Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30135
Quantifying Mobility of Urban Inhabitant Based on Social Media Data

Authors: Yuyun, Fritz Akhmad Nuzir, Bart Julien Dewancker

Abstract:

Check-in locations on social media provide information about an individual’s location. The millions of units of data generated from these sites provide knowledge for human activity. In this research, we used a geolocation service and users’ texts posted on Twitter social media to analyze human mobility. Our research will answer the questions; what are the movement patterns of a citizen? And, how far do people travel in the city? We explore the people trajectory of 201,118 check-ins and 22,318 users over a period of one month in Makassar city, Indonesia. To accommodate individual mobility, the authors only analyze the users with check-in activity greater than 30 times. We used sampling method with a systematic sampling approach to assign the research sample. The study found that the individual movement shows a high degree of regularity and intensity in certain places. The other finding found that the average distance an urban inhabitant can travel per day is as far as 9.6 km.

Keywords: Mobility, check-in, distance, Twitter.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1316837

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 290

References:


[1] M. C. Gonzalez, C. A. Hidalgo, A. L. Barabasi, “Understanding individual human mobility patterns Nature”, Nature, vol. 453, pp 779-782. June 2008.
[2] A. Noulas, S. Salvatore, R. Lambiotte, M. Pontil,C. Mascolo, “A tale of many cities: universal patterns in human urban mobility,” PloS one, vol. 7: e37027. May 2012.
[3] C. Song, T. Koren, P. Wang, A. L. Barabasi, “Modelling the scaling properties of human mobility,” Nature Physics, vol. 6(10), pp. 818-823. September 2010.
[4] R. Ewing, R. Cervero, “Travel and the built environment: A synthesis”, Transportation research record, No. 01-3515, pp.87–113.
[5] B. Vilhelmson, “Daily mobility and the use of time for different activities”, Geo-Journal, vol. 48, no. 3, pp. 177–185. 1999.
[6] S. Hanson, “Perspectives on the geographic stability and mobility of people in cities”, Proceedings of National Academy of Science of the USA, vol. 102, no. 43, pp. 15301-15306. October 2005.
[7] E. Thomas, I. Serwicka, P. Swinney, “Urban demographics where people live and work”, DAC beachroft. November 2015.
[8] C. Boldrini, A. Passarella, “HCMM: Modelling spatial and temporal properties of human mobility driven by users’ social relationships”, Computer Communications, vol. 33, no.9, pp. 1056–1074. June 2010.
[9] C. Song, Z. Qu, N. Blumm, A. L Barabasi, “Limits of predictability in human Mobility”, Science 327: 1018–1021. 2010.
[10] X. Cao, G. Cong, C.S. Jensen, “Mining Significant Semantic Locations from GPS Data,” Proceedings of the VLDB Endowment, vol. 3, no. 1, pp. 1009-1020, Sept. 2010.
[11] Twitter. Open twitter streaming api. (2015, August 26). https://dev.twitter.com/docs/streaming-api.