Estimating the size of urban populations using Landsat images: a case study of Bo, Sierra Leone, West Africa.
Int J Health Geogr
; 18(1): 16, 2019 07 11.
Article
en En
| MEDLINE
| ID: mdl-31296224
ABSTRACT
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.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Población Urbana
/
Características de la Residencia
/
Densidad de Población
/
Imágenes Satelitales
Tipo de estudio:
Prognostic_studies
Aspecto:
Determinantes_sociais_saude
Límite:
Humans
País/Región como asunto:
Africa
Idioma:
En
Revista:
Int J Health Geogr
Asunto de la revista:
EPIDEMIOLOGIA
/
SAUDE PUBLICA
Año:
2019
Tipo del documento:
Article
País de afiliación:
Estados Unidos