You are where you eat: Foursquare checkins as indicators of human mobility and behaviour

Colombo, Gualtiero B.; Chorley, Martin; Williams, Matthew; Allen, Stuart M.; Whitaker, Roger M.

doi:10.1109/percomw.2012.6197483

Cited by 26 publications

(15 citation statements)

References 4 publications

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“…1(b) we report the histogram of the mean (over the week) number of ACs accessed by user in a day. In line with similar studies [2], [3], [4], [5], [6], most users are active in very few different locations/cells (mean: 6, median: 5), while a very small percentage of individuals show a higher mobility.…”

Section: A Cell-based Activity Patternssupporting

confidence: 89%

Selective Offload and Proactive Caching of Mobile Data in LTE-Based Urban Networks

Gaito

Maggiorini

Quadri

et al. 2013

2013 IEEE 14th International Conference on Mobile Data Management

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In this paper we focus on mobile data offloading and propose a solution for the placement of offloading infrastructures that optimizes the trade-off between reducing deployed resources and increasing traffic breakout. We base our analysis on a real dataset of Internet accesses generated, in the city of Milano, by some 50,000 users of an important mobile network operator. The target application we consider is the distribution of digital contents (such as MP3 songs, videos and newspapers) over an urban area. The paper's contributions is showing that offloading of digital contents can be achieved by leveraging people's regular mobility patterns.

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Section: A Cell-based Activity Patternssupporting

confidence: 89%

Selective Offload and Proactive Caching of Mobile Data in LTE-Based Urban Networks

Gaito

Maggiorini

Quadri

et al. 2013

2013 IEEE 14th International Conference on Mobile Data Management

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“…Human movement shows a high degree of temporal and spatial regularity, with each individual presenting a characteristic travel distance and a significant probability to return to a restricted number of highly frequented locations [10], [11], [12]. It has also been shown that it is possible to analyse mobility information for similarity and statistical characteristics that can be 2 http://mypersonality.org used to classify users according to their movements [13], [14] and predict aspects of their social or mobile interactions [15].…”

Section: Related Workmentioning

confidence: 99%

Visiting Patterns and Personality of Foursquare Users

Chorley

Colombo

Allen

et al. 2013

2013 International Conference on Cloud and Green Computing

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The study of personality can now provide us with greater insights into human behaviour, when combined with data from social and mobile networks. In this paper we study the relationship between the types of locations that Foursquare users visit and their personality. Using the five-factor personality model, personality profiles were obtained for 173 participating Foursquare users whose activities were analysed, representing a total of 474,539 check-ins across the globe. In this paper we present initial results concerning the number and types of venues and the relationship with personality traits. Particular correlations are identified concerning the openness, conscientiousness and neuroticism of participants and their levels of Foursquare activity. Extracting different categories of venues reveals variation in the correlation strength of personality traits of the users. As far as we are aware this is the first study of its type, linking human personality with spatial behaviour in mobile social networks.

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“…From GPS traces to Points of Interest GPS datasets, like the ones we are analyzing, present many difficulties concerning the PoIs extraction task as to the mobility data inferred from geo-coded or geo-tagged social networks [11] ( e.g. Foursquare, Facebook Places,.…”

Section: Appendix a Pre-processing And General Statisticsmentioning

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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You are where you eat: Foursquare checkins as indicators of human mobility and behaviour

Cited by 26 publications

References 4 publications

Selective Offload and Proactive Caching of Mobile Data in LTE-Based Urban Networks

Selective Offload and Proactive Caching of Mobile Data in LTE-Based Urban Networks

Visiting Patterns and Personality of Foursquare Users

On the properties of human mobility

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