PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Giannotti F., Pappalardo L., Pedreschi D., Wang D. A complexity science perspective on human mobility. Chiara Renso, Stefano Spaccapietra, Esteban Zimányi (eds.). New York, USA: Cambridge University Press, 2013.
 
 
Abstract
(English)
Fueled by big data collected by a wide range of high-throughput tools and technologies, a new wave of data-driven, interdisciplinary science have rapidly proliferated during the past decade, impacting a wide array of disciplines, from physics and computer science to cell biology and economics. In particular, the ICT's are inundating us with huge amounts of information about human activities, o ering access to observing and measuring human behavior at an unprecedented level of details. The understanding of how objects move, and humans in particular, is a longstanding challenge in the natural sciences, since the seminal observations by Robert Brown in the 19th century, but it has attracted particular interest in recent years, due to the data availability and to the relevance of the topic in various domains, from urban planning and virus spreading to emergency response. A rst contribution of this chapter is to provide a brief account of this body of research, with a focus on the recent results on the empirical laws that govern the individual mobility patterns: we discuss how the key variables of people's travels (such as length, duration, radius of gyration, ...) follow universal laws, validated against di erent datasets of real observations. We also discuss how predictable people's movements are, illustrating recent ndings indicating that the high degree of predictability of human motion is a universal.
URL: http://www.cambridge.org/us/academic/subjects/computer-science/knowledge-management-databases-and-data-mining/mobility-data-modeling-management-and-understanding
Subject Human Mobility
Data Mining
Network Science
H.2.8 Database Applications. Data Mining


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