Beyond geotagged tweets: exploring the geolocalisation of tweets for transportation applications

Gonzalez Paule, J. D., Sun, Y. and Thakuriah, P. (2019) Beyond geotagged tweets: exploring the geolocalisation of tweets for transportation applications. In: Ukkusuri, S. V. and Yang, C. (eds.) Transportation Analytics in the Era of Big Data. Springer. ISBN 9783319758619 (doi: 10.1007/978-3-319-75862-6_1)

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Researchers in multiple disciplines have used Twitter to study various mobility patterns and “live” aspects of cities. In the field of transportation planning, one major area of interest has been to use Twitter data to infer movement patterns and origins and destinations of trip-makers. In the area of transportation operations, researchers have been interested in automated incident detection or event detection. Because the number of geotagged tweets pinpointing the location of the user at the time of tweeting tends to be sparse for transportation applications, there is a need to consider expanding and geolocalising the sample of non-geotagged tweets that can be associated with locations.We call this process “geolocalisation”. While geolocalisation is an active area of research associated with the geospatial semanticWeb and Geographic Information Retrieval, much of the work has focused on geolocalisation of users, or on geolocalisation of tweeting activity to fairly coarse geographical levels, whereas our work relates to street-level or even building-level geolocalisation. We will consider two different approaches to geolocalisation that make use of Points of Interest databases and a second information retrieval-based approach that trains on geotagged tweets. Our objective is to make a comprehensive assessment of the differences in spatial and content coverage between non-geotagged tweets geolocalised using different approaches compared to using geotagged tweets alone. We find that using geolocalised tweets allow discovery of a larger number of incidents and socioeconomic patterns that are not evident from using geotagged data alone, including activity throughout the metropolitan area, including deprived “Environmental Justice” (EJ) areas where the degree of social media activity detected is usually low. Conclusions are drawn on the relative usefulness of the alternative approaches.

Item Type:Book Sections
Additional Information:Electronic ISBN: 9783319758626.
Glasgow Author(s) Enlighten ID:Thakuriah, Professor Piyushimita and Sun, Mr Yeran and Gonzalez Paule, Jorge
Authors: Gonzalez Paule, J. D., Sun, Y., and Thakuriah, P.
College/School:College of Social Sciences > School of Social and Political Sciences > Urban Studies
Research Group:Urban Big Data Centre

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
651871Social Transport with Urban Big DataPiyushimita ThakuriahEuropean Commission (EC)632075SPS - URBAN STUDIES