On the validity of geosocial mobility traces

Mobile networking researchers have long searched for large-scale, fine-grained traces of human movement, which have remained elusive for both privacy and logistical reasons. Recently, researchers have begun to focus on geosocial mobility traces, e.g. Foursquare checkin traces, because of their availability and scale. But are we conceding correctness in our zeal for data? In this paper, we take initial steps towards quantifying the value of geosocial datasets using a large ground truth dataset gathered from a user study. By comparing GPS traces against Foursquare checkins, we find that a large portion of visited locations is missing from checkins, and most checkin events are either forged or superfluous events. We characterize extraneous checkins, describe possible techniques for their detection, and show that both extraneous and missing checkins introduce significant errors into applications driven by these traces.

[1]  Cecilia Mascolo,et al.  A Tale of Many Cities: Universal Patterns in Human Urban Mobility , 2011, PloS one.

[2]  Kyunghan Lee,et al.  On the Levy-Walk Nature of Human Mobility , 2008, IEEE INFOCOM 2008 - The 27th Conference on Computer Communications.

[3]  Cecilia Mascolo,et al.  Track globally, deliver locally: improving content delivery networks by tracking geographic social cascades , 2011, WWW.

[4]  Xing Xie,et al.  Mining interesting locations and travel sequences from GPS trajectories , 2009, WWW '09.

[5]  David A. Maltz,et al.  Dynamic Source Routing in Ad Hoc Wireless Networks , 1994, Mobidata.

[6]  Cecilia Mascolo,et al.  NextPlace: A Spatio-temporal Prediction Framework for Pervasive Systems , 2011, Pervasive.

[7]  Cecilia Mascolo,et al.  Mining User Mobility Features for Next Place Prediction in Location-Based Services , 2012, 2012 IEEE 12th International Conference on Data Mining.

[8]  Cecilia Mascolo,et al.  An Empirical Study of Geographic User Activity Patterns in Foursquare , 2011, ICWSM.

[9]  Magdalena Balazinska,et al.  Characterizing mobility and network usage in a corporate wireless local-area network , 2003, MobiSys '03.

[10]  Daniel Gatica-Perez,et al.  Discovering places of interest in everyday life from smartphone data , 2011, Multimedia Tools and Applications.

[11]  A. Helmy,et al.  Empirical modeling of campus-wide pedestrian mobility observations on the USC campus , 2004, IEEE 60th Vehicular Technology Conference, 2004. VTC2004-Fall. 2004.

[12]  Mingyan Liu,et al.  Building realistic mobility models from coarse-grained traces , 2006, MobiSys '06.

[13]  Pietro Michiardi,et al.  Characterizing user mobility in second life , 2008, WOSN '08.

[14]  Ian F. Akyildiz,et al.  Effects of Different Mobility Models on Traffic Patterns in Wireless Sensor Networks , 2010, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010.

[15]  Kyumin Lee,et al.  Exploring Millions of Footprints in Location Sharing Services , 2011, ICWSM.

[16]  Cecilia Mascolo,et al.  Evolution of a location-based online social network: analysis and models , 2012, IMC '12.

[17]  Thomas R. Gross,et al.  A mobility model based on WLAN traces and its validation , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[18]  Jure Leskovec,et al.  Friendship and mobility: user movement in location-based social networks , 2011, KDD.

[19]  Yasir Saleem,et al.  Network Simulator NS-2 , 2015 .

[20]  John Zimmerman,et al.  I'm the mayor of my house: examining why people use foursquare - a social-driven location sharing application , 2011, CHI.

[21]  Shashi Shekhar,et al.  Discovering personally meaningful places: An interactive clustering approach , 2007, TOIS.

[22]  Ahmed Helmy,et al.  The IMPORTANT framework for analyzing the Impact of Mobility on Performance Of RouTing protocols for Adhoc NeTworks , 2003, Ad Hoc Networks.

[23]  Jussara M. Almeida,et al.  Detection of spam tipping behaviour on foursquare , 2013, WWW.

[24]  Kevin C. Almeroth,et al.  Towards realistic mobility models for mobile ad hoc networks , 2003, MobiCom '03.

[25]  Hui Zang,et al.  Are call detail records biased for sampling human mobility? , 2012, MOCO.

[26]  Deborah Estrin,et al.  SensLoc: sensing everyday places and paths using less energy , 2010, SenSys '10.

[27]  Albert-László Barabási,et al.  Limits of Predictability in Human Mobility , 2010, Science.

[28]  Paul A. Zandbergen,et al.  Accuracy of iPhone Locations: A Comparison of Assisted GPS, WiFi and Cellular Positioning , 2009 .

[29]  Cecilia Mascolo,et al.  Exploiting place features in link prediction on location-based social networks , 2011, KDD.

[30]  Sunny Consolvo,et al.  Learning and Recognizing the Places We Go , 2005, UbiComp.

[31]  Kentaro Toyama,et al.  Project Lachesis: Parsing and Modeling Location Histories , 2004, GIScience.

[32]  Henriette Cramer,et al.  Performing a check-in: emerging practices, norms and 'conflicts' in location-sharing using foursquare , 2011, Mobile HCI.