Social cartography and satellite-derived building coverage for post-census population estimates in difficult-to-access regions of Colombia

Effective government services rely on accurate population numbers to allocate resources. In Colombia and globally, census enumeration is challenging in remote regions and where armed conflict is occurring. During census preparations, the Colombian National Administrative Department of Statistics con...

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Veröffentlicht in:Population studies Jg. 78; H. 1; S. 3 - 20
Hauptverfasser: Sanchez-Cespedes, Lina Maria, Leasure, Douglas Ryan, Tejedor-Garavito, Natalia, Amaya Cruz, Glenn Harry, Garcia Velez, Gustavo Adolfo, Mendoza, Andryu Enrique, Marín Salazar, Yenny Andrea, Esch, Thomas, Tatem, Andrew J., Ospina Bohórquez, Mariana
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Routledge 01.03.2024
Population Investigation Committee, London School of Economics and Political Science
Schlagworte:
Bayes Theorem
Bayesian analysis
Bayesian statistics
Bias
Buildings
Cartography
Census
Censuses
Colombia
Community
community engagement
Data
Enumeration
GIS
Government services
Humans
Local knowledge
Mathematical models
modelled population estimates
Population
population and housing census
Population statistics
Regions
Remote regions
Remote sensing
Resource allocation
Spatial data
Statistics
Workshops
Colombia
GIS
remote sensing
population and housing census
Bayesian statistics
modelled population estimates
community engagement
ISSN:0032-4728, 1477-4747, 1477-4747
Online-Zugang:Volltext
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Zusammenfassung:Effective government services rely on accurate population numbers to allocate resources. In Colombia and globally, census enumeration is challenging in remote regions and where armed conflict is occurring. During census preparations, the Colombian National Administrative Department of Statistics conducted social cartography workshops, where community representatives estimated numbers of dwellings and people throughout their regions. We repurposed this information, combining it with remotely sensed buildings data and other geospatial data. To estimate building counts and population sizes, we developed hierarchical Bayesian models, trained using nearby full-coverage census enumerations and assessed using 10-fold cross-validation. We compared models to assess the relative contributions of community knowledge, remotely sensed buildings, and their combination to model fit. The Community model was unbiased but imprecise; the Satellite model was more precise but biased; and the Combination model was best for overall accuracy. Results reaffirmed the power of remotely sensed buildings data for population estimation and highlighted the value of incorporating local knowledge.
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ISSN:0032-4728
1477-4747
1477-4747
DOI:10.1080/00324728.2023.2190151