Geolocalized Tweets for assessing daily mobility: methodology to analyse and detect homelocation in the urban area of Valencia

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Published: Dec 11, 2018
DOI: https://doi.org/10.21138/bage.2464

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Carmen Zornoza Gallego
Instituto de Desarrollo Local Departamento de Geografía Universidad de Valencia.
http://orcid.org/0000-0001-9680-4983
Julia Salom Carrasco
Instituto de Desarrollo Local Departamento de Geografía Universidad de Valencia.
http://orcid.org/0000-0001-6436-9761

Abstract

Geolocalized data from social network Twitter is analyzed with the aim of studying its possible use in a daily mobility pattern investigation. The area for the practical application is Valencia’s urban area, Spain. Based on the previous analysis, a methodological proposal is created to the use of data, focused on the detection of the user’s home location, a core information in a mobility study. The proper adjustment of the results with the sources of evidences validates the methodology and shows that the possibilities of this information are vast.



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How to Cite
Zornoza Gallego, C., & Salom Carrasco, J. (2018). Geolocalized Tweets for assessing daily mobility: methodology to analyse and detect homelocation in the urban area of Valencia. Boletín De La Asociación Española De Geografía, (79). https://doi.org/10.21138/bage.2464

References

Béjar, J., Álvarez, S., García, D., Gómez, I., Oliva, L., Tejeda, A., & Vázquez-Salceda, J. (2016). Discovery of spatio-temporal patterns from location-based social networks. Journal of Experimental & Theoretical Artificial Intelligence, 28(1–2), 313–329. https://doi.org/10.1080/0952813X.2015.1024492

Bojic, I., Massaro, E., Belyi, A., Sobolevsky, S., & Ratti, C. (2015). Choosing the Right Home Location Definition Method for the Given Dataset (pp. 194–208). In T.Y. Liu, C. Scollon & W. Zhu (Eds.), Social Informatics. SocInfo 2015. Lecture Notes in Computer Science. Springer. https://doi.org/10.1007/978-3-319-27433-1_14

Frias-Martinez, V., & Frias-Martinez, E. (2014). Spectral clustering for sensing urban land use using Twitter activity. Engineering Applications of Artificial Intelligence, 35, 237–245. https://doi.org/10.1016/j.engappai.2014.06.019

Gabrielli, L., Rinzivillo, S., Ronzano, F., & Villatoro, D. (2014). From Tweets to Semantic Trajectories: Mining Anomalous Urban Mobility Patterns. In Jordi Nin & Daniel Villatoro (Eds.), Citizen in Sensor Networks (pp. 26–35). Switzerland: Springer International Publishing. https://doi.org/10.1007/978-3-319-04178-0_3

García-Palomares, J. C., Gutiérrez, J., & Mínguez, C. (2015). Identification of tourist hot spots based on social networks: A comparative analysis of European metropolises using photo-sharing services and GIS. Applied Geography, 63, 408–417. https://doi.org/10.1016/j.apgeog.2015.08.002

Goodchild, M. (2007). Citiziens as sensors: the word of volunteered geography. GeoJournal, 69, 211–221. https://doi.org/10.1007/s10708-007-9111-y

Gonzalez, M. C., Hidalgo, C. A., & Barabasi, A. L. (2008). Understanding individual human mobility patterns. Nature, 453(7196), 779–782. https://doi.org/10.1038/nature06958

Gutiérrez-Puebla, J., García-Palomares, J. C., & Salas-Olmedo, M. H. (2016). Big (Geo)Data in Social Sciences:Challenges and Opportunities. Revista de estudios andaluces, 33, 1–23. http://dx.doi.org/10.12795/rea.2016.i33.01

Hasan, S., Zhan, X., & Ukkusuri, S. V. (2013, August). Understanding urban human activity and mobility patterns using large-scale location-based data from online social media. In Proceedings of the 2nd ACM SIGKDD international workshop on urban computing (pp. 1–8). Chicago. https://doi.org/10.1145/2505821.2505823

Huang, W., Li, S., Liu, X., & Ban, Y. (2015). Predicting human mobility with activity changes. International Journal of Geographical Information Science, 29(9), 1569–1587. https://doi.org/10.1080/13658816.2015.1033421

Jurdak, R., Zhao, K., Liu, J., AbouJaoude, M., Cameron, M., & Newth, D. (2015). Understanding human mobility from Twitter. PloS one, 10(7), e0131469. https://doi.org/10.1371/journal.pone.0131469

Kwan, M. P. (1999). Gender, the home-work link, and space-time patterns of nonemployment activities. Economic geography, 75(4), 370–394. https://doi.org/10.1111/j.1944-8287.1999.tb00126.x

Masquenegocio. (2016, January). Twitter users in Spain [PDF report]. Retrieved from http://www.masquenegocio.com/wp-content/uploads/2016/01/Twitter-en-Espan%CC%83a.pdf

Song, C., Qu, Z., Blumm, N., & Barabási, A. L. (2010). Limits of predictability in human mobility. Science, 327(5968), 1018–1021. https://doi.org/10.1126/science.1177170

Li, S., Dragicevic, S., Castro, F., Sesterd, M., Wintere,S., Coltekin, A., … Chengi, T. (2016). Geospatial big data handling theory and methods: A review and research challenges. Isprs journal of photogrammetry and remote sensing, 115, 119–133. https://doi.org/10.1016/j.isprsjprs.2015.10.012

Llorente, A., Garcia-Herranz, M., Cebrian, M., & Moro, E. (2015). Social media fingerprints of unemployment. PloS one, 10(5), e0128692. https://doi.org/10.1371/journal.pone.0128692

Serrano Estrada, L., Serrano Salazar, S., & Álvarez Álvarez, F. J. (2014). Las redes sociales y los SIG como herramientas para conocer las preferencias sociales en las ciudades turísticas: el caso de Benidorm. Presented at the XVI Congreso Nacional de Tecnologías de Información Geográfica (pp. 1005–1012). Alicante, Spain, June 25–27. Madrid: Asociación de Geógrafos Españoles.