Mobile phone data and tourism statistics: a broken promise?

Laura Grassini; Gianni Dugheri; Laura Grassini; Gianni Dugheri

doi:10.3934/NAR.2021002

National Accounting Review

2021, Volume 3, Issue 1: 50-68. doi: 10.3934/NAR.2021002

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Research article Special Issues

Mobile phone data and tourism statistics: a broken promise?

Laura Grassini ^{1
,
,},
Gianni Dugheri ²

1.
Department of Statistics, Computer Science, Applications, University of Florence, Italy
2.
Statistics Department of the Municipality of Florence, Florence, Italy

Received: 13 October 2020 Accepted: 12 January 2021 Published: 18 January 2021
JEL Codes: C8, C10, Z3

Mobile phone data represents an original source of information about the movements of individuals across territories and, in particular, of visitors. The systematic use of this data for statistical purposes is expected to provide several advantages: timeliness, deeper geographical and time granularity, and reduction of the statistical burden of respondents. To check whether that expectation is well placed, this paper reports a bibliometric analysis and a subsequent literature review of the recent contributions to the use of mobile phone data in quantifying the volume of tourist flows. The main findings show that the systematic exploitation of mobile phone data for producing tourism statistics is still limited in terms of countries (Estonia, Indonesia) and domain (international flows). Furthermore, the basic definitions of visitors stated by the EU Regulation 692/2011 are rarely applied on mobile phone data, and the population of visitors/tourists is often derived as a residual group after having identified the other people movements (residents, commuters). Both the literature review and a brief case study of the Metropolitan City of Florence show the main weaknesses of mobile phone data, which include costs, privacy restrictions, statistical issues of representativeness, among others. Finally, it is clear that mobile phone data cannot completely substitute current surveys on tourism flows because they do not include some noteworthy information concerning a tourist's motivations and a trip's characteristics.
- big data,
- tracking technologies,
- Firenze,
- people movements,
- Biblioshiny,
- tourism statistics
Citation: Laura Grassini, Gianni Dugheri. Mobile phone data and tourism statistics: a broken promise?[J]. National Accounting Review, 2021, 3(1): 50-68. doi: 10.3934/NAR.2021002

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Abstract

Mobile phone data represents an original source of information about the movements of individuals across territories and, in particular, of visitors. The systematic use of this data for statistical purposes is expected to provide several advantages: timeliness, deeper geographical and time granularity, and reduction of the statistical burden of respondents. To check whether that expectation is well placed, this paper reports a bibliometric analysis and a subsequent literature review of the recent contributions to the use of mobile phone data in quantifying the volume of tourist flows. The main findings show that the systematic exploitation of mobile phone data for producing tourism statistics is still limited in terms of countries (Estonia, Indonesia) and domain (international flows). Furthermore, the basic definitions of visitors stated by the EU Regulation 692/2011 are rarely applied on mobile phone data, and the population of visitors/tourists is often derived as a residual group after having identified the other people movements (residents, commuters). Both the literature review and a brief case study of the Metropolitan City of Florence show the main weaknesses of mobile phone data, which include costs, privacy restrictions, statistical issues of representativeness, among others. Finally, it is clear that mobile phone data cannot completely substitute current surveys on tourism flows because they do not include some noteworthy information concerning a tourist's motivations and a trip's characteristics.

References

[1]	Ahas R, Aasa A, Mark Ü, et al. (2007) Seasonal tourism spaces in Estonia: Case study with mobile positioning data. Tourism Manage 28: 898-910. doi: 10.1016/j.tourman.2006.05.010
[2]	Ahas R, Aasa A, Roose A, et al. (2008) Evaluating passive mobile positioning data for tourism surveys: An Estonian case study. Touriam Manage 29: 469-486. doi: 10.1016/j.tourman.2007.05.014
[3]	Ahas R, Mark Ü (2005) Location Based Services: New Challenges for Planning and Public Administration? Futures 37: 547-561. doi: 10.1016/j.futures.2004.10.012
[4]	Ahas R, Silm S, Järv O, et al. (2010) Using Mobile Positioning Data to Model Locations Meaningful to Users of Mobile Phones. J Urban Techol 17: 3-27. doi: 10.1080/10630731003597306
[5]	Antolini F, Grassini L (2020) Issues in Tourism Statistics: A Critical Review Soc Indic Res 150: 1021-1042. doi: 10.1007/s11205-020-02361-4
[6]	Arhipova I, Berzins G, Brekis E, et al. (2020) Mobile phone data statistics as a dynamic proxy indicator in assessing regional economic activity and human commuting patterns. Expert Systems, John Wiley & Sons. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/exsy.12530 (accessed August 2020).
[7]	Aria M, Cuccurullo C (2017) bibliometrix: An R-tool for comprehensive science mapping analysis. J Informetr 11: 959-975. doi: 10.1016/j.joi.2017.08.007
[8]	Birenboim A (2016) New Approaches to the Study of Tourist Experiences in Time and Space. Tourism Geographies 18: 9-17. doi: 10.1080/14616688.2015.1122078
[9]	Chen Q, Hu Z, Su H, et al. (2018) Understanding Travel Patterns of Tourists from Mobile Phone Data: A Case Study in Hainan. 2018 IEEE International Conference on Big Data and Smart Computing (BigComp), 45-51. Available from: https://ieeexplore.ieee.org/document/8367096 (accessed August 2020).
[10]	Cousin G, Hillaireau F (2018) Can Mobile Phone Data Improve the Measurement of International Tourism in France? Economie et Statistique 505-506: 89-108.
[11]	Daas PJH, Puts MJ, Buelens B, et al. (2015) Big Data as a Source for Official Statistics. J Off Stat 3: 249-262. doi: 10.1515/jos-2015-0016
[12]	Dattilo B, Radini R, Sabato M (2016) How many SIM cards in your luggage? A strategy to make mobile phone data usable in tourism statistics. 14th Global Forum on Tourism Statistics, Venice, Italy. Available from: http://tsf2016venice.enit.it/programme (accessed September 2020).
[13]	De Cantis S, Parroco AM, Ferrante M, et al. (2015) Unobserved tourism. Ann Tourism Res 50: 1-18. doi: 10.1016/j.annals.2014.10.002
[14]	de Montjoye YA, Gambs S, Blondel V, et al. (2018) On the privacy-conscientious use of mobile phone data. Sci Data 5: 180286. doi: 10.1038/sdata.2018.286
[15]	European Commission (2011) EU regulation No 692/2011 Of the European Parliament and of the Council of 6 July 2011. Off J Eur Union 192/17. Available from: https://eurlex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2011:192:0017:0032:EN:PDF (accessed January 2019).
[16]	European Commission (2020) Joint European Roadmap towards lifting COVID-19 containment measures. Available from: https://ec.europa.eu/info/sites/info/files/communication_-_a_european_roadmap_to_lifting_coronavirus_containment_measures_0.pdf (accessed September 2020).
[17]	Eurostat (2014a) Methodological manual for tourism statistics. Version 3.1. Available from: https://ec.europa.eu/eurostat/documents/3859598/6454997/KS-GQ-14-013-EN-N.pdf (accessed January 2019).
[18]	Eurostat (2014b) Feasibility Study on the Use of Mobile Positioning Data for Tourism Statistics. Consolidated Report. Eurostat, Luxembourg, ISBN: 978-92-79-39762-2.
[19]	Eurostat (2014c) Feasibility Study on the Use of Mobile Positioning Data for Tourism Statistics. Report 3a. Feasibility of Use: Methodological Issues. Eurostat. Available from: https://ec.europa.eu/eurostat/documents/747990/6225717/MP-Task-3a-report.pdf/38716d6f-c2db-4ee3-92f3-0241bca55f94 (accessed September 2020).
[20]	García FC, Valverde MI, Mascuano JP, et al. (2018) Quality Implications of the use of Big Data in Tourism Statistics: Three exploratory examples. Boletin de estadistica e Investigacion Operativa 34: 138-148.
[21]	Guizzardi A, Bernini C (2012). Measuring underreporting in accommodation statistics: Evidence from Italy. Curr Issues Tourism 15: 597-602. doi: 10.1080/13683500.2012.667071
[22]	Hardy A, Birenboim A, Wells M (2020) Using geoinformatics to assess tourist dispersal at the state level. Ann Tourism Res 82: 1-17. doi: 10.1016/j.annals.2020.102903
[23]	Hardy A, Hyslop S, Booth K, et al. (2017) Tracking tourists' travel with smartphone-based GPS technology: a methodological discussion. Inf Technol Tourism 17: 255-274. doi: 10.1007/s40558-017-0086-3
[24]	ISTAT (2014) Linee strategiche del Censimento permanente della popolazione e delle abitazioni. Metodi, tecniche e organizzazione. ISTAT, Rome. Available from: https://www.istat.it/it/files/2014/11/Cens_Perm_pop.pdf (accessed December, 2020).
[25]	Järv O, Ahas R, Saluveer E, et al. (2012) Mobile Phones in a Traffic Flow: A Geographical Perspective to Evening Rush Hour. PlosOne 14. Available from: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0049171.
[26]	Kim HH, Law R (2015) Smartphones in Tourism and Hospitality Marketing: A Literature Review. J Travel Tourism Mark 32: 692-711. doi: 10.1080/10548408.2014.943458
[27]	Kubo T, Uryu S, Yamano H, et al. (2020) Mobile phone network data reveal nationwide economic value of coastal tourism under climate change. Tourism Manage 77: 1-9. doi: 10.1016/j.tourman.2019.104010
[28]	Kuusik A, Tiru M, Ahas R, et al. (2011) Innovation in destination marketing: The use of passive mobile positioning for the segmentation of repeat visitors in Estonia. Baltic J Managem 6: 378-399. doi: 10.1108/17465261111168000
[29]	Law R, Chan IIC, Wang L (2018) A comprehensive review of mobile technology use in hospitality and tourism. J Hospitality Mark Manage 27: 626-648. doi: 10.1080/19368623.2018.1423251
[30]	Masiero L (2016) International tourism statistics: UNWTO benchmark and cross‐country comparison of definitions and sampling issues. Statistics and TSA: Issue Paper Series. Available from: http://cf.cdn.unwto.org/sites/all/files/docpdf/ipinttourismstat.pdf (access August 2020).
[31]	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: 91-107. doi: 10.1080/10630732.2014.888218
[32]	Nurmi O (2018) Improving the accuracy of outbound tourism statistics with mobile positioning data. 15th Global Forum on Tourism Statistics, Cusco, Peru. Available from: http://www.15th-tourism-stats-forum.com/pdf/Papers/S3/3_3_Improving_the_accuracy_of_outbound_tourism_statistics_with_mobile_positioning_data.pdf (accessed September 2020).
[33]	Nurmi O, Piela P (2019) The Use of Mobile Phone Data in Tourism Statistics, Proceedings of the 62nd ISI World Statistics Congress. Proceeding Special Topic Session, 4: 135-142. Available from: https://2019.isiproceedings.org/Files/6.Special-Topic-Session(STS)-Volume-4.pdf (accessed September 2020).
[34]	Ortega-Fraile FJ, Ríos-Martín MÁ, Ceballos-Hernandez C (2018) Tourism and mobile devices: provenance of academic research through a bibliometric study. J Tourism Anal 25: 91-112. doi: 10.1108/JTA-02-2018-0008
[35]	Osaragi T, Kudo R (2020) Enhancing the Use of Population Statistics Derived from Mobile Phone Users by Considering Building-Use Dependent Purpose of Stay, In: Kyriakidis P, Hadjimitsis D, Skarlatos D, Mansourian A. (eds), Geospatial Technologies for Local and Regional Development. AGILE 2019, Lecture Notes in Geoinformation and Cartography, Springer, Cham.
[36]	Othman AG, Mohamed B, Bahauddin A, et al. (2010) A Geographic Information System Based Approach for Mapping Tourist Accommodations in the East Coast States of Malaysia. World Appl Sci J 10: 14-23.
[37]	Park SY, Pan B (2018) Identifying the next non-stop flying market with a big data approach. Tourism Manage 66: 411-421. doi: 10.1016/j.tourman.2017.12.008
[38]	Park S, Xu Y, Jiang L, et al. (2020) structures of tourism destinations: A trajectory data mining approach leveraging mobile big data. Ann Tourism Res 84: 1-17. doi: 10.1016/j.annals.2020.102973
[39]	Phithakkitnukoon S, Horanont T, Witayangkurn A, et al. (2015) Understanding tourist behavior using large-scale mobile sensing approach: A case study of mobile phone users in Japan. Pervasive Mobile Comput 18: 18-39. doi: 10.1016/j.pmcj.2014.07.003
[40]	Raun J, Ahas R (2016) Defining usual environment with mobile tracking data. 14 Global Forum on Tourism Statistics, Venice, Italy. Available from: http://tsf2016venice.enit.it/programme (accessed September, 2020).
[41]	Raun J, Ahas R, Tiru M (2016) Measuring tourism destinations using mobile tracking data. Tourism Manage 57: 202-212. doi: 10.1016/j.tourman.2016.06.006
[42]	Rodríguez J, Semanjski I, Gautama S, et al. (2018) Unsupervised hierarchical clustering approach for tourism market segmentation based on crowdsourced mobile phone data. Sensors (Switzerland) 18: 2972.
[43]	Saluveer E, Raun J, Tiru M, et al. (2020) Methodological framework for producing national tourism statistics from mobile positioning data. Ann Tourism Res 81: 1-13. doi: 10.1016/j.annals.2020.102895
[44]	Seynaeve G, Demunter C, De Meersman F, et al. (2016) When mobile network operators and statistical offices meet- integrating mobile positioning data into the production process of tourism statistics. 14 Global Forum on Tourism Statistics, Venice, Italy. Available from: http://tsf2016venice.enit.it/programme (accessed September, 2020).
[45]	Shoval N, Ahas R (2016) The use of tracking technologies in tourism research: the first decade. Tourism Geographies 18: 587-606. doi: 10.1080/14616688.2016.1214977
[46]	Sikder R, Uddin Md J, Halder S (2016) An efficient approach of identifying tourist by call detail record analysis, 2016 IWCI: 2016 International Workshop on Computational Intelligence, 136-141.
[47]	Šveda M, Krian F, Barlk P (2019) Utilizing mobile positioning data in tourism: who are the foreign visitors in Slovakia? When do they come and where they stay? Geografický Ćasopis (Geographical Rev) 71: 203-225.
[48]	Uluwiyah A, Setadi B (2017) Improving Data Quality through Big Data: case Study on Big-data Mobile Positioning Data in Indonesia Tourism Statistics. Proceedings--WBIS 2017: 2017 Internatinal Workshop on Big Data and Information Security. Available from: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8275101 (accessed August 2020).
[49]	UN (2019) Handbook on the Use of Mobile Phone Data for Official Statistics (Draft 2019). Global Working Group on Big Data for Official Statistic. Available from: https://unstats.un.org/bigdata/taskteams/mobilephone/.
[50]	UNECE (2019) New data sources for official statistics-access, use and new skills. Economic Commission for Europe, Conference of European Statisticians, Paris, ECE/CES/2019/41. Available from: https://www.unece.org/fileadmin/DAM/stats/documents/ece/ces/2019/ECE_CES_2019_41.pdf (accessed September 2020).
[51]	Wang D, Park S, Fesenmaier DR (2012) The role of smartphones in mediating the touristic experience. J Travel Res 51: 371-387. doi: 10.1177/0047287511426341
[52]	Wang Y, de Almeida Correia GH, van Arem B (2019) Relationships between mobile phone usage and activity-travel behavior: A review of the literature and an example, Advances in Transport Policy and Planning, Ben-Elia, E. (ed.)., Elsevier, 3: 81-105.
[53]	Wang Y, de Almeida Correia GH, van Arem B, et al. (2018) Understanding travellers' preferences for different types of trip destination based on mobile internet usage data. Urban Plann Transp 90: 247-259.
[54]	Xu Y, Li J, Belyi A, et al. (2021) Characterizing destination networks through mobility traces of international tourists - A case study using a nationwide mobile positioning dataset. Aust Geogr 82: 104195.
[55]	Yuan Y, Tseng YH, Ho CI (2019) Tourism information technology research trends: 1990-2016. Tourism Rev 74: 5-19. doi: 10.1108/TR-08-2017-0128
[56]	Yun HJ, Kang DJ, Lee MJ (2018) Spatiotemporal distribution of urban walking tourists by season using GPS-based smartphone application. Asia Pac J Tourism Res 23: 1047-1061. doi: 10.1080/10941665.2018.1513949
[57]	Zhu X, Su T, Yuan H, et al. (2019) Exploring group movement pattern through cellular data: A case study of tourists in Hainan. ISPRS Int J Geo-Inf 8: 74-83. doi: 10.3390/ijgi8020074

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