Other large-area population data sets rely on ancillary data to spatially weight population density within a given administrative unit. GRUMP differs from GPW by incorporating urban-rural designations in the spatial reallocation of population for each census unit, primarily derived from satellite nightlights, while GPW is a simple redistribution across census unit. Widely-used global data sets that rely on an areal weighting scheme include the freely available Gridded Population of the World (GPW) database, versions 2 and 3, and the Global Rural Urban Mapping Project (GRUMP). Previous efforts to generate gridded population data sets have utilized areal interpolation techniques, which include basic dasymetric approaches often in conjunction with ancillary data or, alternatively, statistical modeling methods. Gridded population data sets can vary substantially in their depiction of population distributions, especially for resource poor countries where recent and detailed census and country-specific geospatial data are often limited. But, to make the most of these data sources, new methodologies are needed to more accurately estimate human population distributions. To satisfy this demand researchers are increasingly turning to remotely sensed data and other geospatial data sets to refine the process of producing high resolution estimates of population density. Demand is increasing for more contemporary, easily-updatable population data as research and decision-making become more complex and operates at sub census-unit scales. Considering an estimated world population increase of 2.3 billion people between 20, with more than 50% of that growth absorbed into urban areas, the ramifications of having accurate population information has never been more important. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist.Īccurate spatial data sets that represent the distributions of human populations are critical in many health, economic, and environmental fields across various temporal and spatial scales. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are creditedįunding: AJT acknowledges funding support from the RAPIDD program of the Science and Technology Directorate, Department of Homeland Security, and the Fogarty International Center, National Institutes of Health, and is also supported by grants from the Bill and Melinda Gates Foundation (#49446 and #1032350). Received: JanuAccepted: AugPublished: February 17, 2015Ĭopyright: © 2015 Stevens et al. Nunes Amaral, Northwestern University, UNITED STATES Finally, we outline how this algorithm will be extended to provide freely-available gridded population data sets for Africa, Asia and Latin America.Ĭitation: Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data.

We discuss the advantages of the new method and increases over the accuracy and flexibility of those previous approaches. 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. This prediction layer is then used as the weighting surface to perform dasymetric redistribution of the census counts at a country level. 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. We present a new semi-automated dasymetric modeling approach that incorporates detailed census and ancillary data in a flexible, “Random Forest” estimation technique. Many methods are used to disaggregate census data and predict population densities for finer scale, gridded population data sets. 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.

Us population density map 2012 article