Uncovering individual and collective human dynamics from mobile phone records

By Julián Candia, Marta C. González, Pu Wang, Timothy W. Schoenharl, Greg Madey, Albert-László Barabási. 2007

645 citations recorded by the source corpus.

Novel aspects of human dynamics and social interactions are investigated by means of mobile phone data. Using extensive phone records resolved in both time and space, we study the mean collective behavior at large scales and focus on the occurrence of anomalous events. We discuss how these spatiotemporal anomalies can be described using standard percolation theory tools. We also investigate patterns of calling activity at the individual level and show that the interevent time of consecutive calls is heavy-tailed. This finding, which has implications for dynamics of spreading phenomena in social networks, agrees with results previously reported on other human activities.

Subfields: Statistical and Nonlinear Physics, Transportation

View Paper: https://www.semanticscholar.org/paper/ce0144027fd0716af679cbbedbddabe90ba89973

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In Statistical and Nonlinear Physics

In Transportation

Citations in the corpus

A Poissonian explanation for heavy tails in e-mail communication (2008, background): “Other studies have identified a similar power-law scaling in the inter-event time distribution of m any other facets of human behavior, such as file downloads [10, 11, 12], letter correspondence [15, 17, 18], library usage [17], broker tra des [17], web browsing [17, 19], human locomotor activity [2”
Understanding the Spreading Patterns of Mobile Phone Viruses (2009, background): “Thus to quantitatively study the spreading dynamics of mobile viruses we need to simultaneously model the location (13), the mobility (14-17) and the communication patterns (18-21) of mobile phone users.”
Demographic research with non-representative internet data (2015, methodology): “Cellphone data have also been used to evaluate patterns and regularities of internal mobility, for a country, or movements within a relatively small region (Bayir et al., 2009; Gonzalez et al., 2008; Candia et al., 2008; Blumenstock, 2012; Deville et al., 2014).”
Activity-Aware Map: Identifying Human Daily Activity Pattern Using Mobile Phone Data (2010, background): “With the extensive records of mobile phone data such as calling pattern and location of the mobile phone user, analyses have been performed on numerous aspects including behavioral routine [7][8][9], social proximity [10][11], call prediction [12][13], social closeness [14][15], and human mobility [”
Inferring international and internal migration patterns from Twitter data (2014, methodology): “Cellphone data have been used mainly to evaluate patterns and regularities of internal mobility for a country [3, 13, 6, 5], but also ties between countries (in terms of international calls) [4].”
Universal features of correlated bursty behaviour (2011, methodology): “ie i denoted the average inter-event times, calculated separately for each group as it was done in [5, 6].”
Extracting Diurnal Patterns of Real World Activity from Social Media (2013, background): “Previous work examined, for example, cellphone use to understand spatio-temporal human dynamics (Candia et al. 2008), geo-coded image posting rate to learn about points of interest (Girardin et al. 2008; Pereira et al. 2011) and activity on Foursquare to model city dynamic structure and functional…”
A review of urban computing for mobile phone traces: current methods, challenges and opportunities (2013, background): “1 Exploration and Preferential Returns Several ubiquitous characteristics of individual human trajectories have been found [4, 15, 42, 5], most of which are using tower level cell phone records.”
Understanding traffic dynamics in cellular data networks (2011, methodology): “Much of our analysis has been motivated by Barabasi and co-authors’ influential work on this topic [6], [24].”
Urban Computing: Concepts, Methodologies, and Applications (2014, methodology): “For example, CDRs have been widely used in traf.c and human mobility modeling [Gonz ´ alez et al. 2008; Candia et al. 2012].”
Understanding Road Usage Patterns in Urban Areas (2012, background): “variety of problems. 2. Definition of trips and extraction of travel demands: The major challenge when estimating travel demands with mobile phone data is embedded in the sparse and irregular records (12), in which user displacements (consecutive different recorded locations) are usually observed be”