Unveiling mobility complexity through complex network analysis

Social Network Analysis and Mining, Aug 2016

The availability of massive digital traces of individuals is offering a series of novel insights on the understanding of patterns characterizing human mobility. Many studies try to semantically enrich mobility data with annotations about human activities. However, these approaches either focus on places with high frequencies (e.g., home and work), or relay on background knowledge (e.g., public available points of interest). In this paper, we depart from the concept of frequency and we focus on a high level representation of mobility using network analytics. The visits of each driver to each systematic destination are modeled as links in a bipartite network where a set of nodes represents drivers and the other set represents places. We extract such network from two real datasets of human mobility based, respectively, on GPS and GSM data. We introduce the concept of mobility complexity of drivers and places as a ranking analysis over the nodes of these networks. In addition, by means of community discovery analysis, we differentiate subgroups of drivers and places according both to their homogeneity and to their mobility complexity.

A PDF file should load here. If you do not see its contents the file may be temporarily unavailable at the journal website or you do not have a PDF plug-in installed and enabled in your browser.

Alternatively, you can download the file locally and open with any standalone PDF reader:

https://link.springer.com/content/pdf/10.1007%2Fs13278-016-0369-2.pdf

Unveiling mobility complexity through complex network analysis

Social Network Analysis and Mining December 2016, 6:59 | Cite as Unveiling mobility complexity through complex network analysis AuthorsAuthors and affiliations Riccardo GuidottiAnna MonrealeSalvatore RinzivilloDino PedreschiFosca Giannotti Open Access Original Article First Online: 12 August 2016 1.3k Downloads 1 Citations Part of the following topical collections:ARS15 Abstract The availability of massive digital traces of individuals is offering a series of novel insights on the understanding of patterns characterizing human mobility. Many studies try to semantically enrich mobility data with annotations about human activities. However, these approaches either focus on places with high frequencies (e.g., home and work), or relay on background knowledge (e.g., public available points of interest). In this paper, we depart from the concept of frequency and we focus on a high level representation of mobility using network analytics. The visits of each driver to each systematic destination are modeled as links in a bipartite network where a set of nodes represents drivers and the other set represents places. We extract such network from two real datasets of human mobility based, respectively, on GPS and GSM data. We introduce the concept of mobility complexity of drivers and places as a ranking analysis over the nodes of these networks. In addition, by means of community discovery analysis, we differentiate subgroups of drivers and places according both to their homogeneity and to their mobility complexity. KeywordsMobility network Ranking Communities  1 Introduction One of the most fascinating challenges of our time is to understand the complexity of the global interconnected society and possibly to predict human behavior. A great part of human behavior is observable through individual movements, registered in many different layers: mobile phone network, GPS devices, social media applications, road sensors, credit card transactions, etc. Movement is the “hardware” of our daily life. We move to perform any activity: we have to move to bring children at school, to buy a new electronic device, to meet with colleagues at work, etc. If we understand the patterns of human movement, we can also comprehend the mechanics of human behavior. On the basis of this assumption, in the last years, we have witnessed many studies exploring movements data to understand different aspects related to the mobility of individuals, such as the density of traffic (Giannotti et al. 2011), the identification of systematic movements (Trasarti et al. 2011), the identification of groups of drivers following common routes (Monreale et al. 2009) and many others. On one hand, the movement is an objective phenomenon that can be observed, measured, and recorded easily with the modern ICT services. On the other hand, the intended activity of each movement is not always easy to sense and register. A common approach to better understand movement behavior consists into the study of the motivations that push an individual to move toward a given destination. There are proposals in the literature to semantically enrich movement data on the basis of movement dynamics and properties. For example, Jiang et al. (2012) tries to estimate home/work locations of an individual by analyzing the frequency she visits a particular place; Lafferty et al. (2001) observe a sequence of movements to derive the sequence of activities performed; Rinzivillo et al. (2014) extract a series of individual mobility network to learn structured patterns of visits to places; and Furletti et al. (2013) exploit the background knowledge of the points of interest (POIs) available in a territory to derive the activities of persons stopping nearby. In this paper, we propose an approach that can be considered as an intermediate step between the movement dynamics exploration and the semantic enrichment of movements. We start from the analysis of individual movements to understand the relevance of each destination. However, we are not interested in the specific activity a person is performing on her destination, rather we focus on the “relevance” that a specific destination has for the person. A well-known proverb says that “Home is where the Hearth is,” meaning that the home for an individual is not just a mere geographical place, but it represents a complex mixture of sensations, perceptions, and feelings linked to that place. It goes without saying that this kind of definition is strongly tied to a personal and subjective vision of that place. From the analytical point of view, it is difficult to measure this perception. The approaches based on semantic enrichment are focused either on places of general interest (like restaurants, shopping center) or on individual-based destinations (like home or work). Our proposal tries to fill this gap by starting from an individual ranking of personal places to generalize to collective relevance of destinations. (...truncated)


This is a preview of a remote PDF: https://link.springer.com/content/pdf/10.1007%2Fs13278-016-0369-2.pdf

Riccardo Guidotti, Anna Monreale, Salvatore Rinzivillo, Dino Pedreschi, Fosca Giannotti. Unveiling mobility complexity through complex network analysis, Social Network Analysis and Mining, 2016, pp. 59, Volume 6, Issue 1, DOI: 10.1007/s13278-016-0369-2