Work

Methods for Estimating Population Density in Data-Limited Areas: Evaluating Regression and Tree-Based Models in Peru

Year: 2014

Type: article

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.

Source: PLoS ONE

DOI: 10.1371/journal.pone.0100037

Authors: Weston Anderson, Seth Guikema, Ben Zaitchik, William Pan

Institutions: Johns Hopkins University, International Food Policy Research Institute, Duke University

Language: English

Topic: Census and Population Estimation

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Life on land

Open Access status: gold

Estimated APC paid: $2,382