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Geospatial Dataset Catalog
Kontur has a variety of geospatial datasets that cover both natural and human landscape.
Our Geocint data pipeline converts any type of data into hexagonal datasets — for you, this means we can quickly integrate the specific data you need for analysis or solutions, without delays.
Kontur datasets are represented by H3 hexagons with 400m resolution. The reason why we use H3 grid instead of the common square grid is that unlike squares, hexagons have equal distances between a hexagon centerpoint and the centers of neighboring cells. This property greatly simplifies performing analysis and smoothing over gradients.
You can leverage our datasets in the following ways
Geoplatform
For users who prefer a visual interface and interactive work with data, try Atlas.
$100..1000/month
API
For developers and companies integrating geodata into their systems.
$100/month
Embedded Map
For companies that need to quickly embed geodata into webpages or internal systems.
$100/month
File dump
For those who prefer to receive ready-to-use files (.gpkg, .shp, .geojson, .csv, and others).
$1000/download
Internationally recognized and reliable sources
We have collected datasets from leading vendors, unified, hexagonalized and standardized them to offer you the most accurate, up-to-date and reliable solutions based on this data.
- © OpenStreetMap contributors https://www.openstreetmap.org/copyright
- © INFORM Initiative https://www.inform-index.org
- Copyright 2024 Foursquare Labs, Inc. All rights reserved.
- © Kontur https://kontur.io
- Copernicus Global Land Service: Land Cover 100 m: Marcel Buchhorn, Bruno Smets, Luc Bertels, Bert De Roo, Myroslava Lesiv, Nandin-Erdene Tsendbazar, Steffen Fritz. (2020). Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019: Globe (Version V3.0.1). Zenodo. http://doi.org/10.5281/zenodo.3939050
- © 2021 Probable Futures, a Project of the SouthCoast Community Foundation. https://probablefutures.org — CC BY 4.0
- Events data from Kontur Event Feed https://www.kontur.io/portfolio/event-feed
- © United States Census Bureau. 2019 5-Year American Community Survey (ACS). https://www.census.gov/en.html
- © Kontur Boundaries https://data.humdata.org/dataset/kontur-boundaries
- © 2012–2024 Internal Displacement Monitoring Centre (IDMC)
- Facebook Connectivity Lab and CIESIN – Columbia University. (2016). High Resolution Settlement Layer (HRSL). Source imagery © 2016 DigitalGlobe. https://dataforgood.fb.com/tools/population-density-maps
- Dataset: Schiavina M., Freire S., Carioli A., MacManus K. (2023). GHS-POP R2023A – GHS population grid multitemporal (1975–2030). European Commission, Joint Research Centre (JRC). PID: http://data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe, DOI: 10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE
- Concept & Methodology: Freire S., MacManus K., Pesaresi M., Doxsey-Whitfield E., Mills J. (2016). Development of new open and free multi-temporal global population grids at 250 m resolution. AGILE 2016.
- Microsoft Buildings: Australia, Canada, Tanzania, Uganda, USA. Licensed by Microsoft under the Open Data Commons Open Database License (ODbL).
- NZ Building Outlines data sourced from the LINZ Data Service – https://data.linz.govt.nz
- Geoalert Urban Mapping: Chechnya, Moscow region, Tyva, Tashkent, Bukhara, Samarkand, Navoi, Chirchiq – https://github.com/Geoalert/urban-mapping
- Data from General Bathymetric Chart of the Oceans – https://www.gebco.net
- Copyright © 2022 WorldClim https://www.worldclim.org/data/index.html
- High Resolution Canopy Height Maps by WRI and Meta. Accessed on 20.05.2024 from https://registry.opendata.aws/dataforgood-fb-forests
- Source imagery for CHM © 2016 Maxar.
- Wikipedia content from https://en.wikipedia.org/wiki/Naturalization, licensed under CC BY-SA 4.0
- All imagery is publicly licensed and made available through the Humanitarian OpenStreetMap Team’s Open Imagery Network (OIN) Node. CC BY 4.0.
- © 2024 The World Bank Group. Doing Business project https://www.doingbusiness.org
- © 2024 United Nations Development Programme https://hdr.undp.org/data-center/human-development-index
- © Data from JHU CSSE COVID-19 Dataset
- © Data from Sentinel-2 L2A 120m Mosaic, CC BY 4.0 https://forum.sentinel-hub.com/c/aws-sentinel
- NRT VIIRS 375 m Active Fire product VJ114IMGTDL_NRT. https://earthdata.nasa.gov/firms — DOI: 10.5067/FIRMS/VIIRS/VJ114IMGT_NRT.002
- NRT VIIRS 375 m Active Fire product VNP14IMGT. https://earthdata.nasa.gov/firms — DOI: 10.5067/FIRMS/VIIRS/VNP14IMGT_NRT.002
- MODIS Collection 6 NRT Hotspot / Active Fire Detections MCD14DL. https://earthdata.nasa.gov/firms — DOI: 10.5067/FIRMS/MODIS/MCD14DL.NRT.006
- MODIS Collection 6 NRT Hotspot / Active Fire Detections MCD14ML. https://earthdata.nasa.gov/firms — DOI: 10.5067/FIRMS/MODIS/MCD14ML
- © 2020 The World Bank Group — CC BY 4.0
- © The Institute for Economics and Peace Limited 2022 https://www.visionofhumanity.org
- © 2019 Facebook, Inc. and its affiliates https://github.com/facebookmicrosites/Open-Mapping-At-Facebook/blob/main/LICENSE.md
- © 9999 Facebook, Inc. and its affiliates https://dataforgood.facebook.com/dfg/tools/electrical-distribution-grid-maps
- Concept of areas © Brahmagupta, René Descartes
- Copyright © 2022 MapSwipe https://mapswipe.org/en/privacy.html
- Copyright © 2022 The World Bank https://globalsolaratlas.info/support/terms-of-use
- Earth Observation Group © 2021 https://eogdata.mines.edu/products/vnl/#reference
- C. D. Elvidge, K. E. Baugh, M. Zhizhin, F.-C. Hsu. (2013). “Why VIIRS data are superior to DMSP for mapping nighttime lights.” Asia-Pacific Advanced Network, 35: 62.
- C. D. Elvidge, M. Zhizhin, T. Ghosh, F-C. Hsu. “Annual time series of global VIIRS nighttime lights derived from monthly averages: 2012 to 2019.” Remote Sensing (In press).