Analysis of the space-temporal patterns of events from Twitter data: the case of Madrid 2017 World Pride

Authors

DOI:

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

Keywords:

mega-events, social network, hot spots, GIS, digital footprint, space-time footprint

Abstract


This work analyses the spatio-temporal patterns of a mass event in a city from new data sources, starting from the hypothesis that crowds register high activity in social networks during the event programs. Identifying users who have posted geolocated tweets in the centre of Madrid during the 2017 World Pride, their origin cities and countries can be located, and the impact of the event at a space-time level can be evaluated from the comparison with the observed results during a regular week. The obtained results show a growth in the number of foreign users and a strong increase in activity in the main areas of the event, while activity in the more remote areas decreases.

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References

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

Published

2020-06-30

How to Cite

Osorio Arjona, J. (2020). Analysis of the space-temporal patterns of events from Twitter data: the case of Madrid 2017 World Pride. Estudios Geográficos, 81(288), e032. https://doi.org/10.3989/estgeogr.202047.027

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Articles