2,001
Views
2
CrossRef citations to date
0
Altmetric
Current Issues in Method and Practice

Revisiting city tourism in the longer run: an exploratory analysis based on LBSN data

ORCID Icon, &
Pages 584-599 | Received 10 Jun 2022, Accepted 14 Feb 2023, Published online: 13 Mar 2023

References

  • Adamiak, C. (2018). Mapping Airbnb supply in European cities. Annals of Tourism Research, 71, 67–71. https://doi.org/10.1016/j.annals.2018.02.008
  • Adamiak, C., & Szyda, B. (2022). Combining conventional statistics and big data to map global tourism destinations before COVID-19. Journal of Travel Research, 61(8), 1848–1871. https://doi.org/10.1177/00472875211051418
  • Amore, A., de Bernardi, C., & Arvanitis, P. (2022). The impacts of Airbnb in Athens, Lisbon and Milan: A rent gap theory perspective. Current Issues in Tourism, 25(20), 3329–3342. https://doi.org/10.1080/13683500.2020.1742674
  • Anselin, L. (1995). Local indicators of spatial association-LISA. Geographical Analysis, 27(2), 93–115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
  • Assaf, A. G., Kock, F., & Tsionas, M. (2022). Tourism during and after COVID-19: An expert-informed agenda for future research. Journal of Travel Research, 61(2), 454–457. https://doi.org/10.1177/00472875211017237
  • Ba, C., Frank, S., Müller, C., Laura Raschke, A., Wellner, K., & Zecher, A. (2021). The power of new urban tourism: Spaces, representations and contestations (1st ed.). Routledge.
  • Barata-Salgueiro, T., Mendes, L., & Guimarães, P. (2017). Tourism and urban changes: Lessons from Lisbon. In M. Gravari-Barbas & S. Guinand (Eds.), Tourism and gentrification in contemporary metropolises: International perspectives (pp. 255–275). Routledge.
  • Barros, C., Moya-Gómez, B., & Gutiérrez, J. (2020). Using geotagged photographs and GPS tracks from social networks to analyse visitor behaviour in national parks. Current Issues in Tourism, 23(10), 1291–1310. https://doi.org/10.1080/13683500.2019.1619674
  • 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
  • Batty, M. (2013). Big data, smart cities and city planning. Dialogues in Human Geography, 3(3), 274–279. https://doi.org/10.1177/2043820613513390
  • Beritelli, P., Reinhold, S., & Laesser, C. (2020). Visitor flows, trajectories and corridors: Planning and designing places from the traveler’s point of view. Annals of Tourism Research, 82, 102936. https://doi.org/10.1016/j.annals.2020.102936
  • Borgatti, S. P. (2005). Centrality and network flow. Social Networks, 27(1), 55–71. https://doi.org/10.1016/j.socnet.2004.11.008
  • Casado-Díaz, A. B., Navarro-Ruiz, S., Nicolau, J. L., & Ivars-Baidal, J. (2021). Expanding our understanding of cruise visitors’ expenditure at destinations: The role of spatial patterns, onshore visit choice and cruise category. Tourism Management, 83, 104199. https://doi.org/10.1016/j.tourman.2020.104199
  • Chen, J., Becken, S., & Stantic, B. (2021a). Harnessing social media to understand tourist mobility: The role of information technology and big data. Tourism Review, 77(4), 1219–1233. https://doi.org/10.1108/TR-02-2021-0090
  • Chen, J., Becken, S., & Stantic, B. (2021b). Using Weibo to track global mobility of Chinese visitors. Annals of Tourism Research, 89, 103078. https://doi.org/10.1016/j.annals.2020.103078
  • Cocola-Gant, A., & Gago, A. (2021). Airbnb, buy-to-let investment and tourism-driven displacement: A case study in Lisbon. Environment and Planning A: Economy and Space, 53(7), 1671–1688. https://doi.org/10.1177/0308518X19869012
  • Crampton, J. W., Graham, M., Poorthuis, A., Shelton, T., Stephens, M., Wilson, M. W., & Zook, M. (2013). Beyond the geotag: Situating ‘big data’ and leveraging the potential of the geoweb. Cartography and Geographic Information Science, 40(2), 130–139. https://doi.org/10.1080/15230406.2013.777137
  • Encalada, L., Boavida-Portugal, I., Cardoso Ferreira, C., & Rocha, J. (2017). Identifying tourist places of interest based on digital imprints: Towards a sustainable smart city. Sustainability, 9(12), 2317. https://doi.org/10.3390/su9122317
  • Encalada-Abarca, L., Ferreira, C. C., & Rocha, J. (2022). Measuring tourism intensification in urban destinations: An approach based on fractal analysis. Journal of Travel Research, 61(2), 394–413. https://doi.org/10.1177/0047287520987627
  • Ferreira, D., Vale, M., Miguel Carmo, R., Encalada-Abarca, L., & Marcolin, C. (2021). The three levels of the urban digital divide: Bridging issues of coverage, usage and its outcomes in VGI platforms. Geoforum; Journal of Physical, Human, and Regional Geosciences, 124, 195–206. https://doi.org/10.1016/j.geoforum.2021.05.002
  • 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
  • Giglio, S., Bertacchini, F., Bilotta, E., & Pantano, P. (2019). Using social media to identify tourism attractiveness in six Italian cities. Tourism Management, 72, 306–312. https://doi.org/10.1016/j.tourman.2018.12.007
  • González-Bailón, S. (2013). Big data and the fabric of human geography. Dialogues in Human Geography, 3(3), 292–296. https://doi.org/10.1177/2043820613515379
  • Haklay, M. (Muki). (2013). Neogeography and the delusion of democratisation. Environment and Planning A: Economy and Space, 45(1), 55–69. https://doi.org/10.1068/a45184
  • Hamstead, Z. A., Fisher, D., Ilieva, R. T., Wood, S. A., McPhearson, T., & Kremer, P. (2018). Geolocated social media as a rapid indicator of park visitation and equitable park access. Computers, Environment and Urban Systems, 72, 38–50. https://doi.org/10.1016/j.compenvurbsys.2018.01.007
  • Hernández-Martín, R., Rodríguez-Rodríguez, Y., & Gahr, D. (2017). Functional zoning for smart destination management. European Journal of Tourism Research, 17, 43–58. https://doi.org/10.54055/ejtr.v17i.293
  • Huang, H., Yao, X. A., Krisp, J. M., & Jiang, B. (2021). Analytics of location-based big data for smart cities: Opportunities, challenges, and future directions. Computers, Environment and Urban Systems, 90, 101712. https://doi.org/10.1016/j.compenvurbsys.2021.101712
  • Huang, Q., & Wong, D. W. S. (2015). Modeling and visualizing regular human mobility patterns with uncertainty: An example using twitter data. Annals of the Association of American Geographers, 105(6), 1179–1197. https://doi.org/10.1080/00045608.2015.1081120
  • Instituto Nacional de Estatística [INE]. (2017). Statistical Yearbook of Área Metropolitana de Lisboa Region 2017. https://www.ine.pt/xportal/xmain?xpid=INE&xpgid=ine_publicacoes&PUBLICACOESpub_boui=320468753&PUBLICACOESmodo=2.
  • Jiang, Y., Li, Z., & Ye, X. (2019). Understanding demographic and socioeconomic biases of geotagged Twitter users at the county level. Cartography and Geographic Information Science, 46(3), 228–242. https://doi.org/10.1080/15230406.2018.1434834
  • Jin, C., Cheng, J., & Xu, J. (2018). Using user-generated content to explore the temporal heterogeneity in tourist mobility. Journal of Travel Research, 57(6), 779–791. https://doi.org/10.1177/0047287517714906
  • Kádár, B. (2014). Measuring tourist activities in cities using geotagged photography. Tourism Geographies, 16(1), 88–104. https://doi.org/10.1080/14616688.2013.868029
  • Kádár, B., & Gede, M. (2021). Tourism flows in large-scale destination systems. Annals of Tourism Research, 87, 103113. https://doi.org/10.1016/j.annals.2020.103113
  • Kendall, M. G. (1970). Rank correlation methods (4th ed.). London: Charles Griffin.
  • Kitchin, R. (2014). The real-time city? Big data and smart urbanism. GeoJournal, 79(1), 1–14. https://doi.org/10.1007/s10708-013-9516-8
  • Koens, K., Smit, B., & Melissen, F. (2021). Designing destinations for good: Using design roadmapping to support pro-active destination development. Annals of Tourism Research, 89, 103233. https://doi.org/10.1016/j.annals.2021.103233
  • Li, D., Zhou, X., & Wang, M. (2018a). Analyzing and visualizing the spatial interactions between tourists and locals: A Flickr study in ten US cities. Cities, 74, 249–258. https://doi.org/10.1016/j.cities.2017.12.012
  • Li, J., Xu, L., Tang, L., Wang, S., & Li, L. (2018b). Big data in tourism research: A literature review. Tourism Management, 68, 301–323. https://doi.org/10.1016/j.tourman.2018.03.009
  • Li, L., Goodchild, M. F., & Xu, B. (2013). Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr. Cartography and Geographic Information Science, 40(2), 61–77. https://doi.org/10.1080/15230406.2013.777139
  • Li, Z., Huang, X., Hu, T., Ning, H., Ye, X., Huang, B., & Li, X. (2021). ODT flow: Extracting, analyzing, and sharing multi-source multi-scale human mobility. PLOS ONE, 16(8), e0255259. https://doi.org/10.1371/journal.pone.0255259
  • Liu, Q., Wang, Z., & Ye, X. (2018). Comparing mobility patterns between residents and visitors using geo-tagged social media data. Transactions in GIS, 22(6), 1372–1389. https://doi.org/10.1111/tgis.12478
  • Long, Y., Zhai, W., Shen, Y., & Ye, X. (2018). Understanding uneven urban expansion with natural cities using open data. Landscape and Urban Planning, 177, 281–293. https://doi.org/10.1016/j.landurbplan.2017.05.008
  • Lu, W., & Stepchenkova, S. (2015). User-generated content as a research mode in tourism and hospitality applications: Topics, methods, and software. Journal of Hospitality Marketing & Management, 24(2), 119–154. https://doi.org/10.1080/19368623.2014.907758
  • Mancini, F., Coghill, G. M., & Lusseau, D. (2018). Using social media to quantify spatial and temporal dynamics of nature-based recreational activities. PLOS ONE, 13(7), e0200565. https://doi.org/10.1371/journal.pone.0200565
  • Martí, P., García-Mayor, C., & Serrano-Estrada, L. (2021). Taking the urban tourist activity pulse through digital footprints. Current Issues in Tourism, 157–176. https://doi.org/10.1080/13683500.2019.1706458
  • McKercher, B., & Wong, I. A. (2021). Do destinations have multiple lifecycles? Tourism Management, 83, 104232. https://doi.org/10.1016/j.tourman.2020.104232
  • Milano, C., González-Reverté, F., & Benet Mòdico, A. (2023). The social construction of touristification. Residents’ perspectives on mobilities and moorings. Tourism Geographies, 1–19. https://doi.org/10.1080/14616688.2022.2150785
  • Mor, M., Dalyot, S., & Ram, Y. (2023). Who is a tourist? Classifying international urban tourists using machine learning. Tourism Management, 95, 104689. https://doi.org/10.1016/j.tourman.2022.104689
  • Niu, H., & Silva, E. A. (2023). Understanding temporal and spatial patterns of urban activities across demographic groups through geotagged social media data. Computers, Environment and Urban Systems, 100, 101934. https://doi.org/10.1016/j.compenvurbsys.2022.101934
  • Observatório Turismo de Lisboa. (2018). Hotel information by area. https://www.visitlisboa.com/about-turismo-de-lisboa/observatório.
  • Önder, I., Koerbitz, W., & Hubmann-Haidvogel, A. (2016). Tracing tourists by their digital footprints. Journal of Travel Research, 55(5), 566–573. https://doi.org/10.1177/0047287514563985
  • Paldino, S., Bojic, I., Sobolevsky, S., Ratti, C., & González, M. C. (2015). Urban magnetism through the lens of geo-tagged photography. EPJ Data Science, 4(1), 5. https://doi.org/10.1140/epjds/s13688-015-0043-3
  • Paldino, S., Kondor, D., Bojic, I., Sobolevsky, S., González, M. C., & Ratti, C. (2016). Uncovering urban temporal patterns from geo-tagged photography. Plos One, 11(12), e0165753. https://doi.org/10.1371/journal.pone.0165753
  • Preis, T., Botta, F., & Moat, H. S. (2020). Sensing global tourism numbers with millions of publicly shared online photographs. Environment and Planning A: Economy and Space, 52(3), 471–477. https://doi.org/10.1177/0308518X19872772
  • 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
  • Sequera, J., & Nofre, J. (2020). Touristification, transnational gentrification and urban change in Lisbon: The neighbourhood of Alfama. Urban Studies, 57(15), 3169–3189. https://doi.org/10.1177/0042098019883734
  • 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. (2018). Urban planning and tourism in European cities. Tourism Geographies, 20(3), 371–376. https://doi.org/10.1080/14616688.2018.1457078
  • Song, X. P., Richards, D. R., He, P., & Tan, P. Y. (2020). Does geo-located social media reflect the visit frequency of urban parks? A city-wide analysis using the count and content of photographs. Landscape and Urban Planning, 203, 103908. https://doi.org/10.1016/j.landurbplan.2020.103908
  • Steiger, E., Westerholt, R., Resch, B., & Zipf, A. (2015). Twitter as an indicator for whereabouts of people? Correlating Twitter with UK census data. Computers, Environment and Urban Systems, 54, 255–265. https://doi.org/10.1016/j.compenvurbsys.2015.09.007
  • Su, S., Wan, C., Hu, Y., & Cai, Z. (2016). Characterizing geographical preferences of international tourists and the local influential factors in China using geo-tagged photos on social media. Applied Geography, 73, 26–37. https://doi.org/10.1016/j.apgeog.2016.06.001
  • Tenkanen, H., Di Minin, E., Heikinheimo, V., Hausmann, A., Herbst, M., Kajala, L., & Toivonen, T. (2017). Instagram, Flickr, or Twitter: Assessing the usability of social media data for visitor monitoring in protected areas. Scientific Reports, 7(1), 17615. https://doi.org/10.1038/s41598-017-18007-4
  • Vu, H. Q., Li, G., & Law, R. (2020). Cross-country analysis of tourist activities based on venue-referenced social media data. Journal of Travel Research, 59(1), 90–106. https://doi.org/10.1177/0047287518820194
  • Vu, H. Q., Li, G., Law, R., & Ye, B. H. (2015). Exploring the travel behaviors of inbound tourists to Hong Kong using geotagged photos. Tourism Management, 46, 222–232. https://doi.org/10.1016/j.tourman.2014.07.003
  • Vu, H. Q., Li, G., Law, R., & Zhang, Y. (2018). Tourist activity analysis by leveraging mobile social media data. Journal of Travel Research, 57(7), 883–898. https://doi.org/10.1177/0047287517722232
  • Xu, F., Nash, N., & Whitmarsh, L. (2020). Big data or small data? A methodological review of sustainable tourism. Journal of Sustainable Tourism, 28(2), 144–163. https://doi.org/10.1080/09669582.2019.1631318
  • Yuan, Y., & Medel, M. (2016). Characterizing international travel behavior from geotagged photos: A case study of Flickr. PLOS ONE, 11(5), e0154885. https://doi.org/10.1371/journal.pone.0154885
  • 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(Supplement C), 144–153. https://doi.org/10.1016/j.compenvurbsys.2015.07.006