Mali population data

This dataset contains resources transformed from other datasets on HDX. They exist here only in a format modified to support visualization on HDX and may not be as up to date as the source datasets from which they are derived. Source datasets: https://data.hdx.rwlabs.org/dataset/idps-data-by-region-in-mali

Open and free data for assessing the human presence on the planet. The Global Human Settlement Layer (GHSL) project produces global spatial information, evidence-based analytics, and knowledge describing the human presence on the planet. The GHSL relies on the design and implementation of spatial data processing technologies that allow automatic data analytics and information extraction from large amounts of heterogeneous geospatial data including global, fine-scale satellite image data streams, census data, and crowd sourced or volunteered geographic information sources. The JRC, together with the Directorate-General for Regional and Urban Policy (DG REGIO) and Directorate-General for Defence Industry and Space (DG DEFIS) are working towards a regular and operational monitoring of global built-up and population based on the processing of Sentinel Earth Observation data produced by European Copernicus space program. In addition, the EU Agency for the Space Programme (EUSPA) undertakes activities related to user uptake of data, information and services.

Settlements and residential areas: named cities, towns, villages, suburbs, and residential land (`place=*` and `landuse=residential`). Useful for demographic context, settlement-level targeting, and population analysis. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: place, landuse - Part of the country exports on HDX by HOT

Gridded population density of Mali for the year 2020 at 1km resolution, adjusted to match UN national population estimates. Each grid cell shows the estimated number of people per square kilometre. Use this layer for settlement pattern analysis, infrastructure planning, service delivery optimisation, and identifying high and low density areas.

This dataset contains administrative polygons grouped by country (admin-0) with the following subdivisions according to Who's On First placetypes: - macroregion (admin-1 including region) - region (admin-2 including state, province, department, governorate) - macrocounty (admin-3 including arrondissement) - county (admin-4 including prefecture, sub-prefecture, regency, canton, commune) - localadmin (admin-5 including municipality, local government area, unitary authority, commune, suburb) The dataset also contains human settlement points and polygons for: - localities (city, town, and village) - neighbourhoods (borough, macrohood, neighbourhood, microhood) The dataset covers activities carried out by Who's On First (WOF) since 2015. Global administrative boundaries and human settlements are aggregated and standardized from hundreds of sources and available with an open CC-BY license. Who's On First data is updated on an as-need basis for individual places with annual sprints focused on improving specific countries or placetypes. Please refer to the README.md file for complete data source metadata. Refer to our blog post for explanation of field names. Data corrections can be proposed using Write Field, an web app for making quick data edits. You’ll need a Github.com account to login and propose edits, which are then reviewed by the Who's On First community using the Github pull request process. Approved changes are available for download within 24-hours. Please contact WOF admin about bulk edits.

These data consist of modelled gridded population estimates produced at a spatial resolution of approximately 100m across Mali. The estimates comprise a combination of total population counts at enumeration area level collected by the census cartography team of the Mali Statistics Office and modelled population counts created using a Bayesian statistical model for areas that could not be covered by the cartography team because of security issues. The main input data for the model are the cartography data collected in the safe part of the country in 2019-2020 (628 out of 714 Communes –administrative level 3–, that is 87% of the country territory). Other essential input data include metrics derived from building footprints, which were automatically delineated by Ecopia.AI in 2021 using satellite imagery collected by Maxar Technologies between 2010 and 2021. The modelled population estimates represent the period of the census cartography, but their consistency may be impacted by the accuracy of the building footprints. These data were produced by the WorldPop Research Group at the University of Southampton as part of the GRID3 Project, GRID3 (Geo-Referenced Infrastructure and Demographic Data for Development) programme funded by the Bill and Melinda Gates Foundation (BMGF) and the United Kingdom’s Foreign, Commonwealth & Development Office (INV 009579, formerly OPP 1182425). The study was approved by the Faculty Ethics Committee of the University of Southampton (ERGO II 64957). The project was led by the Center for International Earth Science Information Network (CIESIN) at Columbia University, in collaboration with the WorldPop Research Group at the University of Southampton, the United Nations Fund for Population (UNFPA) and the Malian Institut National de la Statistique (INSTAT). The production of these data was led by Edith Darin (WorldPop) with support from Matthias Kuépié and Jean Wakam (UNFPA), Abdoul Karim Diawara, Assa Gakou and Siaka Cissé (Institut National de la Statistique), and Attila N Lazar (WorldPop) and Andrew J Tatem (WorldPop). The authors acknowledge the support of their respective institutions in the completion of this work. **Recommended citations** WorldPop and Institut National de la Statistique du Mali. 2022. *Census-cartography-based gridded population estimates for Mali (2020), version 1.0.* WorldPop, University of Southampton. doi:10.5258/SOTON/WP00745 **License** These data may be redistributed following the terms of a Creative Commons Share-Alike Attribution 4.0 International (CC BY SA 4.0) license

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