Análisis de los patrones espacio-temporales de eventos a partir de datos de Twitter: el caso de la <em>World Pride</em> 2017 en Madrid

Joaquín Osorio Arjona

doi:10.3989/estgeogr.202047.027

Autores/as

Joaquín Osorio Arjona Departamento de Geografía Universidad Complutense de Madrid https://orcid.org/0000-0002-0102-8756

DOI:

https://doi.org/10.3989/estgeogr.202047.027

Palabras clave:

mega eventos, redes sociales, puntos calientes, SIG, huella digital, huella espacio-temporal

Resumen

Este trabajo analiza los patrones espaciotemporales de un macroevento en una ciudad a partir de nuevas fuentes de datos, partiendo como hipótesis que las multitudes registran una alta actividad en las redes sociales durante los programas del evento. Identificando usuarios que han publicado tweets geolocalizados en el centro de Madrid durante la World Pride 2017, se puede localizar su procedencia, y evaluar el impacto del evento a nivel espaciotemporal a partir de la comparación con los resultados observados durante una semana habitual. Los resultados obtenidos muestran un crecimiento del número de usuarios extranjeros y un fuerte aumento de la actividad en las principales zonas del evento mientras que la actividad de las zonas más alejadas disminuye.

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Citas

Abdelhaq, H., Sengstock, C., y Gertz, M. (2013). EvenTweet: Online localizated event detection from Twitter Proceedings of the VLDB Endowment, 6(12), pp. 1326-1329. https://doi.org/10.14778/2536274.2536307

Anselin, L. (1995). Local indicators of spatial association-LISA. Geographical Analysis, 27(2), pp. 93-115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x

Bar-Gera, H. (2007). Evaluation of a cellular phone-based system for measurements of traffic speeds and travel times: a case study from Israel. Transportation Research Part C: Emerging Technologies, 15(6), pp. 380-391. https://doi.org/10.1016/j.trc.2007.06.003

Batista e Silva, F., Marin Herrera, M., Rosina, K., Ribeiro Barranco, R., Freire, S., y Schiavina, M. (2018). Analyzing spatiotemporal patterns of tourism in Europe at high-resolution with conventional and big data sources. Tourism Management, 68, pp. 101-115. https://doi.org/10.1016/j.tourman.2018.02.020

Cáceres, N. (2012). Traffic Flow Estimation Models Using Cellular Phone Data. IEEE Transactions on Intelligent Transportation Systems, pp. 1-12. https://doi.org/10.1109/TITS.2012.2189006

Cáceres, N., Wideberg, J.P, y Benítez, F.G. (2007). Deriving origin-destination data from a mobile phone network. IET Intelligent Transport Systems, 1, pp. 15-26. https://doi.org/10.1049/iet-its:20060020

Frías-Martínez, V., Soto, V., Hohwald, H., y Frías-Martínez, E. (2012). Characterizing urban landscapes using geolocated tweets. Privacy, Security, Risk and Trust (PASSAT), 2012 International Conference on and 2012 International Conference on Social Computing (SocialCom), pp. 239-248. IEEE. https://doi.org/10.1109/SocialCom-PASSAT.2012.19

Gutiérrez-Puebla, J., y García-Palomares, J.C. (2016). Big (Geo) Data en Ciencias Sociales: Retos y Oportunidades. Revista de Estudios Andaluces, 33(331), pp. 1-23. https://doi.org/10.12795/rea.2016.i33.01

Gutiérrez-Puebla, J. (2018). Big Data y nuevas geografías: la huella digital de las actividades humanas. Documents d'Anàlisi Geogràfica, 64, pp. 195-217. https://doi.org/10.5565/rev/dag.526

Huang, Q., y Wong, D. (2016). Activity patterns, socioeconomic status and urban spatial structure: what can social media data tell us? International Journal of Geographical Information Science, 30(9), pp. 1873-1898. https://doi.org/10.1080/13658816.2016.1145225

Huang, Y., Li, Y., y Shan, J. (2018). Spatial-Temporal Event Detection from Geo-Tagged Tweets. ISPRS International Journal of Geo-Information, 7(4). https://doi.org/10.3390/ijgi7040150

Kim, K-S., Kojima, I., y Ogawa, H. (2016). Discovery of local topics by using latent spatio-temporal relationships in geo-social media. International Journal of Geographical Information Science, 30(9), pp. 1899-1922. https://doi.org/10.1080/13658816.2016.1146956

Kirilenko, A. P., y Stepchenkova, S. O. (2017). Sochi 2014 Olympics on Twitter: Perspectives of hosts and guests. Tourism Management, 63, pp. 54-65. https://doi.org/10.1016/j.tourman.2017.06.007

Kitchin, R. (2013). Big Data and human geography: opportunities, challenges and risks. Dialogues in Human Geography, 3(3), pp. 262-267. https://doi.org/10.1177/2043820613513388

Kitchin, R. (2014). Big Data, new epistemologies and paradigm shifts. Big Data & Society, 1(1), pp. 1-12. https://doi.org/10.1177/2053951714528481

Knott, B., Swart, K., y Visser, S. (2015). The impact of sport mega-events on the quality of life for host city residents: reflections on the 2010 FIFA World Cup. African Journal of Hospitality, Tourism and Leisure, 4, pp. 1-16.

Lansley, G., Smith, M. De, Goodchild, M., y Longley, P. (2018). Big Data and Geospatial Analysis. Geospatial Analysis 6th Edition, pp. 547-570. Edimburgo, Reino Unido: The Winchelsea Press.

Lee, R., y Sumiya, K. (2010). Measuring geographical regularities of crowd behaviors for Twitter-based geo-social event detection. Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks - LBSN '10, pp. 1-10. New York, ACM Press. https://doi.org/10.1145/1867699.1867701

Leszczynski, A. y Crampton, J. (2016). Introduction: spatial big data and everyday life. Big Data & Society, 3(2), pp. 1-6. https://doi.org/10.1177/2053951716661366

Leung, D., Law, R., van Hoof, H., y Buhalis, D. (2013). Social Media in Tourism and Hospitality: A Literature Review. Journal of Travel and Tourism Marketing, 30(1-2), pp. 3-22. https://doi.org/10.1080/10548408.2013.750919

Li, H., Ji, H., y Zhao, L. (2015). Social Event Extraction: Task, Challenges and Techniques. Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015 - ASONAM '15, pp. 526-532, New York, New York, ACM Press. https://doi.org/10.1145/2808797.2809413

Liu, H., Ge, Y., Zheng, Q., Lin, R., y Li, H. (2018). Detecting global and local topics via mining twitter data. Neurocomputing, 273, pp. 120-132. https://doi.org/10.1016/j.neucom.2017.07.056

Marine-Roig, E., y Anton Clavé, S. (2015). Tourism analytics with massive user-generated content: A case study of Barcelona. Journal of Destination Marketing and Management, 4(3), pp. 162-172. https://doi.org/10.1016/j.jdmm.2015.06.004

Masala, E., y Pallares-Barbera, M. (2016). When Internet became Geography. Spatial patterns on urban open spaces through the analysis of user-generated data in Barcelona. ICiTy - Enhancing Places through Technology. MALTA: April, 18‐19. The mid‐term conference for COST Action TU1306 CyberParks: Fostering Knowledge about the Relationship between Information and Communication Technologies and Public Spaces. https://www.um.edu.mt/ev, Valletta, Malta.

Miller, H. J. (2010). The data avalanche is here. Shouldn't we be digging? Journal of Regional Science, 50(1), pp. 181-201. https://doi.org/10.1111/j.1467-9787.2009.00641.x

Miller, H.J., y Goodchild, M.F. (2014). Data-driven geography. GeoJournal, 80(4), pp. 449-461. https://doi.org/10.1007/s10708-014-9602-6

Moya-Gómez, B.; Salas-Olmedo, M. H.; García-Palomares, J. C. y Gutiérrez-Puebla, J. (2017). Dynamic accessibility using Big Data: The role of the changing conditions of network congestion and destination attractiveness. Networks and Spatial Economics, 18(2), pp. 273-290. https://doi.org/10.1007/s11067-017-9348-z

Osorio, J., y García-Palomares, J.C. (2017). Nuevas fuentes y retos para el estudio de la movilidad urbana. Cuadernos Geográficos, 56(3), pp. 247-267.

Osorio, J., y García-Palomares, J.C. (2019). Social media and urban mobility: using Twitter to calculate home-work travel matrices. Cities, 89, pp. 268-280. https://doi.org/10.1016/j.cities.2019.03.006

Popescu, A., y Pennacchiotti, M. (2011). Dancing with the Stars, NBA Games, Politics: An Exploration of Twitter Users' Response to Events. Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, pp. 594-597. http://www.aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/viewPDFInterstitial/2866/3233

Raun, J., Ahas, R., & Tiru, M. (2016). Measuring tourism destinations using mobile tracking data. Tourism Management, 57, pp. 202-212. https://doi.org/10.1016/j.tourman.2016.06.006

Steiger, E., Ellersiek, T., Resch, B., y Zipf, A. (2015). Uncovering latent mobility patterns from Twitter during mass events. Journal for Geographic Information Science, 1, pp. 525-534. https://doi.org/10.1553/giscience2015s525

Tascón, M., y Coullaut, A. (2016). Big Data y el internet de las cosas: qué hay detrás y cómo nos va a cambiar. Madrid, Catarata, 128 pp.

Versichele, M., Neutens, T., Delafontaine, M., y van de Weghe, N. (2012). The use of Bluetooth for analysing spatiotemporal dynamics of human movement at mass events: A case study of the Ghent Festivities. Applied Geography, 32(2), pp. 208-220. https://doi.org/10.1016/j.apgeog.2011.05.011

Weng, J., Yao, Y., Leonardi, E., y Lee, F. (2011). Event Detection in Twitter. Proceedings of the Fifth International Conference on Weblogs and Social Media.

Xu, Y., y González, M. C. (2017). Collective benefits in traffic during mega events via the use of information technologies. Journal of The Royal Society Interface, 14(129), pp. 1-10. https://doi.org/10.1098/rsif.2016.1041 PMid:28404868 PMCid:PMC5414910

Zeng, B., y Gerritsen, R. (2014). What do we know about social media in tourism? A review. Tourism Management Perspectives, 10, 27-36. https://doi.org/10.1016/j.tmp.2014.01.001

Zhou, X., y Xu, C. (2017). Tracing the Spatial-Temporal Evolution of Events Based on Social Media Data. ISPRS International Journal of Geo-Information, 6(3), 88 https://doi.org/10.3390/ijgi6030088