title: Methods for estimating population density in data-limited areas: evaluating regression and tree-based models in Peru.
abstract: Obtaining accurate small area estimates of population is essential for policy and health planning but is often difficult in countries with limited data. In lieu of available population data, small area estimate models draw information from previous time periods or from similar areas. This study focuses on model-based methods for estimating population when no direct samples are available in the area of interest. To explore the efficacy of tree-based models for estimating population density, we compare six different model structures including Random Forest and Bayesian Additive Regression Trees. Results demonstrate that without information from prior time periods, non-parametric tree-based models produced more accurate predictions than did conventional regression methods. Improving estimates of population density in non-sampled areas is important for regions with incomplete census data and has implications for economic, health and development policies.
authors.affiliation: Department of Geography and Environmental Engineering, The Johns Hopkins University, Baltimore, Maryland, United States of America; International Food Policy Research Institute, Washington, D.C., United States of America.
authors.forename: Weston
authors.initials: W
authors.lastname: Anderson
authors.affiliation: Department of Geography and Environmental Engineering, The Johns Hopkins University, Baltimore, Maryland, United States of America.
authors.forename: Seth
authors.initials: S
authors.lastname: Guikema
authors.affiliation: Department of Earth and Planetary Sciences, The Johns Hopkins University, Baltimore, Maryland, United States of America.
authors.forename: Ben
authors.initials: B
authors.lastname: Zaitchik
authors.affiliation: Nicholas School of Environment and Duke Global Health Institute, Duke University, Durham, North Carolina, United States of America.
authors.forename: William
authors.initials: W
authors.lastname: Pan
mesh_terms: D001499:Bayes Theorem; D019352:Censuses; D006801:Humans; D015233:Models, Statistical; D010568:Peru; D011156:Population Density; D012044:Regression Analysis; D014481:United States