Identificación de atracciones urbanas centrales mediante seguimiento GPS y análisis de redes

  1. Aranburu Amiano, Ibon 1
  2. Plaza Inchausti, Beatriz 1
  3. Esteban Galarza, Marisol
  1. 1 UPV-EHU
Revista:
BAGE. Boletín de la Asociación Española de Geografía

ISSN: 0212-9426 2605-3322

Año de publicación: 2020

Número: 84

Tipo: Artículo

DOI: 10.21138/BAGE.2840 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: BAGE. Boletín de la Asociación Española de Geografía

Objetivos de desarrollo sostenible

Resumen

This study introduces a useful methodology to identify central urban tourism attractions based on the combination of GPS tracking data and the Network Analysis of visited attractions derived from GPS data. Identifying central attractions becomes critical for city managers when it comes to planning urban facilities, managing municipal resources, locating new attractions or capturing all the potential returns. The first step of the proposed methodology is the detection of visited attractions based on GPS tracking data analysis. Then from this GPS data set a network of visited attractions is built in order to carry out a network analysis. The empirical study is performed for the city of Bilbao, a tourism destination made famous by the Guggenheim Museum. Surprisingly, our methodology leads to unexpected results: while social media content (e.g. TripAdvisor) and experts (tourism agents) point to the Guggenheim as the main tourism asset, in fact it turns out to be the Old Town the most visited place in Bilbao according to real spatial behavior detected by our method. This methodological approach can be valuable for performing decisions that are more accurate and better policies concerning urban planning and management.

Referencias bibliográficas

  • Abedi, N., Bhaskar, A., & Chung, E. (2014). Tracking spatio-temporal movement of human in terms of space utilization using Media-Access-Control address data. Applied Geography, 51, 72–81. https://doi.org/10.1016/j.apgeog.2014.04.001
  • Aranburu, I., Plaza, B., & Esteban, M. (2016). Sustainable Cultural Tourism in Urban Destinations: Does Space Matter? Sustainability, 8(8), 699. https://doi.org/10.3390/su8080699
  • Ashbrook, D., & Starner, T. (2003). Using GPS to learn significant locations and predict movement across multiple users. Personal and Ubiquitous Computing, 7(5), 275–286. https://doi.org/10.1007/s00779-003-0240-0
  • Axhausen, K. W., Schonfelder, S., Wolf, J., Oliveira, M., & Samaga, U. (2003). Eighty weeks of GPS traces: Approaches to enriching trip information. In The 83rd Transportation Research Board Meeting (pp. 1870–1876). Washington, DC.
  • Barthélemy, M. (2011). Spatial networks. Physics Reports, 499(1–3), 1–101. https://doi.org/10.1016/j.physrep.2010.11.002
  • Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: An Open Source Software for Exploring and Manipulating Networks. Retrieved from http://www.aaai.org/ocs/index.php/ICWSM/09/paper/view/154
  • Bohte, W., & Maat, K. (2009). Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: A large-scale application in the Netherlands. Transportation Research Part C: Emerging Technologies, 17(3), 285–297. https://doi.org/10.1016/j.trc.2008.11.004
  • Capello, R., & Perucca, G. (2017). Cultural Capital and Local Development Nexus: Does the Local Environment Matter? In Socioeconomic Environmental Policies and Evaluations in Regional Science (pp. 103–124). Springer. https://doi.org/10.1007/978-981-10-0099-7_6
  • Cheng, J., Karambelkar, B., & Xie, Y. (2017). Leaflet: Create Interactive Web Maps with the JavaScript “Leaflet” Library. R package version 1.1.0. Retrieved from https://cran.r-project.org/package=leaflet
  • CICtourGUNE. (2011). Centro de Investigación Cooperativa en Turismo. Spain.
  • Demšar, U., Špatenková, O., & Virrantaus, K. (2008). Identifying Critical Locations in a Spatial Network with Graph Theory. Transactions in GIS, 12(1), 61–82. https://doi.org/10.1111/j.1467-9671.2008.01086.x
  • Dietvorst, A., & Ashworth, G. (1995). Tourist behaviour and the importance of time-space analysis. In Tourism and spatial transformations. (pp. 163–181). CAB International.
  • Esteban, M. (1999). Bilbao, luces y sombras del titanio: el proceso de regeneración del Bilbao metropolitano. Servicio Editorial de la Universidad del País Vasco.
  • Freeman, L. C. (1978). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215–239. https://doi.org/10.1016/0378-8733(78)90021-7
  • Freytag, T. (2002). Tourism in Heidelberg: getting a picture of the city and its visitors. In City tourism 2002: Proceedings of European Cities Tourism's International Conference in Vienna, Austria, 2002 (pp. 211–219). Springer-Verlag Wien.
  • Fu, Z., Tian, Z., Xu, Y., & Qiao, C. (2016). A Two-Step Clustering Approach to Extract Locations from Individual GPS Trajectory Data. ISPRS International Journal of Geo-Information, 5(10), 166. https://doi.org/10.3390/ijgi5100166
  • Galí Espelt, N., & Donaire Benito, J. A. (2018). First-time versus repeat visitors’ behavior patterns: a GPS analysis. Boletín de La Asociación de Geógrafos Españoles, (78), 2711. https://doi.org/10.21138/bage.2711
  • 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
  • Gobierno Vasco. (2012). Open Data Euskadi. Retrieved from http://opendata.euskadi.eus
  • Gospodini, A. (2001). Urban Design , Urban Space Morphology , Urban Tourism : An Emerging New Paradigm Concerning. European Planning Studies, 9(7), 925–934. https://doi.org/10.1080/0965431012007984
  • Grinberger, A. Y., Shoval, N., & McKercher, B. (2014). Typologies of tourists’ time–space consumption: a new approach using GPS data and GIS tools. Tourism Geographies, 16(1), 105–123. https://doi.org/10.1080/14616688.2013.869249
  • Gschwender, A., Munizaga, M., & Simonetti, C. (2016). Using smart card and GPS data for policy and planning: The case of Transantiago. Research in Transportation Economics, 59, 242–249. https://doi.org/10.1016/j.retrec.2016.05.004
  • Hall, C. M., & Ram, Y. (2019). Measuring the relationship between tourism and walkability? Walk Score and English tourist attractions. Journal of Sustainable Tourism, 27(2), 223–240. https://doi.org/10.1080/09669582.2017.1404607
  • Hartman, G. W. (1950). The Central Business District - A Study in Urban Geography. Economic Geography, 26(4), 237. https://doi.org/10.2307/141260
  • Hasan, S., Schneider, C. M., Ukkusuri, S. V, & González, M. C. (2013). Spatiotemporal Patterns of Urban Human Mobility. Journal of Statistical Physics, 151(1–2), 304–318. https://doi.org/10.1007/s10955-012-0645-0
  • Hawelka, B., Sitko, I., Beinat, E., Sobolevsky, S., Kazakopoulos, P., & Ratti, C. (2014). Geo-located Twitter as proxy for global mobility patterns. Cartography and Geographic Information Science, 41(3), 260–271. https://doi.org/10.1080/15230406.2014.890072
  • Jayasinghe, A., Sano, K., & Rattanaporn, K. (2017). Application for developing countries: Estimating trip attraction in urban zones based on centrality. Journal of Traffic and Transportation Engineering (English Edition), 4(5), 464–476.
  • Kaplan, E. D. (1996). Understanding GPS: Principles and Applications. Artech House.
  • Karambelkar, B., & Zheng, B. (2017). Extra Functionality for “leaflet” Package. Retrieved from https://cran.r-project.org/package=leaflet.extras
  • Kladou, S., & Mavragani, E. (2015). Assessing destination image: An online marketing approach and the case of TripAdvisor. Journal of Destination Marketing & Management, 4(3), 187–193. https://doi.org/10.1016/j.jdmm.2015.04.003
  • Kourtit, K., Nijkamp, P., & Partridge, M. D. (2013). The New Urban World. European Planning Studies, 21(3), 285–290. https://doi.org/10.1080/09654313.2012.716242
  • Lau, G., & McKercher, B. (2006). Understanding tourist movement patterns in a destination: A GIS approach. Tourism and Hospitality Research, 7(1), 39–49. https://doi.org/10.1057/palgrave.thr.6050027
  • Le-Klähn, D.-T. (2016). Sustainable Tourist Mobility: Implications for Urban Destination Management. In Sustainable Mobility in Metropolitan Regions (pp. 55–63). Wiesbaden: Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-14428-9_4
  • Leung, X. Y., Wang, F., Wu, B., Bai, B., Stahura, K. A., & Xie, Z. (2012). A Social Network Analysis of Overseas Tourist Movement Patterns in Beijing: the Impact of the Olympic Games. International Journal of Tourism Research, 14(5), 469–484. https://doi.org/10.1002/jtr.876
  • 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
  • Liu, B., Huang, S. S., & Fu, H. (2017). An application of network analysis on tourist attractions: The case of Xinjiang, China. Tourism Management, 58, 132–141. https://doi.org/10.1016/j.tourman.2016.10.009
  • Mazimpaka, J. D., & Timpf, S. (2015). Exploring the Potential of Combining Taxi GPS and Flickr Data for Discovering Functional Regions. In AGILE 2015 (pp. 3–18). Springer. https://doi.org/10.1007/978-3-319-16787-9_1
  • Mckercher, B., & Lau, G. (2008). Movement Patterns of Tourists within a Destination. Tourism Geographies, 10(3), 355–374. https://doi.org/10.1080/14616680802236352
  • McKercher, B., Shoval, N., Ng, E., & Birenboim, A. (2012). First and Repeat Visitor Behaviour: GPS Tracking and GIS Analysis in Hong Kong. Tourism Geographies, 14(1), 147–161. https://doi.org/10.1080/14616688.2011.598542
  • Modsching, M., Kramer, R., Hagen, K. Ten, & Gretzel, U. (2008). Using Location-based Tracking Data to Analyze the Movements of City Tourists. Information Technology & Tourism, 10(1), 31–42. https://doi.org/10.3727/109830508785059011
  • Montoliu, R., Blom, J., & Gatica-Perez, D. (2013). Discovering places of interest in everyday life from smartphone data. Multimedia Tools and Applications, 62(1), 179–207. https://doi.org/10.1007/s11042-011-0982-z
  • Murakami, E., & Wagner, D. P. (1999). Can using global positioning system (GPS) improve trip reporting? Transportation Research Part C: Emerging Technologies, 7(2), 149–165.
  • Page, S. (2004). Transport and tourism. In A companion to tourism. Blackwell Publishing, Malden, Mass.
  • Pearce, D. G. (1988). Tourist time-budget. Annals of Tourism Research, 15(1), 106–121.
  • Peng, X., & Huang, Z. (2017). A Novel Popular Tourist Attraction Discovering Approach Based on Geo-Tagged Social Media Big Data. ISPRS International Journal of Geo-Information, 6(7), 216. https://doi.org/10.3390/ijgi6070216
  • Plaza, B., & Haarich, S. N. (2009). Museums for urban regeneration? Exploring conditions for their effectiveness. Journal of Urban Regeneration and Renewal, 2(3), 259–271. Retrieved from https://www.ingentaconnect.com/content/hsp/jurr/2009/00000002/00000003/art00006
  • Quiroga, C. A., & Bullock, D. (1998). Travel time studies with global positioning and geographic information systems: an integrated methodology. Transportation Research Part C: Emerging Technologies, 6(1), 101–127.
  • Ratti, C., Sobolevsky, S., Calabrese, F., Andris, C., Reades, J., Martino, M.,…Strogatz, S. H. (2010). Redrawing the Map of Great Britain from a Network of Human Interactions. PLoS ONE, 5(12), e14248. https://doi.org/10.1371/journal.pone.0014248
  • Richards, G. (2010). Increasing the Attractiveness of Places Through Cultural Resources. Tourism Culture & Communication, 10(1), 47–58. https://doi.org/10.3727/109830410X12629765735678
  • Richards, G. (2011). Creativity and tourism. Annals of Tourism Research, 38(4), 1225–1253. https://doi.org/10.1016/j.annals.2011.07.008
  • Richards, G. (2014). Creativity and tourism in the city. Current Issues in Tourism, 17(2), 119–144. https://doi.org/10.1080/13683500.2013.783794
  • Ruhnau, B. (2000). Eigenvector-centrality—a node-centrality? Social Networks, 22(4), 357–365. https://doi.org/10.1016/S0378-8733(00)00031-9
  • Sacco, P., Ferilli, G., & Blessi, G. T. (2014). Understanding culture-led local development: A critique of alternative theoretical explanations. Urban Studies, 51(13), 2806–2821.
  • Schönfelder, S., Axhausen, K. W., Antille, N., & Bierlaire, M. (2002). Exploring the Potentials of Automatically Collected GPS Data for Travel Behaviour Analysis: A Swedish Data Source. Arbeitsberichte Verkehrs-Und Raumplanung, 124.
  • Schuessler, N., & Axhausen, K. W. (2009). Identifying trips and activities and their characteristics from GPS raw data without further information. Transportation Research Record: Journal of the Transportation Research Board., 2105, 1–28.
  • Scott, J. P. (2000). Social Network Analysis: A Handbook. SAGE Publications. Retrieved from https://uk.sagepub.com/en-gb/eur/the-sage-handbook-of-social-network-analysis/book232753
  • Shaw, G., Agarwal, S., & Bull, P. (2000). Tourism consumption and tourist behaviour: A British perspective. Tourism Geographies, 2(3), 264–289. https://doi.org/10.1080/14616680050082526
  • Shen, L., & Stopher, P. R. (2014). Review of GPS Travel Survey and GPS Data-Processing Methods. Transport Reviews, 34(3), 316–334.https://doi.org/10.1080/01441647.2014.903530
  • Shoval, N., & Isaacson, M. (2009). Tourist Mobility and Advanced Tracking Technologies. Routledge Advances in Tourism series.
  • Shoval, N., McKercher, B., Ng, E., & Birenboim, A. (2011). Hotel location and tourist activity in cities. Annals of Tourism Research, 38(4), 1594–1612. https://doi.org/10.1016/j.annals.2011.02.007
  • Silberberg, T. (1995). Cultural Tourism and Business Opportunities for Museums and Heritage Sites. Tourism Management, 16(5), 361–365. https://doi.org/10.1016/0261-5177(95)00039-Q
  • Soh, H., Lim, S., Zhang, T., Fu, X., Lee, G. K. K., Hung, T. G. G., … Wong, L. (2010). Weighted complex network analysis of travel routes on the Singapore public transportation system. Physica A: Statistical Mechanics and Its Applications, 389(24), 5852–5863. https://doi.org/10.1016/j.physa.2010.08.015
  • Stienmetz, J. L., & Fesenmaier, D. R. (2015). Estimating value in Baltimore, Maryland: An attractions network analysis. Tourism Management, 50, 238–252. https://doi.org/10.1016/j.tourman.2015.01.031
  • Stopher, P., Jiang, Q., & FitzGerald, C. (2005). Processing GPS data from travel surveys. In 2nd International Colloqium on the behavioural foundations of integrated land-use and transportation models: frameworks, models and applications (pp. 1–21). Toronto.
  • Taczanowska, K., González, L.-M., Garcia-Massó, X., Muhar, A., Brandenburg, C., & Toca-Herrera, J.-L. (2014). Evaluating the structure and use of hiking trails in recreational areas using a mixed GPS tracking and graph theory approach. Applied Geography, 55, 184–192. https://doi.org/10.1016/j.apgeog.2014.09.011
  • Tchetchik, a., Fleischer, A., & Shoval, N. (2009). Segmentation of Visitors to a Heritage Site Using High-resolution Time-space Data. Journal of Travel Research, 48(2), 216–229. https://doi.org/10.1177/0047287509332307
  • Throsby, D. (2017). Culturally sustainable development: theoretical concept or practical policy instrument? International Journal of Cultural Policy, 23(2), 133–147.
  • Timmermans, H., Arentze, T., & Joh, C.-H. (2002). Analysing space-time behaviour: new approaches to old problems. Progress in Human Geography, 26(2), 175–190. https://doi.org/10.1191/0309132502ph363ra
  • Urtasun, A., & Gutiérrez, I. (2006). Tourism agglomeration and its impact on social welfare: An empirical approach to the Spanish case. Tourism Management, 27(5), 901–912.
  • Wang, D., Pedreschi, D., Song, C., Giannotti, F., & Barabasi, A.-L. (2011). Human mobility, social ties, and link prediction. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ’11 (p. 1100). New York, New York, USA: ACM Press. https://doi.org/10.1145/2020408.2020581
  • Wolf, J., Guensler, R., & Bachman, W. (2001). Elimination of the travel diary: Experiment to derive trip purpose from global positioning system travel data. Transportation Research Record: Journal of the Transportation Research Board, 1768(1), 125–134.
  • Xiang, L., Gao, M., & Wu, T. (2016). Extracting Stops from Noisy Trajectories: A Sequence Oriented Clustering Approach. ISPRS International Journal of Geo-Information, 5(3), 29. https://doi.org/10.3390/ijgi5030029
  • Yu, W., Ai, T., & Shao, S. (2015). The analysis and delimitation of Central Business District using network kernel density estimation. Journal of Transport Geography, 45, 32–47.
  • Yuan, J., Zheng, Y., & Xie, X. (2012). Discovering regions of different functions in a city using human mobility and POIs. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 186–194). New York, USA: ACM Press. https://doi.org/10.1145/2339530.2339561
  • Zhong, C., Schläpfer, M., Müller Arisona, S., Batty, M., Ratti, C., & Schmitt, G. (2017). Revealing centrality in the spatial structure of cities from human activity patterns. Urban Studies, 54(2), 437–455. https://doi.org/10.1177/0042098015601599
  • 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 de Geógrafos Españoles, (79). https://doi.org/10.21138/bage.2464