Natural Earth is a public domain map dataset available at 1:10m, 1:50m, and 1:110 million scales. Featuring tightly integrated vector and raster data, with Natural Earth you can make a variety of visually pleasing, well-crafted maps with cartography or GIS software.
Natural Earth was built through a collaboration of many volunteers and is supported by NACIS (North American Cartographic Information Society).
Natural Earth Vector comes in ESRI shapefile format, the de facto standard for vector geodata. Character encoding is Windows-1252.
Natural Earth Vector includes features corresponding to the following:
Cultural Vector Data Thremes:
Physical Vector Data Themes:
Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
License information was derived automatically
The Bright Earth eAtlas Basemap dataset collection is a satellite-derived global map of the world at a 1:1M scale for most of the world and 1:200k scale for Australia. This map was inspired by Natural Earth II (NEII) and NASA's Blue Marble Next Generation (BMNG) imagery.
Its aim was to provide a basemap similar to NEII but with a higher resolution (~10x).
This basemap is derived from the following datasets: Blue Marble Next Generation 2004-04 (NASA), VMap0 coastline, Coast100k 2004 Australian coastline (GeoScience Australia), SRTM30 Plus v8.0 (UCSD) hillshading, Natural Earth Vector 10m bathymetry and coastline v2.0 (NE), gbr100 hillshading (JCU).
This dataset (World_Bright-Earth-e-Atlas-basemap) contains all the files required to setup the Bright Earth eAtlas basemap in a GeoServer. All the data files are stored in GeoTiffs or shapefiles and so can also be loaded into ArcMap, however no styling has been included for this purpose.
This basemap is small enough (~900 MB) that can be readily used locally or deployed to a GeoServer.
Base map aesthetics (added 28 Jan 2025)
The Bright Earth e-Atlas Basemap is a high-resolution representation of the Earth's surface, designed to depict global geography with clarity, natural aesthetics with bright and soft color tones that enhance data overlays without overwhelming the viewer. The land areas are based on NASA's Blue Marble imagery, with modifications to lighten the tone and apply noise reduction filtering to soften the overall coloring. The original Blue Marble imagery was based on composite satellite imagery resulting in a visually appealing and clean map that highlights natural features while maintaining clarity and readability. Hillshading has been applied across the landmasses to enhance detail and texture, bringing out the relief of mountainous regions, plateaus, and other landforms.
The oceans feature three distinct depth bands to illustrate shallow continental areas, deeper open ocean zones, and the very deep trenches and basins. The colors transition from light blue in shallow areas to darker shades in deeper regions, giving a clear sense of bathymetric variation. Hillshading has also been applied to the oceans to highlight finer structures on the seafloor, such as ridges, trenches, and other geological features, adding depth and dimensionality to the depiction of underwater topography.
At higher zoom levels prominent cities are shown and the large scale roads are shown for Australia.
Rendered Raster Version (added 28 Jan 2025)
A low resolution version of the dataset is available as a raster file (PNG, JPG and GeoTiff) at ~2 km and 4 km resolutions. These rasters are useful for applications where GeoServer is not available to render the data dynamically. While the rasters are large they represent a small fraction of the full detail of the dataset. The rastered version was produced using the layout manager in QGIS to render maps of the whole world, pulling the imagery from the eAtlas GeoServer. This imagery from converted to the various formats using GDAL. More detail is provided in 'Rendered-bright-earth-processing.txt' in the download and browse section.
Change Log 2025-01-28: Added two rendered raster versions of the dataset at 21600x10800 and 10400x5400 pixels in size in PNG, JPG and GeoTiff format. Added
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
Created for the tutorial Design symbology for a thematic map.
Source: Natural Earth and 2015 Inter-census Survey by Badan Pusat Statistik, Jakarta, via Wikipedia
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
The Green Book Online is a fully searchable database which gives New Yorkers the opportunity to search for the agencies, offices, boards and commissions that keep our City running. It includes listings for New York City, County, Courts, and New York State government offices.
This is a dataset hosted by the City of New York. The city has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York City using Kaggle and all of the data sources available through the City of New York organization page!
This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.
Cover photo by Venveo on Unsplash
Unsplash Images are distributed under a unique Unsplash License.
ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
License information was derived automatically
The Hills of Governor's Island Dataset for QGIS
This archive contains a QGIS project and a geopackage with raster and vector data for the Hills region of Governor's Island, New York City, USA. The CRS is NAD83 / New York Long Island (ftUS) with the EPSG code 2263.
Data Sources
License
This dataset is licensed under the ODC Public Domain Dedication and License 1.0 (PDDL) by Brendan Harmon.
ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
License information was derived automatically
Basic Global Dataset for GRASS GIS
This archive contains a QGIS project and a geopackage with raster and vector data for Governor's Island, New York City, USA. The CRS is NAD83 / New York Long Island (ftUS) with the EPSG code 2263.
Data Sources
License
This dataset is licensed under the ODC Public Domain Dedication and License 1.0 (PDDL) by Brendan Harmon.
This dataset was created by Diogo Caliman
It contains the following files:
Available as a single coverage collection of data over 50 European Cities acquired by KOMPSAT-1’s Electro-Optical Camera (EOC) geolocated and orthorectified. The dataset is composed by PAN imagery at 6.6 m GSD, in GeoTIFF orthorectified format.
Cities in Canada dataset from NRCAN - Geographical Names Data.https://natural-resources.canada.ca/earth-sciences/geography/download-geographical-names-data/9245Filted the full Canada dataset by:Generic Category = Populated PlacesGeneric Term = City
Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
License information was derived automatically
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset collection contains GIS layers for creating the AIMS eReefs visualisation maps (https://ereefs.aims.gov.au/). These datasets are useful for creating A4 printed maps of the Great Barrier Reef and the Coral Sea. It contains the following datasets: - Countries - Australia plus surrounding countries at 1:10M scale. Crop of Natural Earth Data 1:10 Admin 0 - Countries dataset. Allows filtering out of surrounding countries. - Cities - 21 Cities along the Queensland coastline. - Basins - Drainage basins adjacent to the Great Barrier Reef along the eastern Queensland coastline. Derived from Geoscience Australia River Basins 1997 dataset. It is a subset and reprojection. - Land and Basins - This layer contains both Queensland and PNG land areas, along with the river basins along the eastern Queensland coastline. This is an integrated layer that represents both the background land area and the river basins all in one layer. This layer saves having to map the land area, then overlay the river basins. In this way each polygon only needs to be rendered once. The goal of this layer is to optmise the rendering time of the eReefs base map. This dataset is made up from the Geoscience Australia Australia's River Basins 1997 dataset for the Queensland coastline and the eastern Queensland basins. PNG is copied from Natural Earth Data 10 m countries dataset. - Rivers - Rivers that drain along the Queensland eastern coast. This is a subset of the Geoscience Australia Geodata Topo 1:5M 2004. - Reefs - Boundaries of reefs in GBR, Torres Strait and Coral Sea. In the Coral Sea it contains the atoll platform boundaries rather than the individual reefs. This is derived from the GBRMPA GBR features dataset, AIMS Torres Strait features dataset and the AIMS Coral Sea features dataset. These were combined and simplified to a scale of 1:1M. Note that this simplification resulted in multiple neighbouring reefs being grouped together. This dataset is intended for visual rendering of maps. - Clip regions - Polygons for clipping eReefs data to the GBR. Also contains approximate polygons for Coral Sea, Torres Strait, PNG and New Caledonia. This was created principally for setting the region attribute for the Reefs dataset, but was made available as it is useful for clipping eReefs data to the GBR for plotting purposes.
Methods: Most of the base map layers are derived from a variety of data sources. The full workflow used to transform these source datasets is documented on GitHub (https://github.com/eatlas/GBR_AIMS_eReefs-basemap).
Limitations of the data: The datasets in this collection have been cropped and simplified for the purposes of creating low detail printed maps of the GBR. They are not intended for creating a high resolution base map.
Format of the data: Shapefile and GeoJSON files. The Cities dataset is provided as a CSV file.
Location of the data: This dataset is filed in the eAtlas enduring data repository at: data\custodian\2018-2024-eReefs\GBR_AIMS_eReefs-basemap
This data set includes 16 zipped archives of shapefiles of cities, rivers and streams, roads, and study area boundaries of several Amazonian study sites: Altamira, Santarem, Bragantina, and Ponta de Pedras, in the state of Para, and 1 site at Machadinho D'Oeste, in the state of Rondonia. Data from Brazil were digitized from Instituto Nacional de Colonizacao e Reforma Agraria (INCRA) maps and other data from Instituto Brasileiro de Geografia e Estatistica (IBGE). These products were prepared in the 2000-2004 time period. The data of creation for the source material is unknown.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Spatial dataset derived from many different open data repositories and cropped on the Omo-Turkana Basin boundary, used to create a base-map describing the components of Water-Energy-Food nexus in the case study.
omo-turkana.gpkg: vector dataset including the following layers, together with the related map style for QGIS Desktop used in the DAFNE Geoportal basemap:
zambezi_raster.zip: raster dataset including the following layers:
Original data sources include:
Natural Earth, a public domain map dataset available at different scales;
Protected Planet, the most up to date and complete source of data on protected areas and other effective area-based conservation measures, maintained by UNEP-WCMC and IUCN;
OpenStreetMap, a collaborative project to create a free editable map of the world;
NASA's Shuttle Radar Topography Mission (SRTM) Digital Elevation Database;
Global Water Surface, a virtual time machine that maps the location and temporal distribution of water surfaces at the global scale.
This dataset was created by Rubayet Alam
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
Nowadays, climate change is a major problem. The air is getting polluted at unprecedented levels increasing the number of diseases due to air pollution. Now, it's our time to use technologies to assess this problem and help the mankind.
I acquired the data by web scraping. The data represents the period from March 1, 2013, to February 28, 2017
I want to thank the machine learning community members at the University of California, Irvine, for helping me and cooperating with my tasks.
I want people to calculate and analyze the air quality index of different cities and how it's correlated with the environmental surroundings. Gain some insights into how the data is spread, how does each factor affects the concentrations of different pollutants and so on.
LandCoverNet is a global annual land cover classification training dataset with labels for the multi-spectral satellite imagery from Sentinel-1, Sentinel-2 and Landsat-8 missions in 2018. LandCoverNet Asia contains data across Asia, which accounts for ~31% of the global dataset. Each pixel is identified as one of the seven land cover classes based on its annual time series. These classes are water, natural bare ground, artificial bare ground, woody vegetation, cultivated vegetation, (semi) natural vegetation, and permanent snow/ice.
There are a total of 2753 image chips of 256 x 256 pixels in LandCoverNet South America V1.0 spanning 92 tiles. Each image chip contains temporal observations from the following satellite products with an annual class label, all stored in raster format (GeoTIFF files):
* Sentinel-1 ground range distance (GRD) with radiometric calibration and orthorectification at 10m spatial resolution
* Sentinel-2 surface reflectance product (L2A) at 10m spatial resolution
* Landsat-8 surface reflectance product from Collection 2 Level-2
Radiant Earth Foundation designed and generated this dataset with a grant from Schmidt Futures with additional support from NASA ACCESS, Microsoft AI for Earth and in kind technology support from Sinergise.
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
The City Record Online (CROL) is now a fully searchable database of notices published in the City Record newspaper which includes but is not limited to: public hearings and meetings, public auctions and sales, solicitations and awards and official rules proposed and adopted by city agencies.
This is a dataset hosted by the City of New York. The city has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York City using Kaggle and all of the data sources available through the City of New York organization page!
This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.
Cover photo by Jay Clark on Unsplash
Unsplash Images are distributed under a unique Unsplash License.
LandCoverNet is a global annual land cover classification training dataset with labels for the multi-spectral satellite imagery from Sentinel-1, Sentinel-2 and Landsat-8 missions in 2018. LandCoverNet North America contains data across North America, which accounts for ~13% of the global dataset. Each pixel is identified as one of the seven land cover classes based on its annual time series. These classes are water, natural bare ground, artificial bare ground, woody vegetation, cultivated vegetation, (semi) natural vegetation, and permanent snow/ice.
There are a total of 1561 image chips of 256 x 256 pixels in LandCoverNet North America V1.0 spanning 40 tiles. Each image chip contains temporal observations from the following satellite products with an annual class label, all stored in raster format (GeoTIFF files):
* Sentinel-1 ground range distance (GRD) with radiometric calibration and orthorectification at 10m spatial resolution
* Sentinel-2 surface reflectance product (L2A) at 10m spatial resolution
* Landsat-8 surface reflectance product from Collection 2 Level-2
Radiant Earth Foundation designed and generated this dataset with a grant from Schmidt Futures with additional support from NASA ACCESS, Microsoft AI for Earth and in kind technology support from Sinergise.
Natural Earth is a public domain map dataset available at 1:10, 1:50 and 1:110 million scales. Featuring tightly integrated vector and raster data, with Natural Earth you can make a variety of visually pleasing, well-crafted maps with cartography or GIS software.
Natural Earth is a public domain map dataset available at 1:10m, 1:50m, and 1:110 million scales. Featuring tightly integrated vector and raster data, with Natural Earth you can make a variety of visually pleasing, well-crafted maps with cartography or GIS software.
Natural Earth was built through a collaboration of many volunteers and is supported by NACIS (North American Cartographic Information Society).
Natural Earth Vector comes in ESRI shapefile format, the de facto standard for vector geodata. Character encoding is Windows-1252.
Natural Earth Vector includes features corresponding to the following:
Cultural Vector Data Thremes:
Physical Vector Data Themes: