Datos de redes sociales basados en localización para explorar los patrones de consumo espaciales y funcionales de turistas urbanos y residentes

Aurelie Cerdan Schwitzguebel, Oriol Romero Bartomeus

Resumen


La creciente popularidad de los destinos urbanos ha actuado como catalizador del debate sobre la delimitación geográfica de la actividad turística. En este contexto, el Big Data, y más específicamente las redes sociales que integran ubicación (LBSN), aparecen como una valiosa fuente de información para aproximarse a la interacción espacial entre turistas y residentes, desde una perspectiva renovada. Este artículo se centra en la aproximación a las similitudes y diferencias entre el uso geográfico y funcional de las unidades económicas urbanas, por parte de turistas y residentes. Para ello, se ha desarrollado y aplicado un algoritmo de clasificación de usuarios a un conjunto de datos de YELP. Se ha calculado también un ratio de integración entre turistas y residentes urbanos, posteriormente aplicado a los negocios georreferenciados y sus categorías funcionales, en las 11 áreas metropolitanas incluidas en la muestra: Champaign (Illinois, EEUU), Charlotte (Carolina del Norte, EEUU), Cleveland (Ohio, EEUU), Edimburgo (Escocia, GB), Las Vegas (Nevada, EEUU), Madison (Wisconsin, EEUU), Montreal (Quebec, CA), Pittsburg (Pennsylvania, EEUU), Phoenix (Arizona, EEU), Stuttgart (DE) and Toronto (Ontario, CA). Las categorías funcionales que agrupan los negocios muestran claras similitudes en cuanto a la coincidencia espacial entre turistas y residentes. Además, hay una clara concentración geográfica de la actividad para ambos grupos de usuario en todos los casos estudiados.


Palabras clave


turismo urbano; Big Data; Yelp; análisis espacial; patrones de consumo

Texto completo:

PDF (English)

Referencias


Arizona Office of Tourism. (2016). Phoenix & Central Region Visitor Profile report. Retrieved from http://www.stat.gouv.qc.ca/quebec-chiffre-main/pdf/qcm2017_fr.pdf Ashworth, G., & Page, S. J. (2011). Urban tourism research: Recent progress and current paradoxes. Tourism Management, 32(1), 1–15. https://doi.org/10.1016/j.tourman.2010.02.002 Batista e Silva, F., Marín Herrera, M. A., Rosina, K., Ribeiro Barranco, R., Freire, S., & Schiavina, M. (2018). Analysing spatiotemporal patterns of tourism in Europe at high-resolution with conventional and big data sources. Tourism Management, 68, 101–115. https://doi.org/10.1016/j.tourman.2018.02.020 Brandt, T., Bendler, J., & Neumann, D. (2017). Social media analytics and value creation in urban smart tourism ecosystems. Information and Management, 54(6), 703–713. https://doi.org/10.1016/j.im.2017.01.004 Burtenshaw, D., Bateman, M., & Ashworth, G. (1991). The European city. A western perspective. (2nd ed.). London: David Fulton Publishers Ltd. Edwards, D., Griffin, T., & Hayllar, B. (2008). Urban Tourism Research. Developing an Agenda. Annals of Tourism Research, 35(4), 1032–1052. https://doi.org/doi:10.1016/j.annals.2008.09.002 Fernández Güell, J. M., & López, J. G. (2016). Cities futures. A critical assessment of how future studies are applied to cities. Foresight, 18(5), 454–468. https://doi.org/10.1108/FS-06-2015-0032 Florido Trujillo, G., Garzón García, R., & Ramírez López, M. L. (2018). En torno al concepto de sostenibilidad y su compleja aplicación al turismo. el caso del turismo urbano cultural. International Journal of Scientific Management and Tourism (2018), 269–302. Füller, H., & Michel, B. (2014). “Stop Being a Tourist!” New Dynamics of Urban Tourism in Berlin-Kreuzberg. International Journal of Urban and Regional Research, 38(4), 1304–1318. https://doi.org/10.1111/1468-2427.12124 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 Getz, D. (1993a). Planning for tourism business districts. Annals of Tourism Research, 20(3), 583–600. https://doi.org/10.1016/0160-7383(93)90011-Q Getz, D. (1993b). Tourist shopping villages. Development and planning strategies. Tourism Management, 14(1), 15–26. https://doi.org/10.1016/0261-5177(93)90078-Y Hayllar, B., & Griffin, T. (2005). The precinct experience: A phenomenological approach. Tourism Management, 26(4), 517–528. https://doi.org/10.1016/j.tourman.2004.03.011 Hayllar, B., Griffin, T., & Edwards, D. (2008). City Spaces-Tourist Places: Urban tourism precincts. (B. Hayllar, T. Griffin, & D. Edwards, Eds.), City Spaces–Tourist Places. Butterworth-Heinemann. https://doi.org/10.4324/9780080878270 Jansen-Verbeke, M. (1986). Inner-city tourism: resources, tourists and promoters. Annals of Tourism Research, 13(1), 79–100. https://doi.org/https://doi.org/10.1016/0160-7383(86)90058-7 Jansen-Verbeke, M. (1998). Tourismification of Historical Cities. Annals of Tourism Research, 25(4), 739–742. https://doi.org/10.1016/S0160-7383(98)00015-2 Jansen-Verbeke, M., & Ashworth, G. (1990). Environmental integration of recreation and tourism. Annals of Tourism Research, 17(4), 618–622. https://doi.org/10.1016/0160-7383(90)90034-O Judd, D. R. (1999). The Tourist City. (D. R. Judd & S. S. Fainstein, Eds.). New Haven and London: Yale University Press. Kádár, B. (2013). Differences in the spatial patterns of urban tourism in Vienna and Prague. Urbani Izziv, 24(2), 96–111. https://doi.org/10.5379/urbani-izziv-en-2013-24-02-002 Kannisto, P. (2018). Travelling like locals: Market resistance in long-term travel. Tourism Management, 67, 297–306. https://doi.org/10.1016/j.tourman.2018.02.009 Kuo, C.-L., Chan, T.-C., Fan, I.-C., & Zipf, A. (2018). Efficient Method for POI/ROI Discovery Using Flickr Geotagged Photos. ISPRS International Journal of Geo-Information, 7(3), 121. https://doi.org/10.3390/ijgi7030121 Leung, R., Vu, H. Q., & Rong, J. (2017). Understanding tourists’ photo sharing and visit pattern at non-first tier attractions via geotagged photos. Information Technology and Tourism, 17(1), 55–74. https://doi.org/10.1007/s40558-017-0078-3 Lew, A., & McKercher, B. (2006). Modeling tourist movements: A local destination analysis. Annals of Tourism Research, 33(2), 403–423. https://doi.org/10.1016/j.annals.2005.12.002 Li, D., Zhou, X., & Wang, M. (2018). Analyzing and visualizing the spatial interactions between tourists and locals: A Flickr study in ten US cities. Cities, 74(January), 249–258. https://doi.org/10.1016/j.cities.2017.12.012 Li, J., Xu, L., Tang, L., Wang, S., & Li, L. (2018). Big data in tourism research: A literature review. Tourism Management, 68, 301–323. https://doi.org/10.1016/j.tourman.2018.03.009 MacCannell, D. (1976/2017). El Turista: una nueva teoría de la clase ociosa [The Tourist: a New Theory of the Leisure Class]. Editorial Melusina. Maeda, T., Yoshida, M., Toriumi, F., & Ohashi, H. (2018). Extraction of Tourist Destinations and Comparative Analysis of Preferences Between Foreign Tourists and Domestic Tourists on the Basis of Geotagged Social Media Data. ISPRS International Journal of Geo-Information, 7(3), 99. https://doi.org/10.3390/ijgi7030099 Marine-Roig, E., & Anton Clavé, S. (2015). Tourism analytics with massive user-generated content: A case study of Barcelona. Journal of Destination Marketing & Management, 4(3), 162–172. https://doi.org/10.1016/j.jdmm.2015.06.004 Mukhina, K. D., Rakitin, S. V., & Visheratin, A. A. (2017). Detection of tourists attraction points using Instagram profiles. Procedia Computer Science, 108(C), 2378–2382. https://doi.org/10.1016/j.procs.2017.05.131 Önder, I. (2017). Classifying multi-destination trips in Austria with big data. Tourism Management Perspectives, 21, 54–58. https://doi.org/10.1016/j.tmp.2016.11.002 Page, S. J. (1995). Urban Tourism. London: Routledge. Page, S. J., & Hall, C. M. (2003). Managing Urban Tourism. Harlow: Prentice Hall. Pearce, D. G. (1998). Tourist districts in Paris: Structure and functions. Tourism Management, 19(1), 49–65. https://doi.org/10.1016/S0261-5177(97)00095-2 Pearce, D. G. (2001). An integrative framework for urban tourism research. Annals of Tourism Research, 28(4), 926–946. https://doi.org/10.1016/S0160-7383(00)00082-7 Pranata, I., & Susilo, W. (2016). Are the most popular users always trustworthy? The case of Yelp. Electronic Commerce Research and Applications, 20, 30–41. https://doi.org/10.1016/j.elerap.2016.09.005 Rogerson, C. M., & Rogerson, J. M. (2016). Intra-urban spatial differentiation of tourism: Evidence from Johannesburg, South Africa. Urbani Izziv, 27(2), 125–137. https://doi.org/10.5379/urbani-izziv-en-2016-27-02-004 Sakoda, J. M. (1981). A Generalized Index of Dissimilarity. Demography, 18(2), 245–250. https://doi.org/10.2307/2061096 Salas-Olmedo, M. H., Moya-Gómez, B., García-Palomares, J. C., & Gutiérrez, J. (2018). Tourists’ digital footprint in cities: Comparing Big Data sources. Tourism Management, 66, 13–25. https://doi.org/10.1016/j.tourman.2017.11.001 Sapountzi, A., & Psannis, K. E. (2016). Social networking data analysis tools & challenges. Future Generation Computer Systems, 86, 893–913. https://doi.org/10.1016/j.future.2016.10.019 Scherrer, L., Tomko, M., Ranacher, P., & Weibel, R. (2018). Travelers or locals ? Identifying meaningful sub-populations from human movement data in the absence of ground truth. EPJ Data Science, 1–21. https://doi.org/10.1140/epjds/s13688-018-0147-7 Shao, H., Zhang, Y., & Li, W. (2017). Extraction and analysis of city’s tourism districts based on social media data. Computers, Environment and Urban Systems, 65, 66–78. https://doi.org/10.1016/j.compenvurbsys.2017.04.010 Shoval, N., & Raveh, A. (2004). Categorization of tourist attractions and the modeling of tourist cities: Based on the co-plot method of multivariate analysis. Tourism Management, 25(6), 741–750. https://doi.org/10.1016/j.tourman.2003.09.005 Stock, K. (2018). Mining location from social media: A systematic review. Computers, Environment and Urban Systems, (May), 1–32. https://doi.org/10.1016/j.compenvurbsys.2018.05.007 Vera Rebollo, J. F., López Palomeque, F., Marchena Gómez, M. J., & Anton Clavé, S. (2011). Análisis Territorial del Turismo y Planificación de Destinos Turísticos. (J. F. Vera Rebollo, Ed.). Valencia: Tirant lo Blanch. Zhou, B., Tang, X., Zhang, H., & Wang, X. (2014). Measuring crowd collectiveness. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(8), 1586–1599. https://doi.org/10.1109/TPAMI.2014.2300484 Zhou, X., Xu, C., & Kimmons, B. (2015). Detecting tourism destinations using scalable geospatial analysis based on cloud computing platform. Computers, Environment and Urban Systems, 54, 144–153. https://doi.org/10.1016/j.compenvurbsys.2015.07.006


Enlaces refback

  • No hay ningún enlace refback.




Copyright (c) 2019 Aurelie Cerdan Schwitzguebel, Oriol Romero Bartomeus

Licencia de Creative Commons
Este obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial-SinObraDerivada 4.0 Internacional.
RCUB revistesub@ub.edu Avís Legal RCUB Universitat de Barcelona