title: Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data.
abstract: High resolution, contemporary data on human population distributions are vital for measuring impacts of population growth, monitoring human-environment interactions and for planning and policy development. Many methods are used to disaggregate census data and predict population densities for finer scale, gridded population data sets. We present a new semi-automated dasymetric modeling approach that incorporates detailed census and ancillary data in a flexible, "Random Forest" estimation technique. We outline the combination of widely available, remotely-sensed and geospatial data that contribute to the modeled dasymetric weights and then use the Random Forest model to generate a gridded prediction of population density at ~100 m spatial resolution. This prediction layer is then used as the weighting surface to perform dasymetric redistribution of the census counts at a country level. As a case study we compare the new algorithm and its products for three countries (Vietnam, Cambodia, and Kenya) with other common gridded population data production methodologies. We discuss the advantages of the new method and increases over the accuracy and flexibility of those previous approaches. Finally, we outline how this algorithm will be extended to provide freely-available gridded population data sets for Africa, Asia and Latin America.
authors.affiliation: Department of Geography and Geosciences, University of Louisville, Louisville, Kentucky, United States of America.
authors.forename: Forrest R
authors.initials: FR
authors.lastname: Stevens
authors.affiliation: Department of Geography and Geosciences, University of Louisville, Louisville, Kentucky, United States of America.
authors.forename: Andrea E
authors.initials: AE
authors.lastname: Gaughan
authors.affiliation: Fonds National de la Recherche Scientifique (F.R.S.-FNRS), Rue d'Egmont 5, B-1000 Brussels, Belgium; Biological Control and Spatial Ecology, Université Libre de Bruxelles, CP 160/12, Avenue FD Roosevelt 50, B-1050 Brussels, Belgium.
authors.forename: Catherine
authors.initials: C
authors.lastname: Linard
authors.affiliation: Department of Geography and Environment, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom; Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, United States of America.
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Tatem
mesh_terms: D002160:Cambodia; D019352:Censuses; D066264:Datasets as Topic; D005843:Geography; D006801:Humans; D007630:Kenya; D015233:Models, Statistical; D011156:Population Density; D015203:Reproducibility of Results; D062206:Spatial Analysis; D014744:Vietnam