Abstract
Understanding tourists’ spatial distribution and subgroups is important for urban tourism planning and management. This study utilized mobile signaling data from 21 million equipments to examine the tourist spots travelers visited and their movements in the city of Shanghai. In addition, we used latent profile analysis (LPA) to identify potential tourist groups according to their duration of stay and the tourist spots they visited. The results indicated that historical tourist spots drew a lot of travelers and nearly half of all tourists visited at least one historical site/district. Areas within the inner ring and around the ancient towns beyond the outer ring were frequently visited by tourists. Tourists preferred to visit famous tourist spots sequentially, rather than stopping by nearby less famous spots. Moreover, the connections between these famous spots were more frequent than between other spots. Three groups of tourists were identified, including long-stay multi-interest traveler, short-stay history-lover, and short-stay culture-lover. This study contributes to the application of mobile signaling data in exploring urban tourists’ spatial distribution, as well as can shed light on urban tourism planning and strategy development.




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The data that support the findings of this study are available from the corresponding author upon request.
References
Achterhof R, Huntjens RJC, Meewisse M-L, Kiers HAL (2019) Assessing the application of latent class and latent profile analysis for evaluating the construct validity of complex posttraumatic stress disorder: cautions and limitations. Eur J psychotraumatology 10(1):1698223–1698223. https://doi.org/10.1080/20008198.2019.1698223
Allen DW (2010) GIS tutorial 2: spatial analysis workbook. Esri Press
Ana C, Kastenholz E (2015) Spatiotemporal behaviour of the urban multi-attraction tourist: does distance travelled from country of origin make a difference? Encontros Científicos - Tourism & Management Studies 11(1):91–97
Ashworth G, Page SJ (2011) Urban tourism research: recent progress and current paradoxes. Tour Manag 32(1):1–15. https://doi.org/10.1016/j.tourman.2010.02.002
Bauder M, Freytag T (2015) Visitor mobility in the city and the effects of travel preparation. Tourism Geographies 17(5):682–700. https://doi.org/10.1080/14616688.2015.1053971
Beeco JA, Hallo JC (2014) GPS Tracking of Visitor Use: factors influencing visitor spatial behavior on a Complex Trail System. J Park Recreation Adm 32(2):43–61
Beeco JA, Huang WJ, Hallo JC, Norman WC, McGehee NG, McGee J, Goetcheus C (2013) GPS Tracking of Travel Routes of Wanderers and Planners. Tourism Geographies 15(3):551–573. https://doi.org/10.1080/14616688.2012.726267
Beriatos E, Gospodini A (2004) Glocalising” urban landscapes: Athens and the 2004 olympics. Cities 21(3):187–202. https://doi.org/10.1016/j.cities.2004.03.004
Boivin M, Tanguay GA (2019) Analysis of the determinants of urban tourism attractiveness: the case of Québec City and Bordeaux. J Destination Mark Manage 11:67–79. https://doi.org/10.1016/j.jdmm.2018.11.002
Bonnetain L, Furno A, Krug J, Faouzi N-EE (2019) Can we Map-Match Individual Cellular Network Signaling Trajectories in Urban environments? Data-Driven Study. Transp Res Rec 2673(7):74–88. https://doi.org/10.1177/0361198119847472
Bureau SMS (2019) Shanghai statistical yearbook 2019. Beijing: China Statistics Press Retrieved from https://kns.cnki.net/KCMS/detail/detail.aspx?
Caldeira AM, Kastenholz E (2018) Tourists’ spatial behaviour in urban destinations:the effect of prior destination experience. J Vacation Mark 24(3):247–260. https://doi.org/10.1177/1356766717706102
Chakrabarti A, Ghosh JK (2011) AIC, BIC and recent advances in Model Selection. In: Bandyopadhyay PS, Forster MR (eds) Philosophy of Statistics, vol 7. North-Holland, Amsterdam, pp 583–605
Cheng Y, Tan M (2018) The quantitative research of landscape color: a study of Ming Dynasty City Wall in Nanjing. Color Res Application 43(3):436–448
Cruz J, Brooks D, Marques A (2016) Accuracy of piezoelectric pedometer and accelerometer step counts. J Sports Med Phys Fit 57. https://doi.org/10.23736/S0022-4707.16.06177-X
Derakhshan S (2019) Designing a GIS-based people flow analytics tool for cultural event management in heritage-led cities. PDEng thesis, Technische Universiteit Eindhoven, Eindhoven
East D, Osborne P, Kemp S, Woodfine T (2017) Combining GPS & survey data improves understanding of visitor behaviour. Tour Manag 61:307–320. https://doi.org/10.1016/j.tourman.2017.02.021
Edwards D, Griffin T (2013) Understanding tourists’ spatial behaviour: GPS tracking as an aid to sustainable destination management. J Sustainable Tourism 21(4):580–595. https://doi.org/10.1080/09669582.2013.776063
Edwards D, Griffin T, Hayllar B (2008) Urban Tourism Research: developing an agenda. Annals of Tourism Research 35(4):1032–1052. https://doi.org/10.1016/j.annals.2008.09.002
Eusébio C, João Carneiro M (2015) How diverse is the youth tourism market? An activity-based segmentation study. Tourism: An International Interdisciplinary Journal 63(3):295–316
Ferguson SL, Moore WG, Hull DM (2019) Finding latent groups in observed data: a primer on latent profile analysis in mplus for applied researchers. Int J Behav Dev 44(5);458–468
Garau C (2017) Emerging Technologies and Cultural Tourism: Opportunities for a Cultural Urban Tourism Research Agenda. In: Bellini N, Pasquinelli C (eds) Tourism in the City: towards an integrative agenda on Urban Tourism. Springer International Publishing, Cham, pp 67–80
General Administration of Quality Supervision (2004) I. a. Q. o. t. P. s. R. o. C. Standard of rating for quality of tourist attractions
Gospodini A (2001) Urban Design, Urban Space morphology, Urban Tourism: an Emerging New Paradigm concerning their relationship. Eur Plan Stud 9(7):925–934. https://doi.org/10.1080/09654310120079841
Hardy A, Hyslop S, Booth K, Robards B, Aryal J, Gretzel U, Eccleston R (2017) Tracking tourists’ travel with smartphone-based GPS technology: a methodological discussion. Inform Technol Tourism 17(3):255–274. https://doi.org/10.1007/s40558-017-0086-3
Kang S, Lee G, Kim J, Park D (2018) Identifying the spatial structure of the tourist attraction system in South Korea using GIS and network analysis: an application of anchor-point theory. J Destination Mark Manage 9:358–370. https://doi.org/10.1016/j.jdmm.2018.04.001
Lau PL, Koo TTR, Wu C-L (2020) Spatial distribution of tourism activities: a Polya urn process model of rank-size distribution. J Travel Res 59(2):231–246. https://doi.org/10.1177/0047287519829258
Lestari TK, Esko S, Sarpono S, Rufiadi R (2018) Indonesia’s experience of using signaling mobile positioning data for official tourism statistics Paper presented at the 15th world forum on tourism statistics
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. Tour Manag 68:301–323. https://doi.org/10.1016/j.tourman.2018b.03.009
Lima J, Eusébio C, Kastenholz E (2012) Expenditure-based segmentation of a Mountain Destination Tourist Market. J Travel Tourism Mark 29(7):695–713. https://doi.org/10.1080/10548408.2012.720155
Liu S, Zhang J, Liu P, Xu Y, Xu L, Zhang H (2021) Discovering spatial patterns of tourist flow with multi-layer transport networks. Tourism Geographies 1–23. https://doi.org/10.1080/14616688.2020.1850849
McKercher B, Shoval N, Ng E, Birenboim A (2012) First and repeat visitor Behaviour: GPS tracking and GIS analysis in Hong Kong. Tourism Geographies 14:147–161. https://doi.org/10.1080/14616688.2011.598542
Mou N, Yuan R, Yang T, Zhang H, Tang J, Makkonen T (2020a) Exploring spatio-temporal changes of city inbound tourism flow: the case of Shanghai, China. Tour Manag 76:103955. https://doi.org/10.1016/j.tourman.2019.103955
Mou N, Zheng Y, Makkonen T, Yang T, Tang J, Song Y (2020b) Tourists’ digital footprint: the spatial patterns of tourist flows in Qingdao, China. Tour Manag 81:104151. https://doi.org/10.1016/j.tourman.2020b.104151
Nilbe K, Ahas R, Silm S (2014) Evaluating the travel distances of events visitors and regular visitors using Mobile Positioning Data: the case of Estonia. J Urban Technol 21(2):91–107. https://doi.org/10.1080/10630732.2014.888218
Önder I (2017) Classifying multi-destination trips in Austria with big data. Tourism Manage Perspect 21:54–58. https://doi.org/10.1016/j.tmp.2016.11.002
Park S, Tussyadiah IP, Mazanec JA, Fesenmaier DR (2010) Travel personae of american pleasure travelers: A Network Analysis. J Travel Tourism Mark 27(8):797–811. https://doi.org/10.1080/10548408.2010.527246
Park D, Kim J, Kim WG, Park H (2019) Does distance matter? Examining the distance effect on tourists’ multi-attraction travel behaviors. J Travel Tourism Mark 36(6):692–709. https://doi.org/10.1080/10548408.2019.1624243
Parroco AM, Vaccina F, De Cantis S, Ferrante M (2012) Multi-destination trips: a survey on incoming zourism in Sicily. Kiel Institute for the World Economy (IfW)
Raun J, Ahas R, Tiru M (2016) Measuring tourism destinations using mobile tracking data. Tour Manag 57:202–212. https://doi.org/10.1016/j.tourman.2016.06.006
Shi B, Zhao J, Chen P-J (2017) Exploring urban tourism crowding in Shanghai via crowdsourcing geospatial data. Curr Issues Tourism 20(11):1186–1209. https://doi.org/10.1080/13683500.2016.1224820
Spencer C, Woolley H (2000) Children and the city: a summary of recent environmental psychology research. Child Care Health Dev 26(3):181–198
Spurk D, Hirschi A, Wang M, Valero D, Kauffeld S (2020) Latent profile analysis: a review and “how to” guide of its application within vocational behavior research. J Vocat Behav 120:103445. https://doi.org/10.1016/j.jvb.2020.103445
Sun H, Chen Y, Lai J, Wang Y, Liu X (2021) Identifying tourists and locals by K-Means clustering method from mobile phone Signaling Data. J Transp Eng Part A: Syst 147(10):04021070. https://doi.org/10.1061/JTEPBS.0000580
Tettamanti T, Demeter H, Varga I (2012) Route choice estimation based on cellular signaling data. Acta Polytech Hungarica 9(4):207–220
Tkaczynski A, Rundle-Thiele SR, Beaumont N (2009) Segmentation: a tourism stakeholder view. Tour Manag 30(2):169–175. https://doi.org/10.1016/j.tourman.2008.05.010
Vu HQ, Luo JM, Li G, Law R (2020) Exploration of Tourist Activities in Urban Destination using Venue Check-In Data. J Hospitality Tourism Res 44(3):472–498. https://doi.org/10.1177/1096348019889121
Wang J, Wang X (2019) Structural equation modeling: applications using Mplus. Hoboken, NJ: John Wiley & Sons. https://doi.org/10.1002/9781119422730
Wang C, Hess DB (2021) Role of Urban Big Data in Travel Behavior Research. Transp Res Rec 2675(4):222–233. https://doi.org/10.1177/0361198120975029
Widhalm P, Yang Y, Ulm M, Athavale S, González MC (2015) Discovering urban activity patterns in cell phone data. Transportation 42(4):597–623
Xiao Y, Wang Y, Miao S, Niu X (2021) Assessing polycentric urban development in Shanghai, China, with detailed passive mobile phone data. Environ Plann B: Urban Analytics City Sci. https://doi.org/10.1177/2399808320982306
Xiaoli Z, Lin LU (2017) Study of the spatial-temporal behavior of domestic tourists in Shanghai, China based on tourism digital footprint. Tourism Research
Xie B (2020) Analysis on the characteristics and linkage effect of Internet attention in Shanghai tourist attractions
Xinyi N, Liang D, Xiaodong S (2015) Understanding urban spatial structure of Shanghai Central City based on mobile phone data. China City Planning Review, 24(3)
Xu F, Nash N, Whitmarsh L (2020) Big data or small data? A methodological review of sustainable tourism. J Sustainable Tourism 28(2):144–163. https://doi.org/10.1080/09669582.2019.1631318
Zhou X, Wang M, Li D (2017) From stay to play–A travel planning tool based on crowdsourcing user-generated contents. Appl Geogr 78:1–11
Acknowledgements
This study was supported by the Fundamental Research Funds for the Central Universities (22120220302), Shanghai Science and Technology Innovation Project (22692111700), National Science Foundation of China (32071835).
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Shi, C., Zhai, Y. & Li, D. Urban tourists’ spatial distribution and subgroup identification in a metropolis --the examination applying mobile signaling data and latent profile analysis. Inf Technol Tourism 25, 453–476 (2023). https://doi.org/10.1007/s40558-023-00255-y
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DOI: https://doi.org/10.1007/s40558-023-00255-y