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.

Model-based projections of Zika virus infections in childbearing women in the Americas

“…To estimate the annual numbers of pregnancies per 1 km × 1 km grid cell in 2015, methods developed by the WorldPop project (www.worldpop.org) 25,31 were adapted for the Americas region. High-resolution estimates of population counts per 100 m × 100 m grid cell for 2015 were recently constructed for Latin American, Asian and African countries 14,32 . With consistent subnational data on sex and age structures, as well as subnational age-specific fertility rate data across the Americas currently unavailable for fully replicating the approaches of Tatem and colleagues 31 , national-level adjustments were made to construct pregnancy and birth counts.…”

Dynamic population mapping using mobile phone data

“…The ability of the MP data-based approach to accurately downscale census population data was compared with that of an existing method used to downscale census data through remote sensing and other geospatial data (19), hereafter called the "remote sensing" method or RS (SI Appendix, section A.1). Fig.…”

“…To assess the accuracy and precision of the MP method described above, we produced a nighttime population map based on a recently developed dasymetric modeling approach that incorporates a wide range of remotely sensed and geospatial data (called the RS method in this paper; SI Appendix, section A.1). Ancillary data layers were used, including the Corine Land Cover 2006 dataset (69), OpenStreetMapderived infrastructure (70), satellite nightlights (71), and slope (72), among others (19). The method combines data in a flexible "Random Forest" model to generate gridded predictions of population density at ∼100 m spatial resolution (SI Appendix, section A.1) (19).…”

“…Ancillary data layers were used, including the Corine Land Cover 2006 dataset (69), OpenStreetMapderived infrastructure (70), satellite nightlights (71), and slope (72), among others (19). The method combines data in a flexible "Random Forest" model to generate gridded predictions of population density at ∼100 m spatial resolution (SI Appendix, section A.1) (19). Analyses have shown that this algorithm produces improved mapping accuracies compared with previous approaches (19).…”

“…Initial efforts to downscale these data used simple areal weighting methods (10,17) or dasymetric modeling approaches (13)(14)(15), which use ancillary layers to redistribute population counts within administrative units (18). Modeling techniques that spatially downscale population numbers into gridded datasets continue to be refined, with basic dasymetric models increasing in sophistication, incorporating multiscale remotely sensed and geospatial data and making improvements in the type of statistical algorithms used in the modeling process (19)(20)(21). These detailed population databases have proven crucial for studies reliant on information about human population distributions, typically for calculating populations at risk for human or natural disasters (22)(23)(24), to assess vulnerabilities (7,25), or to derive health and development indicators (3,5,26,27).…”