Improving Large Area Population Mapping Using Geotweet Densities.

Transactions in GIS : TG
Nirav N PatelAndrew J Tatem

Abstract

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 contin...Continue Reading

References

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Citations

Feb 1, 2017·Scientific Data·Christopher T LloydAndrew J Tatem
Aug 15, 2019·Journal of the American Medical Informatics Association : JAMIA·Emilie BruzeliusPrabhjot Singh
Sep 17, 2020·Nature Communications·Filipe Batista E SilvaCarlo Lavalle
Jul 10, 2019·Environmental Health : a Global Access Science Source·Drew R MichanowiczAaron S Bernstein
Mar 21, 2018·Proceedings of the National Academy of Sciences of the United States of America·N A WardropA J Tatem
Jun 20, 2019·Journal of Urban Health : Bulletin of the New York Academy of Medicine·Dana R ThomsonHelen Elsey
Jan 8, 2019·Environment International·Bin ZouShenxin Li
Nov 24, 2020··Xiang Liu, Philo Pöllmann

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Methods Mentioned

BETA
MDA

Software Mentioned

Random Forest
Google Earth Engine
ArcGIS Desktop
Elastic MapReduce
WorldPop
WorldClim
Open Map
Shutterfly
VMAP0
NGA Vector Map

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