These datasets vary in their modelling techniques and the types of input data used for their construction. In addition, the LandScan Global Population database is updated annually, but has some access restrictions, , and the United Nation Environment Programme (UNEP) has compiled gridded datasets for Latin America, Africa, and Asia, ,, while the AfriPop project provides freely-available gridded population data for Africa,. Current global gridded population datasets that are freely available include the Gridded Population of the World (GPW) database, versions 2 and 3, and the Global Rural Urban Mapping Project (GRUMP). Since the 1990s there has been increasing interest in creating spatially-explicit, large-area gridded population distribution datasets, , to support applications such as disease burden estimation, epidemiological modelling, climate change and human health adaptive strategies, disaster management, accessibility modelling, transport and city planning, poverty mapping and environmental impact assessment, ,,. While high-income countries often have extensive mapping resources and expertise at their disposal to create accurate and regularly-updated spatial population databases, across the lower income regions of the world relevant data are often either lacking or are of poor quality. To measure the impact of this population growth there is a need for accurate, spatially-explicit, high resolution maps that correctly identify population distributions through time. The effects of such rapid demographic growth are well documented, influencing the economies, environment and health of nations. The greatest concentration in growth is set to occur in urban areas, disproportionately impacting Asia where half of the population is expected to be living in urban areas by 2020. The global human population is projected to increase from 7 billion to over 9 billion between 20, with much of this growth concentrated in low income countries. ![]() 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. 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 credited.įunding: 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: SeptemAccepted: JanuPublished: February 13, 2013Ĭopyright: © 2013 Gaughan et al. The 20 datasets produced are freely available as a product of the AsiaPop Project and can be downloaded from: Citation: Gaughan AE, Stevens FR, Linard C, Jia P, Tatem AJ (2013) High Resolution Population Distribution Maps for Southeast Asia in 20. Using existing data, it is possible to produce detailed, contemporary and easily updatable population distribution datasets for Southeast Asia. Results indicate mapping accuracies are consistently higher when incorporating land cover and settlement information into the AsiaPop modelling process. These products were compared with those from two other methods used to construct commonly used global population datasets. Landsat-derived settlement maps and land cover information were combined with ancillary datasets on infrastructure to model population distributions for 20. Here we outline approaches to construct a database of GIS-linked circa 2010 census data and methods used to construct fine-scale (∼100 meters spatial resolution) population distribution datasets for each country in the Southeast Asia region. The Southeast Asia region has undergone rapid urbanization and population growth over the past decade, yet existing spatial population distribution datasets covering the region are based principally on population count data from censuses circa 2000, with often insufficient spatial resolution or input data to map settlements precisely. ![]() Spatially accurate, contemporary data on human population distributions are vitally important to many applied and theoretical researchers.
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