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

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Leasure, Douglas;Tatem, Andrew;Bondarenko, Maksym

Description

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.

Citations (3)

Mentions (0)

Metrics

Dataset Index

1.6

FAIR Score

77%

Citations

3

Mentions

0

Metrics Over Time

Publication Details

DOI

Publisher

University of Southampton

Assigned Domain

Subfield

Management Science and Operations Research

Field

Decision Sciences

Domain

Social Sciences

Confidence Score

85%

Source

Open Alex

Keywords

Human population, Bayesian statistics, bensus microdata, building footprints

Normalization Factors

FT

13.46

CTw

1.00

MTw

1.00