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Personal activity centres and geosocial data analysis: Combining big data with small data

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conference contribution
posted on 10.05.2017, 10:16 authored by Colin Robertson, Rob Feick, Martin SykoraMartin Sykora, Ketan Shankardass, Krystelle Shaughnessy
Understanding how people move and interact within urban settings has been greatly facilitated by the expansion of personal computing and mobile studies. Geosocial data derived from social media applications have the potential to both document how large segments of urban populations move about and use space, as well as how they interact with their environments. In this paper we examine spatial and temporal clustering of individuals’ geosocial messages as a way to derive personal activity centres for a subset of Twitter users in the City of Toronto. We compare the two types of clustering, and for a subset of users, compare to actual self-reported activity centres. Our analysis reveals that home locations were detected within 500 m for up to 53% of users using simple spatial clustering methods based on a sample of 16 users. Work locations were detected within 500 m for 33% of users. Additionally, we find that the broader pattern of geosocial footprints indicated that 35% of users have only one activity centre, 30% have two activity centres, and 14% have three activity centres. Tweets about environment were more likely sent from locations other than work and home, and when not directed to another user. These findings indicate activity centres defined from Twitter do relate to general spatial activities, but the limited degree of spatial variability on an individual level limits the applications of geosocial footprints for more detailed analyses of movement patterns in the city.

Funding

Social Sciences and Humanities Research Council of Canada

History

School

  • Business and Economics

Department

  • Business

Published in

AGILE Lecture Notes in Geoinformation and Cartography

Pages

145 - 161

Citation

ROBERTSON, C. ... et al, 2017. Personal activity centres and geosocial data analysis: Combining big data with small data. IN: Bregt, A. ... et al (eds). Societal Geo-innovation: Selected papers of the 20th AGILE conference on Geographic Information Science, AGILE 2017, Wageningen, Netherlands, 9-12 May 2017, pp. 145-161.

Publisher

© Springer International Publishing

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2017

Notes

This is a pre-copyedited version of a contribution published in Bregt A., Sarjakoski T., van Lammeren R., Rip F. (eds) Societal Geo-innovation: Selected papers of the 20th AGILE conference on Geographic Information Science published by Springer International Publishing. The definitive authenticated version is available online via http://dx.doi.org/10.1007/978-3-319-56759-4_9.

ISBN

9783319567594;9783319567587

Language

en

Location

Wageningen, Netherlands