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Author Centellegher, Simone ♦ Nadai, Marco De ♦ Caraviello, Michele ♦ Leonardi, Chiara ♦ Vescovi, Michele ♦ Ramadian, Yusi ♦ Oliver, Nuria ♦ Pianesi, Fabio ♦ Pentland, Alex ♦ Antonelli, Fabrizio ♦ Lepri, Bruno
Source Paperity
Content type Text
Publisher Springer Berlin Heidelberg
File Format PDF ♦ HTM / HTML
Copyright Year ©2016
Abstract The exploration of people’s everyday life has long been of interest to social scientists. Recent years have witnessed a growing interest in analyzing human behavioral data generated by technology (e.g. mobile phones). To date, a few large-scale studies have been designed to measure human behaviors and interactions using multiple sources of data. A common characteristic of these studies is the population under investigation: students having similar daily routines and needs. This choice constraints the range of behaviors, of places and the generalization of the results. In order to widen this line of studies, we focus on a different target group: parents with young children aged 0 through 10 years. Children influence multiple aspects of their parents’ lives, from the satisfaction of basic human needs and the fulfillment of social roles to their financial status and sleep quality. In this paper, we describe the Mobile Territorial Lab (MTL) project, a longitudinal living lab which has been sensing by means of technology (mobile phones) the lives of more than 100 parents in different areas of the Trentino region in Northern Italy. We present the preliminary results after two years of experimentation of, to the best of our knowledge, the most complete picture of parents’ daily lives. Through the collection and analysis of the collected data, we created a multi-layered view of the participants’ lives, tracking social interactions, mobility routines, spending patterns, and personality characteristics. Overall, our results prove the relevance of living lab approaches to measure human behaviors and interactions, which can pave the way to new studies exploiting a richer number of behavioral indicators. Moreover, we believe that the proposed methodology and the collected data could be very valuable for researchers from different disciplines such as social psychology, sociology, computer science, economy, etc., which are interested in understanding human behaviour.
Learning Resource Type Article
Publisher Date 2016-12-01
Journal EPJ Data Science
Volume Number 5
Issue Number 1