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Estimating the size of urban populations using Landsat images: a case study of Bo, Sierra Leone, West Africa

BACKGROUND: This is the third paper in a 3-paper series evaluating alternative models for rapidly estimating neighborhood populations using limited survey data, augmented with aerial imagery. METHODS: Bayesian methods were used to sample the large solution space of candidate regression models for es...

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Detalles Bibliográficos
Autores principales: Hillson, Roger, Coates, Austin, Alejandre, Joel D., Jacobsen, Kathryn H., Ansumana, Rashid, Bockarie, Alfred S., Bangura, Umaru, Lamin, Joseph M., Stenger, David A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6625010/
https://www.ncbi.nlm.nih.gov/pubmed/31296224
http://dx.doi.org/10.1186/s12942-019-0180-1
Descripción
Sumario:BACKGROUND: This is the third paper in a 3-paper series evaluating alternative models for rapidly estimating neighborhood populations using limited survey data, augmented with aerial imagery. METHODS: Bayesian methods were used to sample the large solution space of candidate regression models for estimating population density. RESULTS: We accurately estimated the population densities and counts of 20 neighborhoods in the city of Bo, Sierra Leone, using statistical measures derived from Landsat multi-band satellite imagery. The best regression model proposed estimated the latter with an absolute median proportional error of 8.0%, while the total population of the 20 neighborhoods was estimated with an error of less than 1.0%. We also compare our results with those obtained using an empirical Bayes approach. CONCLUSIONS: Our approach provides a rapid and effective method for constructing predictive models for population densities and counts utilizing remote sensing imagery. Our results, including cross-validation analysis, suggest that masking non-urban areas in the Landsat section images prior to computing the candidate covariate regressors should further improve model generality.