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A Bayesian approach to produce 100 m gridded population estimates using census microdata and recent building footprints.

A Bayesian approach to produce 100 m gridded population estimates using census microdata and recent building footprints.
A Bayesian approach to produce 100 m gridded population estimates using census microdata and recent building footprints.
This report describes a novel Bayesian statistical method that combines recent building footprints from Ecopia.AI and Maxar Technologies with publicly-available census microdata from IPUMS International to produce 100 m gridded population estimates for Ghana. The model was used to estimate total populations, populations within specific age-sex groups, number of households, people per household, and households per building. Bayesian estimates of uncertainty are provided with all parameter estimates. Supplementary files are included with input data and statistical model code in the Stan programming language. This method is generalizable to additional countries where IPUMS data and building footprints are available.
Human population, Bayesian statistics, bensus microdata, building footprints
University of Southampton
Leasure, Douglas
c025de11-3c61-45b0-9b19-68d1d37959cd
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Bondarenko, Maksym
1cbea387-2a42-4061-9713-bbfdf4d11226
Leasure, Douglas
c025de11-3c61-45b0-9b19-68d1d37959cd
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Bondarenko, Maksym
1cbea387-2a42-4061-9713-bbfdf4d11226

Leasure, Douglas and Tatem, Andrew (2020) A Bayesian approach to produce 100 m gridded population estimates using census microdata and recent building footprints. University of Southampton doi:10.5258/SOTON/WP00686 [Dataset]

Record type: Dataset

Abstract

This report describes a novel Bayesian statistical method that combines recent building footprints from Ecopia.AI and Maxar Technologies with publicly-available census microdata from IPUMS International to produce 100 m gridded population estimates for Ghana. The model was used to estimate total populations, populations within specific age-sex groups, number of households, people per household, and households per building. Bayesian estimates of uncertainty are provided with all parameter estimates. Supplementary files are included with input data and statistical model code in the Stan programming language. This method is generalizable to additional countries where IPUMS data and building footprints are available.

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More information

Published date: 17 November 2020
Keywords: Human population, Bayesian statistics, bensus microdata, building footprints

Identifiers

Local EPrints ID: 445047
URI: http://eprints.soton.ac.uk/id/eprint/445047
PURE UUID: b9a25552-e05a-4ec0-ba41-b2fd5fd98c37
ORCID for Andrew Tatem: ORCID iD orcid.org/0000-0002-7270-941X
ORCID for Maksym Bondarenko: ORCID iD orcid.org/0000-0003-4958-6551

Catalogue record

Date deposited: 18 Nov 2020 17:31
Last modified: 27 Feb 2021 02:43

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