Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data
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Open Access
- 17 February 2015
- journal article
- research article
- Published by Public Library of Science (PLoS) in PLOS ONE
- Vol. 10 (2), e0107042
- https://doi.org/10.1371/journal.pone.0107042
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.Keywords
This publication has 30 references indexed in Scilit:
- High Resolution Population Distribution Maps for Southeast Asia in 2010 and 2015PLOS ONE, 2013
- Mapping populations at risk: improving spatial demographic data for infectious disease modeling and metric derivationPopulation Health Metrics, 2012
- Population Distribution, Settlement Patterns and Accessibility across Africa in 2010PLOS ONE, 2012
- Large-scale spatial population databases in infectious disease researchInternational Journal of Health Geographics, 2012
- Assessing the use of global land cover data for guiding large area population distribution modellingGeoJournal, 2010
- Another look at statistical learning theory and regularizationNeural Networks, 2009
- A spatial national health facility database for public health sector planning in Kenya in 2008International Journal of Health Geographics, 2009
- High Resolution Population Maps for Low Income Nations: Combining Land Cover and Census in East AfricaPLOS ONE, 2007
- Determining Global Population Distribution: Methods, Applications and DataAdvances In Parasitology, Vol 64, 2006
- Automated population and dwelling unit estimation from high-resolution satellite images: a GIS approachInternational Journal of Remote Sensing, 1995