A Tale of Many Cities: Universal Patterns in Human Urban Mobility

Noulas, Anastasios; Scellato, Salvatore; Lambiotte, Renaud; Pontil, Massimiliano; Mascolo, Cecilia

doi:10.1371/journal.pone.0037027

Cited by 517 publications

(452 citation statements)

References 33 publications

(51 reference statements)

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“…Work such as [3] provides an analysis of tracking the mobility of phone users to show that humans do not have random trajectories but have both temporal and spatial regularities. Further researches by [12] and [13] experiment with data from different scales and datasets from various metropolitan areas, and show that different cities can have variances in human mobility patterns. A recent work by [4] showed that call data records can be organized into profiles and clustered using the spatiotemporal usage characteristics of each profile, allowing for accurate prediction and possible adaptation of the network according to the usage dynamics.…”

Section: Related Workmentioning

confidence: 99%

Characterizing and predicting mobile application usage

Lim

Secci

Tabourier

et al. 2016

Computer Communications

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In this paper, we propose data clustering techniques to predict temporal characteristics of data consumption behavior of different mobile applications via wireless communications. While most of the research on mobile data analytics focuses on the analysis of call data records and mobility traces, our analysis concentrates on mobile application usages, to characterize them and predict their behavior. We exploit mobile application usage logs provided by a Wi-Fi local area network service provider to characterize temporal behavior of mobile applications. More specifically, we generate daily profiles of "what" types of mobile applications users access and "when" users access them. From these profiles, we create usage classes of mobile applications via aggregation of similar profiles depending on data consumption rate, using three clustering techniques that we compare. Furthermore, we show that we can utilize these classes to analyze and predict future usages of each mobile application through progressive comparison using distance and similarity comparison techniques. Finally, we also detect and exploit outlying behavior in application usage profiles and discuss methods to efficiently predict them.

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Section: Related Workmentioning

confidence: 99%

Characterizing and predicting mobile application usage

Lim

Secci

Tabourier

et al. 2016

Computer Communications

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“…[16] study the spatio-temporal properties of users activities as captured through the inter-checkin times and the inter-checkin distances. They further identify universal features for human urban mobility [15]. In alignment, Cho et al [2] use cell phone location and LBSN data to understand the laws dictating human mobility.…”

Section: Related Workmentioning

confidence: 99%

Location Affiliation Networks: Bonding Social and Spatial Information

Pelechrinis

Krishnamurthy

2012

Machine Learning and Knowledge Discovery in Databases

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Abstract. Location-based social networks (LBSNs) have recently attracted a lot of attention due to the number of novel services they can offer. Prior work on analysis of LBSNs has mainly focused on the social part of these systems. Even though it is important to know how different the structure of the social graph of an LBSN is as compared to the friendship-based social networks (SNs), it raises the interesting question of what kinds of linkages exist between locations and friendships. The main problem we are investigating is to identify such connections between the social and the spatial planes of an LBSN. In particular, in this paper we focus on answering the following general question "What are the bonds between the social and spatial information in an LBSN and what are the metrics that can reveal them?" In order to tackle this problem, we employ the idea of affiliation networks. Analyzing a dataset from a specific LBSN (Gowalla), we make two main interesting observations; (i) the social network exhibits signs of homophily with regards to the "places/venues" visited by the users, and (ii) the "nature" of the visited venues that are common to users is powerful and informative in revealing the social/spatial linkages. We further show that the "entropy" (or diversity) of a venue can be used to better connect spatial information with the existing social relations. The entropy records the diversity of a venue and requires only location history of users (it does not need temporal history). Finally, we provide a simple application of our findings for predicting existing friendship relations based on users' historic spatial information. We show that even with simple unsupervised learning models we can achieve significant improvement in prediction when we consider features that capture the "nature" of the venue as compared to the case where only apparent properties of the location history are used (e.g., number of common visits).

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“…In fact Noulas et al [27] focused on human mobility patterns in a large number of cities. Mobility data have been retrieved from mobile location-based social services.…”

Section: Related Workmentioning

confidence: 99%

On the properties of human mobility

Papandrea

Jahromi

Zignani

et al. 2016

Computer Communications

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The current age of increased people mobility calls for a better understanding of how people move: how many places does an individual commonly visit, what are the semantics of these places, and how do people get from one place to another. We show that the number of places visited by each person (Points of Interest -PoIs) is regulated by some properties that are statistically similar among individuals. Subsequently, we present a PoIs classification in terms of their relevance on a per-user basis. In addition to the PoIs relevance, we also investigate the variables that describe the travel rules among PoIs in particular, the spatial and temporal distance. As regards the latter, existing works on mobility are mainly based on spatial distance. Here we argue, rather, that for human mobility the temporal distance and the PoIs relevance are the major driving factors. Moreover, we study the semantic of PoIs. This is useful for deriving statistics on people's habits without breaking their privacy. With the support of different datasets, our paper provides an in-depth analysis of PoIs distribution and semantics; it also shows that our results hold independently of the nature of the dataset in use. We illustrate that our approach is able to effectively extract a rich set of features describing human mobility and we argue that this can be seminal to novel mobility research.

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A Tale of Many Cities: Universal Patterns in Human Urban Mobility

Cited by 517 publications

References 33 publications

Characterizing and predicting mobile application usage

Characterizing and predicting mobile application usage

Location Affiliation Networks: Bonding Social and Spatial Information

On the properties of human mobility

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