Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data

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…

• UD
  U.S. Department of Homeland Security
• BA
  Bill and Melinda Gates Foundation

  Award: 1032350
• NI
  National Institutes of Health
• SA
  Science and Technology Directorate
• FI
  Fogarty International Center