Improving Large Area Population Mapping Using Geotweet Densities

Many different methods are used to disaggregate census data and predict population densities to construct finer scale, gridded population data sets. These methods often involve a range of high resolution geospatial covariate datasets on aspects such as urban areas, infrastructure, land cover and top...

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Published inTransactions in GIS Vol. 21; no. 2; pp. 317 - 331
Main Authors Patel, Nirav N., Stevens, Forrest R., Huang, Zhuojie, Gaughan, Andrea E., Elyazar, Iqbal, Tatem, Andrew J.
Format Journal Article
LanguageEnglish
Published England Blackwell Publishing Ltd 01.04.2017
John Wiley and Sons Inc
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Summary:Many different methods are used to disaggregate census data and predict population densities to construct finer scale, gridded population data sets. These methods often involve a range of high resolution geospatial covariate datasets on aspects such as urban areas, infrastructure, land cover and topography; such covariates, however, are not directly indicative of the presence of people. Here we tested the potential of geo‐located tweets from the social media application, Twitter, as a covariate in the production of population maps. The density of geo‐located tweets in 1x1 km grid cells over a 2‐month period across Indonesia, a country with one of the highest Twitter usage rates in the world, was input as a covariate into a previously published random forests‐based census disaggregation method. Comparison of internal measures of accuracy and external assessments between models built with and without the geotweets showed that increases in population mapping accuracy could be obtained using the geotweet densities as a covariate layer. The work highlights the potential for such social media‐derived data in improving our understanding of population distributions and offers promise for more dynamic mapping with such data being continually produced and freely available.
Bibliography:and WorldPop Project
http://www.flowminder.org
Acknowledgements: A.J.T. is supported by funding from the NIH/National Institute of Allergy and Infectious Diseases (U19AI089674), the Bill and Melinda Gates Foundation (OPP1106427, 1032350), a Wellcome Trust Sustaining Health Grant (106866/Z/15/Z), and the Research and Policy for Infectious Disease Dynamics program of the Science and Technology Directorate, Department of Homeland Security, and Fogarty International Center, NIH. This article forms part of the output of the Flowminder Foundation
where a full list of data, funding and acknowledgements may be found. AEG and FRS are supported by funding from a Google Earth Engine Award (OGMB151399).
http://www.worldpop.org
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Acknowledgements: A.J.T. is supported by funding from the NIH/National Institute of Allergy and Infectious Diseases (U19AI089674), the Bill and Melinda Gates Foundation (OPP1106427, 1032350), a Wellcome Trust Sustaining Health Grant (106866/Z/15/Z), and the Research and Policy for Infectious Disease Dynamics program of the Science and Technology Directorate, Department of Homeland Security, and Fogarty International Center, NIH. This article forms part of the output of the Flowminder Foundation (http://www.flowminder.org) and WorldPop Project (http://www.worldpop.org) where a full list of data, funding and acknowledgements may be found. AEG and FRS are supported by funding from a Google Earth Engine Award (OGMB151399).
ISSN:1361-1682
1467-9671
DOI:10.1111/tgis.12214