Travelers or locals? Identifying meaningful sub-populations from human movement data in the absence of ground truth

Ahas R, Aasa A, Yuan Y et al. (2015) Everyday space–time geographies: using mobile phone-based sensor data to monitor urban activity in Harbin, Paris, and Tallinn. Int J Geogr Inf Sci 29:2017–2039. https://doi.org/10.1080/13658816.2015.1063151

Asakura Y, Iryo T (2007) Analysis of tourist behaviour based on the tracking data collected using a mobile communication instrument. Transp Res, Part A, Policy Pract 41:684–690. https://doi.org/10.1016/j.tra.2006.07.003

Australian Bureau of Statistics (2016) Australian statistical geography standard (ASGS). In: Stat. geogr. http://www.abs.gov.au/websitedbs/d3310114.nsf/home/australian+statistical+geography+standard+(asgs) . Accessed 7 Mar 2017

Birenboim A, Shoval N (2015) Mobility research in the age of the smartphone. Ann Assoc Am Geogr 106:283–291. https://doi.org/10.1080/00045608.2015.1100058

Bro R, Smilde AK (2014) Principal component analysis. Anal Methods 6:2812–2831. https://doi.org/10.1039/c3ay41907j

Brock G, Pihur V, Datta S, Datta S (2008) clValid: an R package for cluster validation. J Stat Softw 25:1–28. https://doi.org/10.18637/jss.v025.i04

Calabrese F, Diao M, Di Lorenzo G et al. (2013) Understanding individual mobility patterns from urban sensing data: a mobile phone trace example. Transp Res, Part C, Emerg Technol 26:301–313. https://doi.org/10.1016/j.trc.2012.09.009

Calabrese F, Pereira FC, Di Lorenzo G et al. (2010) The geography of taste: analyzing cell-phone mobility and social events. In: 8th international conference on pervasive computing, pervasive, pp 22–37.

Csáji BC, Browet A, Traag VA et al. (2013) Exploring the mobility of mobile phone users. Phys A, Stat Mech Appl 392:1459–1473. https://doi.org/10.1016/j.physa.2012.11.040

Cuttone A, Lehmann S, González MC (2018) Understanding predictability and exploration in human mobility. EPJ Data Sci 7:2. https://doi.org/10.1140/epjds/s13688-017-0129-1

Datta S, Datta S (2003) Comparisons and validation of statistical clustering techniques for microarray gene expression data. Bioinformatics 19:459–466. https://doi.org/10.1093/bioinformatics/btg025

Dodge S, Laube P, Weibel R (2012) Movement similarity assessment using symbolic representation of trajectories. Int J Geogr Inf Sci 26:1563–1588. https://doi.org/10.1080/13658816.2011.630003

Dodge S, Weibel R, Forootan E (2009) Revealing the physics of movement: comparing the similarity of movement characteristics of different types of moving objects. Comput Environ Urban Syst 33:419–434. https://doi.org/10.1016/j.compenvurbsys.2009.07.008

Edwards D, Griffin T (2013) Understanding tourists’ spatial behaviour: GPS tracking as an aid to sustainable destination management. J Sustain Tour 21:580–595. https://doi.org/10.1080/09669582.2013.776063

Edwards D, Griffin T, Hayllar B et al. (2009) Understanding tourist ‘experiences’ and ‘behaviour’ in cities: an Australian case study. CRC for Sustainable Tourism, Gold Coast

George G, Haas MR, Pentland A (2014) From the editors: big data and management. Acad Manag J 57:321–326

Girardin F, Calabrese F, Fiore FD et al. (2008) Digital footprinting: uncovering tourists with user-generated content. IEEE Pervasive Comput 7:36–43. https://doi.org/10.1109/MPRV.2008.71

Gong L, Liu X, Wu L, Liu Y (2016) Inferring trip purposes and uncovering travel patterns from taxi trajectory data. Cartogr Geogr Inf Sci 43:103–114. https://doi.org/10.1080/15230406.2015.1014424

González MC, Hidalgo CA, Barabási A-L (2008) Understanding individual human mobility patterns. Nature 453:779–782. https://doi.org/10.1038/nature06958

Han J, Kamber M, Pei J (2012) Data mining: concepts and techniques, 3rd edn. Elsevier, Amsterdam

Hayllar B, Griffin T (2005) The precinct experience: a phenomenological approach. Tour Manag 26:517–528. https://doi.org/10.1016/j.tourman.2004.03.011

Hayllar B, Griffin T, Edwards D (2008) Urban tourism precints: engaging with the field. In: Hayllar B, Griffin T, Edwards D (eds) City spaces-tourist places: urban tourism precincts. Butterworth-Heinemann, Oxford, pp 3–18

Hede AM, Hall J (2006) Leisure experiences in tourist attractions: exploring the motivations of local residents. J Hosp Tour Manag 13:10–22. https://doi.org/10.1375/jhtm.13.1.10

James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning. Springer, New York

Jiang S, Ferreira J, González MC (2015) Activity-based human mobility patterns inferred from mobile phone data: a case study of Singapore. IEEE Trans Big Data 3:208–219. https://doi.org/10.1109/TBDATA.2016.2631141

Kádár B, Gede M (2013) Where do tourists go? Visualizing and analysing the spatial distribution of geotagged photography. Cartogr Int J Geogr Inf Geovis 48:78–88. https://doi.org/10.3138/carto.48.2.1839

Kaufman L, Rousseeuw P (1990) Finding groups in data: an introduction to cluster analysis. Wiley-Interscience, New York

Kelly I (2008) Precincts within the urban form: relationships with the city. In: Hayllar B, Griffin T, Edwards D (eds) City spaces-tourist places: urban tourism precincts. Butterworth-Heinemann, Oxford, pp 107–126

Lathia N, Capra L (2011) How smart is your smartcard? Measuring travel behaviours, perceptions, and incentives. In: UbiComp’11—proceedings of the 2011 ACM conference on ubiquitous computing, pp 291–300

Lee J, Han J, Li X, Gonzalez H (2008) TraClass: trajectory classification using hierarchical region based and trajectory based clustering. Proc VLDB Endow 1:1081–1094

Lew A, McKercher B (2006) Modeling tourist movements: a local destination analysis. Ann Tour Res 33:403–423. https://doi.org/10.1016/j.annals.2005.12.002

Lin M, Hsu W-J (2014) Mining GPS data for mobility patterns: a survey. Pervasive Mob Comput 12:1–16. https://doi.org/10.1016/j.pmcj.2013.06.005

MacQueen JB (1967) Some methods for classification and analysis of multivariate observations. In: Proc 5th Berkeley symp math stat probab, vol 1, pp 281–297.

Mayer-Schönberger V, Cukier K (2012) Big data: a revolution that transforms how we work, live, and think

Neef D (2014) Digital exhaust: what everyone should know about big data, digitization and digitally driven innovation, 1st edn. Pearson FT Press, Upper Saddle River

Pan G, Qi G, Wu Z et al. (2013) Land-use classification using taxi GPS traces. IEEE Trans Intell Transp Syst 14:113–123. https://doi.org/10.1109/TITS.2012.2209201

Pappalardo L, Rinzivillo S, Qu Z et al. (2013) Understanding the patterns of car travel. Eur Phys J Spec Top 215:61–73. https://doi.org/10.1140/epjst/e2013-01715-5

Pappalardo L, Simini F, Rinzivillo S et al. (2015) Returners and explorers dichotomy in human mobility. Nat Commun 6:1–8. https://doi.org/10.1038/ncomms9166

Parent C, Pelekis N, Theodoridis Y et al. (2013) Semantic trajectories modeling and analysis. ACM Comput Surv 45:1–32. https://doi.org/10.1145/2501654.2501656

Phithakkitnukoon S, Horanont T, Di Lorenzo G et al. (2010) Activity-aware map: identifying human daily activity pattern using mobile phone data. In: Human behavior understanding. Lecture notes in computer science, pp 14–25

Pihur V, Datta S, Datta S (2009) RankAggreg, an R package for weighted rank aggregation. BMC Bioinform 10:62. https://doi.org/10.1186/1471-2105-10-62

Reades J, Calabrese F, Ratti C (2009) Eigenplaces: analysing cities using the space—time structure of the mobile phone network. Environ Plan B, Plan Des 36:824–836. https://doi.org/10.1068/b34133t

Ren Y, Salim FD, Tomko M et al. (2016) D-Log: a WiFi log-based differential scheme for enhanced indoor localization with single RSSI source and infrequent sampling rate. Pervasive Mob Comput 37:94–114. https://doi.org/10.1016/j.pmcj.2016.09.018

Rousseeuw P (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65. https://doi.org/10.1016/0377-0427(87)90125-7

Shoval N, Ahas R (2016) The use of tracking technologies in tourism research: a review of the first decade. Tour Geogr 18:1–20. https://doi.org/10.1080/14616688.2016.1214977

Song C, Koren T, Wang P, Barabási A-L (2010) Modelling the scaling properties of human mobility. Nat Phys 6:818–823. https://doi.org/10.1038/nphys1760

Sygic (2016) About Sygic. http://www.sygic.com/about . Accessed 14 Nov 2016

Tibshirani R, Walther G, Hastie T (2001) Estimating the number of clusters in a data set via the gap statistic. J R Stat Soc, Ser B, Stat Methodol 63:411–423

Trasarti R, Pinelli F, Nanni M, Giannotti F (2011) Mining mobility user profiles for car pooling. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining—KDD ’11. ACM, New York, pp 1190–1198

Wang D, Pedreschi D, Song C et al. (2011) Human mobility, social ties, and link prediction. In: Proc 17th ACM SIGKDD int conf knowl discov data min—KDD ’11, pp 1100–1108. https://doi.org/10.1145/2020408.2020581

Witten IH, Frank E, Hall MA (2011) Data mining: practical machine learning tools and techniques, 3 edn. Morgan Kaufmann, San Mateo

Zhang D, Huang J, Li Y et al. (2014) Exploring human mobility with multi-source data at extremely large metropolitan scales. In: Proceedings of the 20th ACM annual international conference on mobile computing and networking—MobiCom 2014. ACM, New York, pp 201–212

Zhao Z, Shaw S-L, Xu Y et al. (2016) Understanding the bias of call detail records in human mobility research. Int J Geogr Inf Sci 30:1738–1762. https://doi.org/10.1080/13658816.2015.1137298

Zheng Y, Liu L, Wang L, Xie X (2008) Learning transportation mode from raw GPS data for geographic applications on the web. In: Proceeding of the 17th international conference on World Wide Web—WWW ’08. ACM, New York, p 247

Zheng Y, Liu T, Wang Y et al. (2014) Diagnosing New York city’s noises with ubiquitous data. In: Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing—UbiComp ’14 adjunct. Assoc. Comput. Mach., New York, pp 715–725