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Improving large area population mapping using geotweet densities

Improving large area population mapping using geotweet densities
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 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
1361-1682
1-15
Patel, Nirav N.
ffe57351-45aa-4f43-a329-fd8f6cdf24db
Stevens, Forrest R.
7c96c2ef-edac-41a1-be26-c4bc5b3256a6
Huang, Zhuojie
07e288b7-51b3-414a-82b7-28d83b114be6
Gaughan, Andrea E.
395221c6-1091-4657-af7e-bd6cb93dbaf9
Elyazar, Iqbal
94380dc7-a09d-43a6-8449-53488450a0dc
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Patel, Nirav N.
ffe57351-45aa-4f43-a329-fd8f6cdf24db
Stevens, Forrest R.
7c96c2ef-edac-41a1-be26-c4bc5b3256a6
Huang, Zhuojie
07e288b7-51b3-414a-82b7-28d83b114be6
Gaughan, Andrea E.
395221c6-1091-4657-af7e-bd6cb93dbaf9
Elyazar, Iqbal
94380dc7-a09d-43a6-8449-53488450a0dc
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e

Patel, Nirav N., Stevens, Forrest R., Huang, Zhuojie, Gaughan, Andrea E., Elyazar, Iqbal and Tatem, Andrew (2016) Improving large area population mapping using geotweet densities. Transactions in GIS, 1-15. (doi:10.1111/tgis.12214).

Record type: Article

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 continually produced and freely available

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More information

Accepted/In Press date: 1 January 2016
e-pub ahead of print date: 30 June 2016
Published date: 30 June 2016
Organisations: WorldPop, Geography & Environment, Population, Health & Wellbeing (PHeW)

Identifiers

Local EPrints ID: 397663
URI: https://eprints.soton.ac.uk/id/eprint/397663
ISSN: 1361-1682
PURE UUID: 251f66ae-c170-44bf-bb1f-7f6f576f4e18
ORCID for Andrew Tatem: ORCID iD orcid.org/0000-0002-7270-941X

Catalogue record

Date deposited: 04 Jul 2016 12:59
Last modified: 10 Dec 2019 01:37

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