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Census-cartography-based gridded population estimates for Mali (2020), version 1.0

Census-cartography-based gridded population estimates for Mali (2020), version 1.0
Census-cartography-based gridded population estimates for Mali (2020), version 1.0
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.
Human population, Sub-national, Spatial dataset
University of Southampton
WorldPop,
e0dc4f20-2c0d-494b-8adf-11cb57608ab8
Institut National de la Statistique du Mali,
53fef827-bad0-4e3e-b83a-8b6a1a7fe9e5
WorldPop,
e0dc4f20-2c0d-494b-8adf-11cb57608ab8
Institut National de la Statistique du Mali,
53fef827-bad0-4e3e-b83a-8b6a1a7fe9e5

WorldPop, and Institut National de la Statistique du Mali, (2022) Census-cartography-based gridded population estimates for Mali (2020), version 1.0. University of Southampton doi:10.5258/SOTON/WP00745 [Dataset]

Record type: Dataset

Abstract

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.

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More information

Published date: 30 December 2022
Keywords: Human population, Sub-national, Spatial dataset

Identifiers

Local EPrints ID: 473110
URI: http://eprints.soton.ac.uk/id/eprint/473110
PURE UUID: 51812c01-ae2f-4dca-a92a-964d1afd2ff7

Catalogue record

Date deposited: 10 Jan 2023 18:13
Last modified: 05 May 2023 20:16

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Contributors

Creator: WorldPop
Creator: Institut National de la Statistique du Mali

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