Improving time use measurement with personal big data collection -- the experience of the European Big Data Hackathon 2019

• URL: http://arxiv.org/abs/2004.11940v1
• Date: Fri, 24 Apr 2020 18:40:08 GMT
• Title: Improving time use measurement with personal big data collection -- the experience of the European Big Data Hackathon 2019
• Authors: Mattia Zeni, Ivano Bison, Britta Gauckler, Fernando Reis Fausto Giunchiglia
• Abstract summary: 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 allows to capture personal big data from smartphones' internal sensors to be used for time use measurement.
• Score: 62.997667081978825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 allows to capture 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 have insights on 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 arose during the survey.

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