It is estimated that more than 8 billion people live on Earth and the population is likely to hit more than 9 billion by 2050. Approximately 55 percent of Earth’s human population currently live in areas classified as urban. That number is expected to grow by 2050 to 68 percent, according to the United Nations (UN).The largest cities in the world include Tōkyō, Japan; New Delhi, India; Shanghai, China; México City, Mexico; and São Paulo, Brazil. Each of these cities classifies as a megacity, a city with more than 10 million people. The UN estimates the world will have 43 megacities by 2030.Most cities' populations are growing as people move in for greater economic, educational, and healthcare opportunities. But not all cities are expanding. Those cities whose populations are declining may be experiencing declining fertility rates (the number of births is lower than the number of deaths), shrinking economies, emigration, or have experienced a natural disaster that resulted in fatalities or forced people to leave the region.This Global Cities map layer contains data published in 2018 by the Population Division of the United Nations Department of Economic and Social Affairs (UN DESA). It shows urban agglomerations. The UN DESA defines an urban agglomeration as a continuous area where population is classified at urban levels (by the country in which the city resides) regardless of what local government systems manage the area. Since not all places record data the same way, some populations may be calculated using the city population as defined by its boundary and the metropolitan area. If a reliable estimate for the urban agglomeration was unable to be determined, the population of the city or metropolitan area is used.Data Citation: United Nations Department of Economic and Social Affairs. World Urbanization Prospects: The 2018 Revision. Statistical Papers - United Nations (ser. A), Population and Vital Statistics Report, 2019, https://doi.org/10.18356/b9e995fe-en.

The dataset and the validation are fully described in a Nature Scientific Data Descriptor https://www.nature.com/articles/s41597-019-0265-5

If you want to use this dataset in an interactive environment, then use this link https://mybinder.org/v2/gh/GeographerAtLarge/TravelTime/HEAD

The following text is a summary of the information in the above Data Descriptor.

The dataset is a suite of global travel-time accessibility indicators for the year 2015, at approximately one-kilometre spatial resolution for the entire globe. The indicators show an estimated (and validated), land-based travel time to the nearest city and nearest port for a range of city and port sizes.

The datasets are in GeoTIFF format and are suitable for use in Geographic Information Systems and statistical packages for mapping access to cities and ports and for spatial and statistical analysis of the inequalities in access by different segments of the population.

These maps represent a unique global representation of physical access to essential services offered by cities and ports.

The datasets travel_time_to_cities_x.tif (where x has values from 1 to 12) The value of each pixel is the estimated travel time in minutes to the nearest urban area in 2015. There are 12 data layers based on different sets of urban areas, defined by their population in year 2015 (see PDF report).

travel_time_to_ports_x (x ranges from 1 to 5)

The value of each pixel is the estimated travel time to the nearest port in 2015. There are 5 data layers based on different port sizes.

Format Raster Dataset, GeoTIFF, LZW compressed Unit Minutes

Data type Byte (16 bit Unsigned Integer)

No data value 65535

Flags None

Spatial resolution 30 arc seconds

Spatial extent

Upper left -180, 85

Lower left -180, -60 Upper right 180, 85 Lower right 180, -60 Spatial Reference System (SRS) EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long)

Temporal resolution 2015

Temporal extent Updates may follow for future years, but these are dependent on the availability of updated inputs on travel times and city locations and populations.

Methodology Travel time to the nearest city or port was estimated using an accumulated cost function (accCost) in the gdistance R package (van Etten, 2018). This function requires two input datasets: (i) a set of locations to estimate travel time to and (ii) a transition matrix that represents the cost or time to travel across a surface.

The set of locations were based on populated urban areas in the 2016 version of the Joint Research Centre’s Global Human Settlement Layers (GHSL) datasets (Pesaresi and Freire, 2016) that represent low density (LDC) urban clusters and high density (HDC) urban areas (https://ghsl.jrc.ec.europa.eu/datasets.php). These urban areas were represented by points, spaced at 1km distance around the perimeter of each urban area.

Marine ports were extracted from the 26th edition of the World Port Index (NGA, 2017) which contains the location and physical characteristics of approximately 3,700 major ports and terminals. Ports are represented as single points

The transition matrix was based on the friction surface (https://map.ox.ac.uk/research-project/accessibility_to_cities) from the 2015 global accessibility map (Weiss et al, 2018).

Code The R code used to generate the 12 travel time maps is included in the zip file that can be downloaded with these data layers. The processing zones are also available.

Validation The underlying friction surface was validated by comparing travel times between 47,893 pairs of locations against journey times from a Google API. Our estimated journey times were generally shorter than those from the Google API. Across the tiles, the median journey time from our estimates was 88 minutes within an interquartile range of 48 to 143 minutes while the median journey time estimated by the Google API was 106 minutes within an interquartile range of 61 to 167 minutes. Across all tiles, the differences were skewed to the left and our travel time estimates were shorter than those reported by the Google API in 72% of the tiles. The median difference was −13.7 minutes within an interquartile range of −35.5 to 2.0 minutes while the absolute difference was 30 minutes or less for 60% of the tiles and 60 minutes or less for 80% of the tiles. The median percentage difference was −16.9% within an interquartile range of −30.6% to 2.7% while the absolute percentage difference was 20% or less in 43% of the tiles and 40% or less in 80% of the tiles.

This process and results are included in the validation zip file.

Usage Notes The accessibility layers can be visualised and analysed in many Geographic Information Systems or remote sensing software such as QGIS, GRASS, ENVI, ERDAS or ArcMap, and also by statistical and modelling packages such as R or MATLAB. They can also be used in cloud-based tools for geospatial analysis such as Google Earth Engine.

The nine layers represent travel times to human settlements of different population ranges. Two or more layers can be combined into one layer by recording the minimum pixel value across the layers. For example, a map of travel time to the nearest settlement of 5,000 to 50,000 people could be generated by taking the minimum of the three layers that represent the travel time to settlements with populations between 5,000 and 10,000, 10,000 and 20,000 and, 20,000 and 50,000 people.

The accessibility layers also permit user-defined hierarchies that go beyond computing the minimum pixel value across layers. A user-defined complete hierarchy can be generated when the union of all categories adds up to the global population, and the intersection of any two categories is empty. Everything else is up to the user in terms of logical consistency with the problem at hand.

The accessibility layers are relative measures of the ease of access from a given location to the nearest target. While the validation demonstrates that they do correspond to typical journey times, they cannot be taken to represent actual travel times. Errors in the friction surface will be accumulated as part of the accumulative cost function and it is likely that locations that are further away from targets will have greater a divergence from a plausible travel time than those that are closer to the targets. Care should be taken when referring to travel time to the larger cities when the locations of interest are extremely remote, although they will still be plausible representations of relative accessibility. Furthermore, a key assumption of the model is that all journeys will use the fastest mode of transport and take the shortest path.

This map features boundaries and places for the World, including countries, 1st order administrative areas, and cities. The map layers are delivered as features, which you can click on for attribute information or re-symbolize as you choose.

This global accessibility map enumerates land-based travel time to the nearest densely-populated area for all areas between 85 degrees north and 60 degrees south for a nominal year 2015. Densely-populated areas are defined as a contiguous area with 1,500 or more inhabitants per square kilometre or a majority of built-up land cover types coincident with a population centre of at least 50,000 inhabitants.This dataset is described in "Mapping inequality in accessibility: a global assessment of travel time to cities in 2015" (Weiss et al 2018; doi:10.1038/nature25181)This map was produced through a collaboration between MAP (University of Oxford), Google, the European Union Joint Research Centre (JRC), and the University of Twente, Netherlands.The underlying datasets used to produce the map include roads (comprising the first ever global-scale use of Open Street Map and Google roads datasets), railways, rivers, lakes, oceans, topographic conditions (slope and elevation), landcover types, and national borders. These datasets were each allocated a speed or speeds of travel in terms of time to cross each pixel of that type. The datasets were then combined to produce a "friction surface"; a map where every pixel is allocated a nominal overall speed of travelbased on the types occurring within that pixel. Least-cost-path algorithms (running in Google Earth Engine and, for high-latitude areas, in R) wereused in conjunction with this friction surface to calculate the time of travel from all locations to the nearest (in time) city. The cities dataset used is the high-density-cover product created by the Global Human Settlement Project. Each pixel in the resultant accessibility map thus represents the modelled shortest time from that location to a city.

World Cities provides a base map layer of the cities for the world. The cities include national capitals, provincial capitals, major population centers, and landmark cities.

This global accessibility map enumerates land-based travel time to the nearest densely-populated area for all areas between 85 degrees north and 60 degrees south for a nominal year 2015. Densely-populated areas are defined as contiguous areas with 1,500 or more inhabitants per square kilometre or a majority of built-up land cover types coincident with a population centre of at least 50,000 inhabitants. This map was produced through a collaboration between MAP (University of Oxford), Google, the European Union Joint Research Centre (JRC), and the University of Twente, Netherlands.The underlying datasets used to produce the map include roads (comprising the first ever global-scale use of Open Street Map and Google roads datasets), railways, rivers, lakes, oceans, topographic conditions (slope and elevation), landcover types, and national borders. These datasets were each allocated a speed or speeds of travel in terms of time to cross each pixel of that type. The datasets were then combined to produce a "friction surface"; a map where every pixel is allocated a nominal overall speed of travel based on the types occurring within that pixel. Least-cost-path algorithms (running in Google Earth Engine and, for high-latitude areas, in R) were used in conjunction with this friction surface to calculate the time of travel from all locations to the nearest (in time) city. The cities dataset used is the high-density-cover product created by the Global Human Settlement Project. Each pixel in the resultant accessibility map thus represents the modelled shortest time from that location to a city. Authors: D.J. Weiss, A. Nelson, H.S. Gibson, W. Temperley, S. Peedell, A. Lieber, M. Hancher, E. Poyart, S. Belchior, N. Fullman, B. Mappin, U. Dalrymple, J. Rozier, T.C.D. Lucas, R.E. Howes, L.S. Tusting, S.Y. Kang, E. Cameron, D. Bisanzio, K.E. Battle, S. Bhatt, and P.W. Gething. A global map of travel time to cities to assess inequalities in accessibility in 2015. (2018). Nature. doi:10.1038/nature25181

Processing notes: Data were processed from numerous sources including OpenStreetMap, Google Maps, Land Cover mapping, and others, to generate a global friction surface of average land-based travel speed. This accessibility surface was then derived from that friction surface via a least-cost-path algorithm finding at each location the closest point from global databases of population centres and densely-populated areas. Please see the associated publication for full details of the processing.

Source: https://map.ox.ac.uk/research-project/accessibility_to_cities/

A global analysis of accessibility to high-density urban centres at a resolution of 1×1 kilometre for 2015, as measured by travel time.To model the time required for individuals to reach their most accessible city, we first quantified the speed at which humans move through the landscape. The principle underlying this work was that all areas on Earth, represented as pixels within a 2D grid, had a cost (that is, time) associated with moving through them that we quantified as a movement speed within a cost or ‘friction’ surface. We then applied a least-cost-path algorithm to the friction surface in relation to a set of high-density urban points. The algorithm calculated pixel-level travel times for the optimal path between each pixel and its nearest city (that is, with the shortest journey time). From this work we ultimately produced two products: (a) an accessibility map showing travel time to urban centres, as cities are proxies for access to many goods and services that affect human wellbeing; and (b) a friction surface that underpins the accessibility map and enables the creation of custom accessibility maps from other point datasets of interest. The map products are in GeoTIFF format in EPSG:4326 (WGS84) project with a spatial resolution of 30 arcsecs. The accessibility map pixel values represent travel time in minutes. The friction surface map pixels represent the time, in minutes required to travel one metre. This DANS data record contains these two map products. Issued: 2018-01-10

A feature layer of world cities with labels, for illustrative purposes only for use as a reference layer. This feature layer is pointing to the Political_Map_World_Cities_Features layer provided by Maps.com. The symbology and labels were modified slightly in this version.This layer is used as a reference layer in NOAA NCEI's VIIRS Nighttime Imagery map viewer, displayed in the 3D global view.

Dataset

This dataset was created by viswachaitanyasai

Released under MIT

Contents

This global accessibility map enumerates land-based travel time to the nearest densely-populated area for all areas between 85 degrees north and 60 degrees south for a nominal year 2015. Densely-populated areas are defined as contiguous areas with 1,500 or more inhabitants per square kilometer or a majority of built-up land cover types coincident with a population centre of at least 50,000 inhabitants. This map was produced through a collaboration between the University of Oxford Malaria Atlas Project (MAP), Google, the European Union Joint Research Centre (JRC), and the University of Twente, Netherlands. The underlying datasets used to produce the map, include roads (comprising the first ever global-scale use of Open Street Map and Google roads datasets), railways, rivers, lakes, oceans, topographic conditions (slope and elevation), landcover types, and national borders. These datasets were each allocated a speed or speeds of travel in terms of time to cross each pixel of that type. The datasets were then combined to produce a “friction surface”, a map where every pixel is allocated a nominal overall speed of travel based on the types occurring within that pixel. Least-cost-path algorithms (running in Google Earth Engine and, for high-latitude areas, in R) were used in conjunction with this friction surface to calculate the time of travel from all locations to the nearest city (by travel time). Cities were determined using the high-density-cover product created by the Global Human Settlement Project. Each pixel in the resultant accessibility map thus represents the modeled shortest time from that location to a city. Full Citation D.J. Weiss, A. Nelson, H.S. Gibson, W. Temperley, S. Peedell, A. Lieber, M. Hancher, E. Poyart, S. Belchior, N. Fullman, B. Mappin, U. Dalrymple, J. Rozier, T.C.D. Lucas, R.E. Howes, L.S. Tusting, S.Y. Kang, E. Cameron, D. Bisanzio, K.E. Battle, S. Bhatt, and P.W. Gething. A global map of travel time to cities to assess inequalities in accessibility in 2015. (2018). Nature. doi:10.1038/nature25181.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

A large number of European cities are covered by this dataset; for each city you can find one or more Cartosat-1 ortho image products and one or more Euro-Maps 3D DSM tiles clipped to the extent of the ortho coverage. The Euro-Maps 3D DSM is a homogeneous, 5 m spaced Digital Surface Model semi-automatically derived from 2.5 m Cartosat-1 in-flight stereo data with a vertical accuracy of 10 m. The very detailed and accurate representation of the surface is achieved by using a sophisticated and well adapted algorithm implemented on the basis of the Semi-Global Matching approach. The final product includes several pixel-based quality and traceability layers: The dsm layer (_dsm.tif) contains the elevation heights as a geocoded raster file The source layer (_src.tif) contains information about the data source for each height value/pixel The number layer (_num.tif) contains for each height value/pixel the number of IRS-P5 Cartosat-1 stereo pairs used for the generation of the DEM The quality layer (_qc.tif) is set to 1 for each height/pixel value derived from IRS-P5 Cartosat-1 data and which meets or exceeds the product specifications The accuracy vertical layer (*_acv.tif) contains the absolute vertical accuracy for each quality controlled height value/pixel. The ortho image is a Panchromatic image at 2.5 m resolution. The following table defines the offered product types. EO-SIP product type Description PAN_PAM_3O IRS-P5 Cartosat-1 ortho image DSM_DEM_3D IRS-P5 Cartosat-1 DSM

NCED is currently involved in researching the effectiveness of anaglyph maps in the classroom and are working with educators and scientists to interpret various Earth-surface processes. Based on the findings of the research, various activities and interpretive information will be developed and available for educators to use in their classrooms. Keep checking back with this website because activities and maps are always being updated. We believe that anaglyph maps are an important tool in helping students see the world and are working to further develop materials and activities to support educators in their use of the maps.

This website has various 3-D maps and supporting materials that are available for download. Maps can be printed, viewed on computer monitors, or projected on to screens for larger audiences. Keep an eye on our website for more maps, activities and new information. Let us know how you use anaglyph maps in your classroom. Email any ideas or activities you have to ncedmaps@umn.edu

Anaglyph paper maps are a cost effective offshoot of the GeoWall Project. Geowall is a high end visualization tool developed for use in the University of Minnesota's Geology and Geophysics Department. Because of its effectiveness it has been expanded to 300 institutions across the United States. GeoWall projects 3-D images and allows students to see 3-D representations but is limited because of the technology. Paper maps are a cost effective solution that allows anaglyph technology to be used in classroom and field-based applications.

Maps are best when viewed with RED/CYAN anaglyph glasses!

A note on downloading: "viewable" maps are .jpg files; "high-quality downloads" are .tif files. While it is possible to view the latter in a web-browser in most cases, the download may be slow. As an alternative, try right-clicking on the link to the high-quality download and choosing "save" from the pop-up menu that results. Save the file to your own machine, then try opening the saved copy. This may be faster than clicking directly on the link to open it in the browser.

World Map: 3-D map that highlights oceanic bathymetry and plate boundaries.

Continental United States: 3-D grayscale map of the Lower 48.

Western United States: 3-D grayscale map of the Western United States with state boundaries.

Regional Map: 3-D greyscale map stretching from Hudson Bay to the Central Great Plains. This map includes the Western Great Lakes and the Canadian Shield.

Minnesota Map: 3-D greyscale map of Minnesota with county and state boundaries.

Twin Cities: 3-D map extending beyond Minneapolis and St. Paul.

Twin Cities Confluence Map: 3-D map highlighting the confluence of the Mississippi and Minnesota Rivers. This map includes most of Minneapolis and St. Paul.

Minneapolis, MN: 3-D topographical map of South Minneapolis.

Bassets Creek, Minneapolis: 3-D topographical map of the Bassets Creek watershed.

North Minneapolis: 3-D topographical map highlighting North Minneapolis and the Mississippi River.

St. Paul, MN: 3-D topographical map of St. Paul.

Western Suburbs, Twin Cities: 3-D topographical map of St. Louis Park, Hopkins and Minnetonka area.

Minnesota River Valley Suburbs, Twin Cities: 3-D topographical map of Bloomington, Eden Prairie and Edina area.

Southern Suburbs, Twin Cities: 3-D topographical map of Burnsville, Lakeville and Prior Lake area.

Southeast Suburbs, Twin Cities: 3-D topographical map of South St. Paul, Mendota Heights, Apple Valley and Eagan area.

Northeast Suburbs, Twin Cities: 3-D topographical map of White Bear Lake, Maplewood and Roseville area.

Northwest Suburbs, Mississippi River, Twin Cities: 3-D topographical map of North Minneapolis, Brooklyn Center and Maple Grove area.

Blaine, MN: 3-D map of Blaine and the Mississippi River.

White Bear Lake, MN: 3-D topographical map of White Bear Lake and the surrounding area.

Maple Grove, MN: 3-D topographical map of the NW suburbs of the Twin Cities.

Minnesota River: 3-D topographical map of the Minnesota River Valley highlighting the river bend in Mankato.

St. Croix River: 3-D topographical map of the St. Croix extending from Taylors Falls to the Mississippi confluence.

Mississippi River, Lake Pepin: 3-D topographical map of the confluence of Chippewa Creek and the Mississippi River.

Red Wing, MN: 3-D topographical map of Redwing, MN on the Mississippi River.

Winona, Minnesota: 3-D topographical map of Winona, MN highlighting the Mississippi River.

Cannon Falls, MN: 3-D topographical map of Cannon Falls area.

Rochester, MN: 3-D topographical map of Rochester and the surrounding area.

Northfield, MN: 3-D topographical map of Northfield and the surrounding area.

St. Louis River, MN: 3-D map of the St. Louis River and Duluth, Minnesota.

Lake Itasca, MN: 3-D map of the source of the Mississippi River.

Elmore, MN: 3-D topographical map of Elmore, MN in south-central Minnesota.

Glencoe, MN: 3-D topographical map of Glencoe, MN.

New Prague, MN: 3-D topographical map of the New Prague in south-central Minnesota.

Plainview, MN: 3-D topographical map of Plainview, MN.

Waterville-Morristown: 3-D map of the Waterville-Morris area in south-central Minnesota.

Eau Claire, WI: 3-D map of Eau Claire highlighting abandon river channels.

Dubuque, IA: 3-D topographical map of Dubuque and the Mississippi River.

Londonderry, NH: 3-D topographical map of Londonderry, NH.

Santa Cruz, CA: 3-D topographical map of Santa Cruz, California.

Crater Lake, OR: 3-D topographical map of Crater Lake, Oregon.

Mt. Rainier, WA: 3-D topographical map of Mt. Rainier in Washington.

Grand Canyon, AZ: 3-D topographical map of the Grand Canyon.

District of Columbia: 3-D map highlighting the confluence of the rivers and the Mall.

Ireland: 3-D grayscale map of Ireland.

New Jersey: 3-D grayscale map of New Jersey.

SP Crater, AZ: 3-D map of random craters in the San Francisco Mountains.

Mars Water Features: 3-D grayscale map showing surface water features from Mars.

This vector tile layer presents the World Navigation Map (Places) style (World Edition) and provides a basemap for the world, featuring a Navigation style designed for use during the day in mobile devices with the additional content of global Places. These shops, services, restaurants, attractions, and other points of interest are displayed with icons and labels. This comprehensive street map includes highways, major roads, minor roads, railways, water features, cities, parks, landmarks, building footprints, and administrative boundaries. This vector tile layer provides unique capabilities for customization, high-resolution display, and use in mobile devices.This vector tile layer is built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.The Places data sources in this map include:United States and Canada: SafeGraphrest of the World: TomTomThis layer is used in the Navigation (Places) web map included in ArcGIS Living Atlas of the World.See the Vector Basemaps group for other vector tile layers. Customize this StyleLearn more about customizing this vector basemap style using the Vector Tile Style Editor. Additional details are available in ArcGIS Online Blogs and the Esri Vector Basemaps Reference Document.

The "Major Cities" layer is derived from the "World Cities" dataset provided by ArcGIS Data and Maps group as part of the global data layers made available for public use. "Major cities" layer specifically contains National and Provincial capitals that have the highest population within their respective country. Cities were filtered based on the STATUS (“National capital”, “National and provincial capital”, “Provincial capital”, “National capital and provincial capital enclave”, and “Other”). Majority of these cities within larger countries have been filtered at the highest levels of POP_CLASS (“5,000,000 and greater” and “1,000,000 to 4,999,999”). However, China for example, was filtered with cities over 11 million people due to many highly populated cities. Population approximations are sourced from US Census and UN Data. Credits: ESRI, CIA World Factbook, GMI, NIMA, UN Data, UN Habitat, US Census Bureau Disclaimer: The designations employed and the presentation of material at this site do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.

This city boundary shapefile was extracted from Esri Data and Maps for ArcGIS 2014 - U.S. Populated Place Areas. This shapefile can be joined to 500 Cities city-level Data (GIS Friendly Format) in a geographic information system (GIS) to make city-level maps.

This dataset is built from the Overture 2023-07-26-alpha.0 version of open map data by the Overture Maps Foundation. This dataset compiles points of interests (POIs) for individual cities for convenient and lightweight spatial sampling.Credits: Overture Maps FoundationLicense: https://cdla.dev/permissive-2-0/

World Cities represents a base map layer of the locations of cities for the world. The cities include national capitals, provincial capitals, major population centers, and landmark cities.

Silencio’s Noise Pollution Index Maps provide on-demand, high-resolution visualizations of urban and regional noise patterns worldwide. Built from 35 billion+ real noise measurements and AI-enhanced interpolation, these maps are designed for seamless integration into GIS platforms, smart city dashboards, and real estate location intelligence tools. Only available on request - data in sample is the measurement basis on which the maps are created.

Maps are generated on-demand for: • Cities, regions, or custom-defined areas globally • Real estate platforms for environmental and livability scoring • Government portals and dashboards for noise regulation, urban planning, and public health monitoring • Smart city applications to visualize urban soundscapes and improve planning and infrastructure design

Key Features: • Global coverage with consistent high-density data across continents • Generated specifically based on client-selected regions or cities • Fully GIS-ready in high-resolution image formats • Derived from real measurements combined with AI-powered interpolation for accuracy

Flexible delivery via image exports and available for custom licensing.

This map is designed to be used as a basemap by GIS professionals and as a reference map by anyone. The map includes administrative boundaries, cities, water features, physiographic features, parks, landmarks, highways, roads, railways, and airports overlaid on land cover and shaded relief imagery for added context. The map provides coverage for the world down to a scale of ~1:72k. Coverage is provided down to ~1:4k for the following areas: Australia and New Zealand; India; Europe; Canada; Mexico; the continental United States and Hawaii; South America and Central America; Africa; and most of the Middle East. Coverage down to ~1:1k and ~1:2k is available in select urban areas. This basemap was compiled from a variety of best available sources from several data providers, including the U.S. Geological Survey (USGS), U.S. Environmental Protection Agency (EPA), U.S. National Park Service (NPS), Food and Agriculture Organization of the United Nations (FAO), Department of Natural Resources Canada (NRCAN), GeoBase, Agriculture and Agri-Food Canada, Garmin, HERE, Esri, OpenStreetMap contributors, and the GIS User Community. For more information on this map, including our terms of use, visit us online at http://goto.arcgisonline.com/maps/World_Topo_Map