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

SUBMITTER: Stevens FR 

PROVIDER: S-EPMC4331277 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature