Abstract
Crowdsourcing platforms and social media produce distinctive geographies of informational content. The production process is enabled and influenced by a variety of socio-economic and demographic factors, shaping the place representation, i.e., the amount and type of information available in an area. In this study, we explore and explain the geographies of Twitter and Wikipedia in Greater London, highlighting the relationships between the crowdsourced data and the local geo-demographic characteristics of the areas where they are located. Through a set of robust regression models on a sample of 1.6M tweets and about 22,000 Wikipedia articles, we identify level of education, presence of people aged 30–44, and property prices as the most important explanatory factors for place representation at the urban scale. To some extent, this confirms the received knowledge of such data being created primarily by relatively wealthy, young, and educated users. However, about half of the variability is left unexplained, suggesting that a broader inclusion of potential factors is necessary.
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Acknowledgements
The demographic data used in this work have been provided by the Greater London Authority and Nomis under the Open Government Licence v2.0. The content analysed in this article was produced by Twitter users and Wikipedia contributors, and obtained through the web services by Twitter, Inc. and Wikimedia Foundation, Inc., under the respective licences. The maps contain data from CDRC LOAC Geodata Pack by the ESRC Consumer Data Research Centre; National Statistics data Crown copyright and database right 2015; Ordnance Survey data Crown copyright and database right 2015.
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Ballatore, A., De Sabbata, S. (2018). Charting the Geographies of Crowdsourced Information in Greater London. In: Mansourian, A., Pilesjö, P., Harrie, L., van Lammeren, R. (eds) Geospatial Technologies for All. AGILE 2018. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-78208-9_8
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