Hillson, Roger2026-10-192019-07-11Hillson, R., Coates, A., Alejandre, J. D., Jacobsen, K. H., Ansumana, R., Bockarie, A. S., Bangura, U., Lamin, J. M., & Stenger, D. A. (2019). Estimating the size of urban populations using Landsat images: a case study of Bo, Sierra Leone, West Africa. International Journal of Health Geographics, 18(1), 16. https://doi.org/10.1186/s12942-019-0180-110.1186/s12942-019-0180-1https://doi.org/10.1186/s12942-019-0180-1https://dspace.njala.edu.sl/handle/20.500.144402/1015BACKGROUND: 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.enCC BYRemote sensingArticle