Identifying Important Places in People’s Lives from Cellular Network Data

Isaacman, Sibren; Becker, Richard A.; Cáceres, Ramón; Kobourov, Stephen G.; Martonosi, Margaret; Rowland, James; Varshavsky, Alexander

doi:10.1007/978-3-642-21726-5_9

Cited by 301 publications

(267 citation statements)

References 17 publications

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“…relevance. To solve the aforementioned issue, Isaacman et al [19] have proposed a technique based on clustering and regression to identify important places then assign them a semantic such as home and work. By contrast, Csaji et al [12] have combined principal component analysis with clustering to robustly identify home and work places.…”

Section: Related Workmentioning

confidence: 99%

“…In Figure 1 we report a small sample for each kind of recorded activity accompanied by a mobility trace that comes from combining the CDR entries. One of the advantages of this dataset with respect to other datasets [17,10,19,3,12] is the chance to leverage the Internet access data for purposes of mobility pattern analysis [4]. Although CDRs are rich sources for studying and analyzing human activities in different fields, they have two significant drawbacks as to providing location information.…”

Section: Call Detail Records Datasetsmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Call Detail Records Datasetsmentioning

confidence: 99%

On the properties of human mobility

Papandrea

Jahromi

Zignani

et al. 2016

Computer Communications

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show abstract

“…The preprocessing stage also excludes geotags related to users' residences and work places, because of the possible bias for the unvisited PoIs nearby these locations. The two locations can be identified by either recurring time-of-day patterns of each user [1], [2], or time-spending probability models [3], [4], [5].…”

Section: Inference Of Visited Poismentioning

confidence: 99%

“…The authors of [1], [3], [4] showed that having hold of even anonymized call record details enables inference of user identities as well as the users' most frequently visited locations. Moreover, the authors of [23], [5] shows that having even a vague knowledge of person's home or work addresses can identify the person and personal beliefs, preferences and behavioral aspects.…”

Section: Related Workmentioning

confidence: 99%

GeoEcho: Inferring User Interests from Geotag Reports in Network Traffic

Ning¹,

Miskovic²,

Baldi³

et al. 2014

2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)

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Abstract-Being transmitted as part of numerous Internet services, geo location data is increasingly bringing hints of people's real-world activities into Internet traffic. This paper focuses on the discovery of key properties that motivate personal activities -locational interests. We propose and design GeoEcho, a mobile traffic analysis system that extracts and analyses a wealth of latitude-longitude geotag reports with the purpose of identifying the points of interest (PoI) which people actually visit. The key challenge in such identification is that geotag reports are commonly sent arbitrarily, sparsely and without a sufficient accuracy to uniquely identify any PoI. In our analysis of a two-week trace from a large NorthAmerican cellphone operator, we show that 22% of geo reports do not even represent actual people's positions, while another 45% of the reports have low accuracy, such that they ambiguously indicate a number of potential PoIs. We devise methods that effectively identify and prune irrelevant geo information and infer personal interests of individuals. Thereby creating representative profiles of personal interests, our key results reveal that users show interest in a limited number of topics, and their interests are largely unique and stable over time. Our analysis shows a significant GeoEcho usability in various contexts ranging from generic user profile and user group analysis, to advertising and security applications.

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“…We attempt to automatically attach a label to invisible clusters by applying a simple time-of-day-based heuristic, similar to the one suggested in [2], in order to identify clusters that appear in places such as "home" or "work". To do so, we first aggregate the coverage of all clusters that insist on the same location.…”

Section: A Differential Cluster Analysismentioning

confidence: 99%

Discovering and predicting user routines by differential analysis of social network traces

Pianese¹,

An²,

Kawsar³

et al. 2013

2013 IEEE 14th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)

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Abstract-The study of human activity patterns traditionally relies on the continuous tracking of user location. We approach the problem of activity pattern discovery from a new perspective which is rapidly gaining attention. Instead of actively sampling increasing volumes of sensor data, we explore the participatory sensing potential of multiple mobile social networks, on which users often disclose information about their location and the venues they visit. In this paper, we present automated techniques for filtering, aggregating, and processing combined social networking traces with the goal of extracting descriptions of regularly-occurring user activities, which we refer to as "user routines". We report our findings based on two localized data sets about a single pool of users: the former contains public geotagged Twitter messages, the latter Foursquare check-ins that provide us with meaningful venue information about the locations we observe. We analyze and combine the two datasets to highlight their properties and show how the emergent features can enhance our understanding of users' daily schedule. Finally, we evaluate and discuss the potential of routine descriptions for predicting future user activity and location.

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Identifying Important Places in People’s Lives from Cellular Network Data

Cited by 301 publications

References 17 publications

On the properties of human mobility

On the properties of human mobility

GeoEcho: Inferring User Interests from Geotag Reports in Network Traffic

Discovering and predicting user routines by differential analysis of social network traces

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