Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30132
Analysis of Urban Population Using Twitter Distribution Data: Case Study of Makassar City, Indonesia

Authors: Yuyun Wabula, B. J. Dewancker

Abstract:

In the past decade, the social networking app has been growing very rapidly. Geolocation data is one of the important features of social media that can attach the user's location coordinate in the real world. This paper proposes the use of geolocation data from the Twitter social media application to gain knowledge about urban dynamics, especially on human mobility behavior. This paper aims to explore the relation between geolocation Twitter with the existence of people in the urban area. Firstly, the study will analyze the spread of people in the particular area, within the city using Twitter social media data. Secondly, we then match and categorize the existing place based on the same individuals visiting. Then, we combine the Twitter data from the tracking result and the questionnaire data to catch the Twitter user profile. To do that, we used the distribution frequency analysis to learn the visitors’ percentage. To validate the hypothesis, we compare it with the local population statistic data and land use mapping released by the city planning department of Makassar local government. The results show that there is the correlation between Twitter geolocation and questionnaire data. Thus, integration the Twitter data and survey data can reveal the profile of the social media users.

Keywords: Geolocation, Twitter, distribution analysis, human mobility.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1126954

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 781

References:


[1] Zheng, Y. U., Capra, L., Wolfson, O., & Yang, H. A. I., “Urban Computing: Concepts Methodologies, and Applications,” 2014
[2] Wang, C., Taylor, J.E, “Process Map for Urban-Human Mobility and Civil Infrastructure Data Collection Using Geosocial Networking Platforms,” 2015.
[3] Baedeker, S.B., Kist, C., Merforth. M., “Next Generation Urban Mobility Plans,” 2014.
[4] An Overview: Land Use and Economic Development in Statewide Transportation Planning, A Report Prepared for the Federal Highway Administration by the Center for Urban Transportation Studies, University of Wisconsin-Milwaukee, May, 1999
[5] Ewing, R., Cervero, R., “A Synthesis. Analyzing Public Policy,” pp.154–177. 2013.
[6] Hanson, S., “Perspectives on the geographic stability and mobility of people in cities,” Proceedings of the National Academy of Sciences of the United States of America, 102, pp. 15301–15306. 2005.
[7] Vilhelmson. B., “Daily mobility and the use of time for different activities. The case of Sweden,” GwJournal, pp. 177-185, 1999.
[8] González, M. C., Hidalgo, C. A., & Barabási, A.-L., “Understanding individual human mobility patterns”. Nature, 453, pp. 779–782, 2008.
[9] Brockmann, D., Hufnagel, L., Geisel, T., “The scaling laws of human travel”. Nature, 462, 2006.
[10] Hasan, S., Schneider, C. M., Ukkusuri, S. V., González, M. C., “Spatiotemporal Patterns of Urban Human Mobility,” Journal of Statistical Physics, 151, pp. 304–318, 2012.
[11] Jain, M. “A next-generation approach to the characterization of a non-model plant transcriptome,” Current Science, 101(11), pp. 1435–1439. 2011.
[12] Vazquez-Prokopec, G. M., Bisanzio, D., Stoddard, S. T., Paz-Soldan, V., Morrison, A. C., Elder, J. P., Kitron, U., “Using GPS Technology to Quantify Human Mobility, Dynamic Contacts and Infectious Disease Dynamics in a Resource-Poor Urban Environment,” PLoS ONE, 8(4), pp. 1–10, 2013.
[13] Frias-Martinez, V., & Frias-Martinez, E., “Spectral clustering for sensing urban land use using Twitter activity,” Engineering Applications of Artificial Intelligence, 35, 237–245, 2014.
[14] Croitoru, A., Wayant, N., Crooks, A., Radzikowski, J., & Stefanidis, A., “Linking cyber and physical spaces through community detection and clustering in social media feeds,” Computers, Environment and Urban Systems, 53, pp.47–64, 2015.
[15] Noulas, A., Scellato, S., Mascolo, C., & Pontil, M., “Exploiting Semantic Annotations for Clustering Geographic Areas and Users in Location-based Social Networks,” The Social Mobile Web, pp.32–35, 2011.
[16] Phithakkitnukoon, S., & Olivier, P., “Sensing Urban Social Geography Using Online Social Networking Data,” The Social Mobile Web, pp.36–39, 2011.
[17] Noulas, A., Scellato, S., Lathia, N., & Mascolo, C., “A random walk around the city: New venue recommendation in location-based social networks, ” Proceedings - 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust and 2012 ASE/IEEE International Conference on Social Computing, SocialCom/PASSAT 2012, pp. 144–153, 2012
[18] Cho, E., Myers, S. A., Leskovec, J., “Friendship and mobility,” 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1082. 2011.
[19] Spatial planning map of Makassar city 2010-2030. (2016, August 8). http://darimakassar.com/rtrw-kota-makassar-2010-2030-2
[20] Twitter. Open twitter streaming api. (2015, August 26). https://dev.twitter.com/docs/streaming-api.
[21] Central Bureau Statistic. Makassar in Number (2014).
[22] Tumasjan, A., Sprenger, T. O., Sandner, P. G., & Welpe, I. M. “Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment,” Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, pp. 178–185, 2007.
[23] Central Bureau Statistic. South Sulawesi in Number (2015).