Abstract
This article assesses the experience with i-Log at the European Big Data Hackathon 2019, a satellite event of the New Techniques and Technologies for Statistics (NTTS) conference, organised by Eurostat. i-Log is a system that enables capturing personal big data from smartphones’ internal sensors to be used for time use measurement. It allows the collection of heterogeneous types of data, enabling new possibilities for sociological urban field studies. Sensor data such as those related to the location or the movements of the user can be used to investigate and gain insights into the time diaries’ answers and assess their overall quality. The key idea is that the users’ answers are used to train machine-learning algorithms, allowing the system to learn from the user’s habits and to generate new time diaries’ answers. In turn, these new labels can be used to assess the quality of existing ones, or to fill the gaps when the user does not provide an answer. The aim of this paper is to introduce the pilot study, the i-Log system and the methodological evidence that emerged during the survey.
References
1.
Barney, D. 2004. The Network Society . Cambridge: Polity Press.
2.
Bison, I., and A. Scalcon. 2018. From 07.00 to 22.00: A Dual-Earner Couple’s Typical Day in Italy: Old Questions and New Evidence from Social Sequence Analysis . Edited by G. Ritschard, and M. Studer. Sequence Analysis and Related Approaches.
3.
Bison, I., M. Zeni, M. Busso, E. Bignotti, F. Giunchiglia, and G. Veltri. 2018. “ More Than Meets the Eyes: Complementing Surveys with Mobile Phone Digital Data Trail.” ESRA BigSurv18 Conference. Available at: https://www.bigsurv18.org/program2018?sess=52#222 (accessed May 2021).
4.
Castells, M. 2000. The Rise of the Network Society . The Information Age: Economy, Society and Culture. Volume I (2nd ed.). Oxford: Wiley-Blackwell.
5.
Chenu, A, and L. Lesnard. 2006. “Time Use Surveys: a Review of their Aims, Methods, and Results.” Archives Européennes de Sociologie / European Journal of Sociology , Cambridge University Press (CUP), 47(3): 335–359. Available at: http://www.jstor.org/stable/23998949 (accessed May 2021).
6.
De Montjoye, Y.A., C.A. Hidalgo, M. Verleysen, and V.D. Blondel. 2013. “Unique in the crowd: The privacy bounds of human mobility.” Scientific Reports 3: 1376. DOI: https://doi.org/10.1038/srep01376.
7.
Diou, C., I. Ioakeimidis, E. Charmandari, P. Kassari, I. Lekka, M. Mars, C. Bergh, T. Kechadi, G. Doyle, G.K. O’Malley G, Heimeier R, Lindroos AK, S. Sotiriou, E. Koukoula, S. Guillén, G. Lymperopoulos, N. Maglaveras, and A. Delopoulos. 2018. “BigO: Big Data Against Childhood Obesity.” European Society for Paediatric Endocrinology Vol. 89. Presented at: 57th Annual European Society of Pediatric Endocrinology; 27–29 September 2018; Athens, Greece. Available at: https://abstracts.eurospe.org/hrp/0089/hrp0089p3-p127.
8.
Dumazedier, J. 1975. “The Use of Time. Daily activities of urban and suburban population in twelve countries.” Edited by A. Szalai. Revue française de sociologie 16 no. 1: 125–129. DOI: https://doi.org/10.1080/00222216.1974.11970168.
9.
ESSC (European Statistical System Committee). 2013. “Scheveningen Memorandum-Big Data and Official Statistics.” Adopted 27 September 2013. Luxembourg. Available at: http://ec.europa.eu/eurostat/documents/42577/43315/Scheveningenmemorandum-27-09-13 (accessed March 2016).
10.
ESSC (European Statistical System Committee). 2018. “Bucharest memorandum on Official Statistics in a datafied society (Trusted Smart Statistics).” DGINS Conference. Available at: https://ec.europa.eu/eurostat/web/ess/-/dgins2018-bucharest-memorandum-adopted (accessed May 2121).
11.
Eurostat. 2009. Harmonized European time use surveys, Guidelines 2008. Methodologies and Working Papers. Luxembourg: Office for Official Publications of the European Communities. Available at: https://ec.europa.eu/eurostat/documents/3859598/5909673/KS-RA-08-014-EN.PDF.pdf/a745ca2e-7dc6-48a9-a36c-000ad120380e?t=1414781526000 (accessed May 2121).
12.
Eurostat. 2019a. Harmonised European Time Use Surveys (HETUS) – 2018 Guidelines. Eurostat Manuals and Guidelines. Available at: https://ec.europa.eu/eurostat/documents/3859598/9710775/KSGQ-19-003-EN-N.pdf/ee48c0bd-7287-411a-86b6-fb0f6d5068cc?t=1554468617000 (accessed 20 May 2121).
13.
Eurostat. 2019b. European Big Data Hackathon. Available at: https://ec.europa.eu/eurostat/cros/system/files/european_big_data_hackathon_2019_-_description_20181119.pdf (accessed April 2020).
14.
Fernee, H., N. Sonck, and A. Scherpenzeel. 2013. “Data Collection with Smartphones: Experiences in a Time Use Survey.” NTTS-Conferences on New Techniques and Technologies for Statistics. Brussels, 5–7 March 2013;868-875 Available at: https://ec.europa.eu/eurostat/cros/system/files/NTTS2013%20Proceedings_0.pdf.
15.
Fernee, H., and N. Sonck. 2014. “Measuring Smarter – Time-Use Data Collected by Smartphones.” International Journal of Time Use Research 11(1): 94–111. DOI: https://dx.doi.org/10.13085/eIJTUR.11.1.94-111.
16.
Gershuny, J. 2015. Time Use Research Methods . International Encyclopedia of the Social and Behavioral Sciences (Second Edition) 24.
17.
Gershuny J., and O. Sullivan. 2019. What We Really Do All Day: Insights from the Centre for Time Use Research . Penguin Books Ltd.
18.
Giunchiglia, F., M. Zeni, E. Gobbi, E. Bignotti, and I. Bison. 2017. “Mobile Social Media and Academic Performance.” The 9th International Conference on Social Informatics (SocInfo 2017), September, 2017. Oxford, UK. DOI: https://doi.org/10.1007/978-3-319-67256-4_1.
19.
Giunchiglia, F., M. Zeni, E. Gobbi, E. Bignotti, and I. Bison. 2018. “Mobile social media usage and academic performance.” Computers in Human Behavior 82: 177–185. DOI: https://doi.org/10.1016/j.chb.2017.12.041.
20.
Groves, R.M. 2011. “Three Eras of Survey Research.” Public Opinion Quarterly 75(5): 861–71. DOI: http://doi.org/10.1093/poq/nfr057.
21.
GSM Association. 2018. The Mobile Economy Europe 2018 . Available at: https://www.gsmaintelligence.com/research/?file=884c77f3bc0a405b2d5fd356689be340anddownload (accessed April 2020).
22.
Hatuka, T., and E. Toch. 2017. “Being visible in public space: The normalisation of asymmetrical visibility.” Urban Studies 54(4): 984–998. DOI: https://doi.dox.-org/10.1177/0042098015624384.
23.
Hellgren, M. 2014. “Extracting More Knowledge from Time Diaries?” Social Indicators Research 119(3): 1517–1534. DOI: https://doi.org/10.1007/s11205-013-0558-6.
24.
Hewitt, Eben. 2010 Cassandra: the definitive guide . O’Reilly Media, Inc.
25.
i-Log. 2019. i-Log on the Google Play Store. Available at: https://play.google.com/store/apps/details?id=it.unitn.disi.witmee.sensorlog (accessed April 2020).
26.
Juster, F., and F. Stafford. 1991. “The Allocation of Time: Empirical Findings, Behavioral Models, and Problems of Measurement.” Journal of Economic Literature 29(2): 471–522. Available at: http://www.jstor.org/stable/2727521 (accessed May 2021).
27.
Juster, F., H. Ono, and F. Stafford. 2004. Changing Times of American Youth: 1981–2003 . Institute for Social Research. University of Michigan. Available at: http://ns.umich.edu/Releases/2004/Nov04/teen_time_report.pdf (accessed May 2021).
28.
Kramarczyk, J. 2015. “Spending Time on Media – Results of Using Multitasking Frequency Questionnaire In Poland.” International Journal of Time Use Research 12(1): 153–190. DOI: https://doi.org/10.13085/eIJTUR.10.1.153-190.
29.
Kramarczyk, J., and M. Osowiecka. 2014. “Time is Running Differently on the Internet.” International Journal of Time Use Research 11(1): 94-111. DOI: http://doi.org//10.13085/eIJTUR.11.1.94-111.
30.
Link M.W., J. Murphy, M.F. Schober, T.D. Buskirk, J. Hunter Childs, T. Casey Langer. 2014. “Mobile Technologies for Conducting, Augmenting and Potentially Replacing Surveys: Executive Summary of the AAPOR Task Force on Emerging Technologies in Public Opinion Research.” Public Opinion Quarterly 78: 779–87.
31.
Link, M. 2018. “New data strategies: nonprobability sampling, mobile, big data.” Quality Assurance in Education 26(2): 303–314. DOI: https://doi.org/10.1108/QAE-06-2017-0029.
32.
Maddalena, E., L.-D. Ibáñez, E. Simperl, R. Gomer, M. Zeni, D. Song, and F. Giunchiglia. 2019. “Hybrid Human Machine workflows for mobility management.” In Companion Proceedings of The 2019 World Wide Web Conference (WWW ’19). May 2019, San Francisco, USA: 102–109. DOI: https://doi.org/10.1145/3308560.3317056.
33.
Merz J. 2009. Time use and time budgets: Improvements, future challenges and recommendations . Society for the Study of Economic Inequality ECINEQ 125.
34.
Miller, G.A. 1998. WordNet: An electronic lexical database . MIT press.
35.
Minnen, J., J. Verbeylen, and I. Glorieux. 2018. Onderzoek naar de tijdsbesteding van leraren in het basis- en secundair onderwijs. Deel 1: Algemeen. (Time allocation of teachers in the primary and secondary school. Part 1: General) . Vlaamse Overheid, Brussel: Vakgroep Sociologie, Onderzoeksgroep TOR 57 blz.
36.
Robinson, J.P. 1999. The Time-Diary Method: Structure and Uses. In Time Use Research in the Social Sciences . New York: Academic/Plenum Publishers.
37.
Robinson, J.P. 2002. “The time-diary method.” Time use research in the social sciences : 47–89. DOI: https://doi.org/10.1007/0-306-47155-8_3.
38.
Runyan, J.D., T.A. Steenbergh, C. Bainbridge, D.A. Daugherty, L. Oke, and B.N. Fry. 2013. “A smartphone ecological momentary assessment/intervention ‘app’ for collecting real-time data and promoting self-awareness.” PLoS One 8(8). DOI: https://doi.org/10.1371/journal.pone.0071325.
39.
Sorokin, P.A., and C.Q. Berger. 1939. Time-budgets of human behavior . Harvard University Press
40.
Stella C., K. Fisher, E. Gilbert, L. Calderwood, T. Huskinson, A. Cleary, and J. Gershuny. 2018. “Using new technologies for time diary data collection: Instrument design and data quality findings from a mixed-mode pilot survey.” Social Indicators Research 137(1): 379–390. DOI: https://doi.org/10.1007/s11205-017-1569-5.
41.
Sugie, N.F. 2018. “Utilizing Smartphones to Study Disadvantaged and Hard-to-Reach Groups.” Sociological Methods and Research 47(3): 458 – 491. DOI: https://doi.org/10.1177/0049124115626176.
42.
The Nielsen Company. 2018. The Nielsen Total Audience Report Q1 2018 . Available at: https://www.nielsen.com/content/dam/corporate/us/en/reports-downloads/2018-reports/q1-2018-total-audience-report.pdf (accessed April 2020).
43.
United Nations. 2010. “In-depth review on time-use surveys, Economic Commission for Europe.” Conference of European Statisticians, Note by the German Federal Statistical Office. ECE/CES/2010/25. Paris, France, 2018. Available at: http://unstats.un.org/unsd/demographic/sconcerns/tuse/ (accessed April 2020).
44.
Vilhelmson, B., E. Elldèr, and E. Thulin. 2018. “What did we do when the Internet wasn’t around? Variation in free-time activities among three young-adult cohorts from 1990/1991, 2000/2001, and 2010/2011.” New Media and Society 20(8): 2898–2916. DOI: https://doi.org/10.1177/1461444817737296.
45.
Vohra D. 2016. “Practical Hadoop Ecosystem.” Chapter in Apache Parquet : chap. 8. Apress. DOI: https://doi.org/10.1007/978-1-4842-2199-0_8.
46.
Wang, X.H., D.Q. Zhang, T. Gu, and H.K. Pung. 2004. “Ontology based context modeling and reasoning using OWL.” In IEEE annual conference on pervasive computing and communications workshops, March, 2004. 18-22. Orlando, FL, USA. DOI: https://doi.org/10.1109/PERCOMW.2004.1276898.
47.
Zaharia, M., R.S. Xin, P. Wendell, T. Das, M. Armbrust, A. Dave, X. Meng, J.Rosen, S. Venkataraman, and M.J. Franklin. 2016. “Apache spark: a unified engine for big data processing.” Communications of the ACM 59(11): 56–65. DOI: https://doi.org/10.1145/2934664.
48.
Zeni, M., I. Zaihrayeu, and F. Giunchiglia. 2014. “Multi-device activity logging.” ACM International Joint Conference on Pervasive and Ubiquitous Computing. September 13–17, 2014. 299–302. Seattle, WA, USA. DOI: http://dx.doi.org/10.1145/2638728.2638756/.
49.
Zeni, M. 2017. Bridging Sensor Data Streams and Human Knowledge . Trento: University of Trento. Available at: http://eprints-phd.biblio.unitn.it/2724/ (accessed April 2020).
50.
Zeni, M., W. Zhang, E. Bignotti, A. Passerini, and F. Giunchiglia. 2019. “Fixing Mislabeling by Human Annotators Leveraging Conflict Resolution and Prior Knowledge.” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3(1): 1–23. DOI: https://doi.org/10.1145/3314419.