<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Improving Time Use Measurement with Personal Big Data Collection – The Experience of the European Big Data Hackathon 2019

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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...

Improving Time Use Measurement with Personal Big Data Collection – The Experience of the European Big Data Hackathon 2019

Data publikacji: 22 cze 2021

Zakres stron: 341 - 365

Otrzymano: 01 cze 2019

Przyjęty: 01 lip 2020

DOI: https://doi.org/10.2478/jos-2021-0015

Słowa kluczowe
time use survey, big data, ubiquitous computing, smartphones, smart surveys

© 2021 Mattia Zeni et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Improving Time Use Measurement with Personal Big Data Collection – The Experience of the European Big Data Hackathon 2019

Data publikacji: 22 cze 2021

Zakres stron: 341 - 365

Otrzymano: 01 cze 2019

Przyjęty: 01 lip 2020

DOI: https://doi.org/10.2478/jos-2021-0015

Słowa kluczowetime use survey, big data, ubiquitous computing, smartphones, smart surveys

© 2021 Mattia Zeni et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
time use survey, big data, ubiquitous computing, smartphones, smart surveys