a focus largely on developing countries with the poorest data coverage.Credits: http://sedac.ciesin.columbia.edu/data/set/groads-global-roads-open-access-v1Dams: Lehner, B., C. Reidy Liermann, C. Revenga, C. Vorosmarty, B. Fekete, P. Crouzet, P. Doll, M. Endejan, K. Frenken, J. Magome, C. Nilsson, J.C. Robertson, R. Rodel, N. Sindorf, and D. Wisser. 2011. Global Reservoir and Dam Database, Version 1 (GRanDv1): Reservoirs, Revision 01. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC).http://dx.doi.org/10.7927/H4HH6H08. Accessed 28 August 2016.Credits: http://sedac.ciesin.columbia.edu/pfs/grand.htmlPower Plants: Data for power plants with total installed generating capacity > 10 mw from the Platts World Electric Power Plants Database (WEPP 2006). Plants were georeferenced using location information from the WEPP, auxiliary GIS datasets, World Bank project documents and the internet. Locations are approximate, precision varies greatly by point, based on the source of coordinate information.The following attributes are included:PLANT: power plant name,STATUS: status (OPR, CON, PLN, OTHER, UNK),SUM_MW: total installed generating capacity,LATITUDE: approximate location, latitude,LONGITUDE: approximate location, longitude,GEN_TYPE: type of electricity generation (HYDRO, THERMAL, OTHER)Credits: http://www.infrastructureafrica.org/Transmission Lines & Railroads: Africa Infrastructure Knowledge Program http://www.infrastructureafrica.org/.Socioeconomic: FEWS Livelihood Zones, Lean Times Livelihood Hazards: These were derived form country level livelihood zones information at the Famine Early Warning System Network. : Data for individual countries with detailed descriptions of livelihood zones, inclkuding crop calendars and hazards, can be found at http://www.fews.net/.Distance to Markets:HarvestChoice, 2015. "Travel time to nearest town over 20K (mean, hours, 2000)." International Food Policy Research Institute, Washington, DC., and University of Minnesota, St. Paul, MN. Available online at http://harvestchoice.org/data/tt_20k.Lean Times: Lean Times refer to times of the year when food shortages may occur. These were derived form country level livelihood zones information at the Famine Early Warning System Network. NOTE: None of the regions within Lake Tanganyika indicated July as a time of food shortages; therefore, July is excluded as a seperate layer.http://www.fews.net/Population Density: Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. Gridded Population of the World, Version 4 (GPWv4): Population Density. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H4NP22DQ.http://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density2011 Fishiereis Frame Survey sites: Indicates at the regional or district level, the percentage of fish landing sites with described properties. Citation: LTA Secretariat, 2012.Lake Tanganyika Regional Fisheries Frame Survey 2011, Bujumbura, Burundi, 30 pFamily Planning, HIV Statistics, Women Issues, Childrens Health, Water and Sanitation,Houshold Fuel Source: Socioeconomic data from USAID-funded The Demographic and Health Surveys (DHS) Program: Produced by ICF International. Spatial Data Repository, The Demographic and Health Surveys Program. ICF International. Available from spatialdata.dhsprogram.com [Accessed 18 August 2016]. Fishiereis Frame Survey :All Datasets Indicates at the regional or district level, the percentage of fish landing sites with described properties. Citation: LTA Secretariat, 2012.Lake Tanganyika Regional Fisheries Frame Survey 2011, Bujumbura, Burundi, 30 pFishieries Frame SurveyConservation:Human Disturbance Index:Simple Human Disturbance Index to assess the relative levels of human disturbance along the lakeshore of Lake Tanganyika. Evidence from Britton et al.(2017) indicates that human activity in the nearshore environment will significantly influence fish populations along the lakeshore. For detailed methods see https://tnc.box.com/s/k65bdhh72gjjv7f3v0gwvn2856onh9h9.Credits: Dr. Tracy Baker, The Nature Conservancy Africa Program: tracy.baker@tnc.orgHydroBASINS Level 08 Average HDI:Average level of human disturbance at the HydroBASINS Level 8. This level correxpond to the unit of analysis for IUCN Red List data. Credits: Dr. Tracy Baker, The Nature Conservancy Africa Program: tracy.baker@tnc.orgProtected Areas:IUCN and UNEP-WCMC (year), The World Database on Protected Areas (WDPA) [On-line], [January, 2017], Cambridge, UK: UNEP-WCMC. Available at: www.protectedplanet.net.Priority Aquatic Sites: Aquaruim trade watch fish: Estimated ranges of cichlids considered to be endangered or critically endagered, Credit Ad KoningsProposed Lake Key Biodiversity Area & Key Biodiversity Area Trigger Species Ranges: The Nature Conservancy staff worked with IUCN and other experts to compile and analyze available spatial data for Lake Tanganyika, to identify candidate areas within the lake that have exceptional potential to meet the revised KBAcriteria and thresholds based on the new standard, as well as having practical potential for application of local and regional management and conservation strategies. This layer represents a draft version of this work. The work still must undergo a national level stakeholder consultation. Credits: Dr. Kristen Blann, The Nature Conservancy - Freshwater Ecologist, Minnesota Priority Fisheries Conservation Sites - TAFIRI: TAFIRI Conservation Priorities derived from 2013 presentation by Dr. Ismael Kimirei, TAFIRI Director, Kigoma. Priorities were ranked by a quatitative assessment at each site. Priority Fisheries Conservation Sites - Zambia Fisheries: Zambia Fisheries priority sites acquired via personal communication with Mr. Taylor Banda, Senior Fisheries Officer at Mpulungu. The sites represent the current planning scenario alon the Zambia side of the lake. Lake & Freshwater Species & Basin Freshwater Species: Known and accessible information on freshwater species within Lake Tanganyika. Data may not include all known species for a taxon. Spatial unit used to calcuate total freshwater species richness is the HydroBASINS Level 11 dataset boundaries.Species level data were derived from the IUCN Red List of Threatened Species (http://www.iucnredlist.org), the Lake Tanganyika Biodiversity Program (http://www.ltbp.org/), and Ad Konings. Zambia Terrestrial Species Distributions: Mean probability of species presence, conditioned on environmental variables.See: https://tnc.box.com/s/hvqdyawz26i75lm5lnlj7dh0uut65rk7Credits: Dr. Anne Trainor, The Nature Conservancy Africa Program - Smart Growth Director anne.trainor@tnc.orgMammals & Amphibians : Modeled number of mammal species across the Lake Tangnayika Basin. This is a surface layer with no individual species level information given. International Union for Conservation of Nature - IUCN, and Center for International Earth Science Information Network - CIESIN - Columbia University. 2015. Gridded Species Distribution: Global Mammal Richness Grids, 2015 Release. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H4N014G5.Credits: http://sedac.ciesin.columbia.edu/data/set/species-global-mammal-richness-2015Terrestrial Ecoregions & Greater Mahale Ecosystem: Olson, D. M. and E. Dinerstein. 2002. The Global 200: Priority ecoregions for global conservation. (PDF file) Annals of the Missouri Botanical Garden 89:125-126. -The Nature Conservancy, USDA Forest Service and U.S. Geological Survey, based on Bailey, Robert G. 1995. Description of the ecoregions of the United States (2nd ed.). Misc. Pub. No. 1391, Map scale

At this scale 1cm on the map represents 1km on the ground. Each map covers a minimum area of 0.5 degrees longitude by 0.5 degrees latitude or about 54 kilometres by 54 kilometres. The contour interval is 20 metres. Many maps are supplemented by hill shading. These maps contain natural and constructed features including road and rail infrastructure, vegetation, hydrography, contours, localities and some administrative boundaries. Product Specifications Coverage: Australia is covered by more than 3000 x 1:100 000 scale maps, of which 1600 have been published as printed maps. Unpublished maps are available as compilations. Currency: Ranges from 1961 to 2009. Average 1997. Coordinates: Geographical and either AMG or MGA coordinates. Datum: AGD66, GDA94; AHD Projection: Universal Transverse Mercator UTM. Medium: Printed maps: Paper, flat and folded copies. Compilations: Paper or film, flat copies only.

At this scale 1cm on the map represents 1km on the ground. Each map covers a minimum area of 0.5 degrees longitude by 0.5 degrees latitude or about 54 kilometres by 54 kilometres. The contour interval is 20 metres. Many maps are supplemented by hill shading. These maps contain natural and constructed features including road and rail infrastructure, vegetation, hydrography, contours, localities and some administrative boundaries. Product Specifications Coverage: Australia is covered by more than 3000 x 1:100 000 scale maps, of which 1600 have been published as printed maps. Unpublished maps are available as compilations. Currency: Ranges from 1961 to 2009. Average 1997. Coordinates: Geographical and either AMG or MGA coordinates. Datum: AGD66, GDA94; AHD Projection: Universal Transverse Mercator UTM. Medium: Printed maps: Paper, flat and folded copies. Compilations: Paper or film, flat copies only.

Road Centerlines for the City of Shasta Lake, CA. The road centerline file has been maintained as a major component of the Shasta County Integrated Public Safety System and has been updated to meet the needs of the public safety agencies for dispatch. Road centerline data is segmented at each intersecting feature along a route, which means multiple linear features may represent a single route. The road functional classifications assigned to the roadways are based on the California Department of Transportation (CalTrans) CA Road System (CRS) maps. The CRS maps can be viewed at this location: https://www.dot.ca.gov/hq/tsip/hseb/crs_maps/

CDFW BIOS GIS Dataset, Contact: Chris Stermer, Description: These data describe the location and shape of 702 aspen stands on the Eagle Lake RD, Lassen NF. Data were gathered in the summers of 2001-2005. Delineations of aspen stands were based on dominant vegetation types from aerial imagery and field verification conducted by trained Forest Service crews. Assessments were conducted within aspen stands in the region, or stands identified during field surveys. This dataset is considered to be a complete inventory of aspen in these watersheds.

This Mat-Su Borough road centerlines dataset contains assigned official road names, address ranges, and cartographic classifications. This data is used to create the MSAG table for the Enhanced 9-1-1 program and is suitable for geo-coding purposes. Note: Cartographic classification of roads now includes a classification of "NOT CONST'D" which denotes roads that have been platted but not yet constructed. Original data was aggregated by a consultant (McLane Consulting of Soldotna, AK) as a part of the original addressing/911 project. Centerlines were interpolated from existing digital CAD drawings of property and ROW lines. Consultant (McClane) then did field work to append the centerline file to include additional road segments not represented as part of ROW within the property maps. Additional segments were input using GPS and "heads up" 85 digitizing methods. Each was adjusted to fit with the existing data. Data was originally stored in MapInfo (MIF) format and later converted to ESRI shapefile (SHP) format. Additional data related to the state highway system was collected using GPS technology between 1997 and 1999 by the Alaska Department of Transportation. This data was used to supplement the Borough data set for portions of the Parks Highway, Glenn Highway, Old Glenn Highway, Petersville Road, Denali Highway, and Lake Louise Road. Replacement of those street segments based upon property map interpolation but now available within the AK-DOT GPS collection is planned for Summer 2001. Data is maintained in an ongoing basis, primarily taken from subdivision plats, right-of-way plats, or other similar documentation of road existence. Data is input based on road centerlines as shown on subdivision plats and using "heads up" digitizing from aerial imagery.

The map title is Banff. Tactile map scale. 1.7 centimetres = 2 kilometres North arrow pointing to the north. Banff and surrounding region. Trans-Canada Highway, route 1. Airport. Mountains. Bow River. Lake Minnewanka. Secondary roads. Tertiary roads. Tactile maps are designed with Braille, large text, and raised features for visually impaired and low vision users. The Tactile Maps of Canada collection includes: (a) Maps for Education: tactile maps showing the general geography of Canada, including the Tactile Atlas of Canada (maps of the provinces and territories showing political boundaries, lakes, rivers and major cities), and the Thematic Tactile Atlas of Canada (maps showing climatic regions, relief, forest types, physiographic regions, rock types, soil types, and vegetation). (b) Maps for Mobility: to help visually impaired persons navigate spaces and routes in major cities by providing information about streets, buildings and other features of a travel route in the downtown area of a city. (c) Maps for Transportation and Tourism: to assist visually impaired persons in planning travel to new destinations in Canada, showing how to get to a city, and streets in the downtown area.

A local roads area (LRA) is a geographic area without municipal organization. In these areas, land owners organize and form a local roads board. The local roads board makes recommendations on the boundaries of the local roads area and which local roads should be maintained. The authority for the establishment of an LRA is the Local Roads Boards Act R.S.O. 1990 c. L.27. Local Roads Area (LRA) Boundaries: This dataset shows the Locals Road Area (LRA) boundaries established under the Local Roads Boards Act R.S.O. 1990 c. L.27. in the province of Ontario. Local Roads Area (LRA) Local Roads: This dataset shows the local roads maintained by local roads boards in established local roads areas (LRA).

Status

On going: Data is being continually updated

Maintenance and Update Frequency

Unknown: Frequency of maintenance of the data is not known

Contact

Nedim Oren, GIS Coordinator, Northeastern Region (North Bay), Nedim.Oren@ontario.ca