Photowalking the City: Comparing Hypotheses About Urban Photo Trails on Flickr

Understanding human movement trajectories represents an important problem that has implications for a range of societal challenges such as city planning and evolution, public transport or crime. In this paper, we focus on geo-temporal photo trails from four different cities (Berlin, London, Los Angeles, New York) derived from Flickr that are produced by humans when taking sequences of photos in urban areas. We apply a Bayesian approach called HypTrails to assess different explanations of how the trails are produced. Our results suggest that there are common processes underlying the photo trails observed across the studied cities. Furthermore, information extracted from social media, in the form of concepts and usage statistics from Wikipedia, allows for constructing explanations for human movement trajectories.

[1]  Ming-Syan Chen,et al.  Recommending personalized scenic itinerarywith geo-tagged photos , 2008, 2008 IEEE International Conference on Multimedia and Expo.

[2]  Kristina Lerman,et al.  Geography of Emotion: Where in a City are People Happier? , 2015, WWW.

[3]  Denis Helic,et al.  Detecting Memory and Structure in Human Navigation Patterns Using Markov Chain Models of Varying Order , 2014, PloS one.

[4]  Josep Blat,et al.  Leveraging explicitly disclosed location information to understand tourist dynamics: a case study , 2008, J. Locat. Based Serv..

[5]  Cong Yu,et al.  Automatic construction of travel itineraries using social breadcrumbs , 2010, HT '10.

[6]  Ed H. Chi,et al.  The scent of a site: a system for analyzing and predicting information scent, usage, and usability of a Web site , 2000, CHI.

[7]  Mor Naaman,et al.  HT06, tagging paper, taxonomy, Flickr, academic article, to read , 2006, HYPERTEXT '06.

[8]  Josep Blat,et al.  Digital Footprinting: Uncovering Tourists with User-Generated Content , 2008, IEEE Pervasive Computing.

[9]  Mark Levene,et al.  Evaluating Variable-Length Markov Chain Models for Analysis of User Web Navigation Sessions , 2007, IEEE Transactions on Knowledge and Data Engineering.

[10]  James E. Pitkow,et al.  Characterizing Browsing Strategies in the World-Wide Web , 1995, Comput. Networks ISDN Syst..

[11]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[12]  Mark Hansen,et al.  Predicting Web Users' Next Access Based on Log Data , 2003 .

[13]  Daniele Quercia,et al.  Partisan sharing: facebook evidence and societal consequences , 2014, COSN '14.

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

[15]  Jens Lehmann,et al.  DBpedia - A large-scale, multilingual knowledge base extracted from Wikipedia , 2015, Semantic Web.

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

[17]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

[18]  James E. Pitkow,et al.  Characterizing Browsing Behaviors on the World-Wide Web , 1995 .

[19]  L. Wasserman,et al.  Computing Bayes Factors by Combining Simulation and Asymptotic Approximations , 1997 .

[20]  Pietro Liò,et al.  Collective Human Mobility Pattern from Taxi Trips in Urban Area , 2012, PloS one.

[21]  R. Sinnott Virtues of the Haversine , 1984 .

[22]  Roelof van Zwol,et al.  Flickr tag recommendation based on collective knowledge , 2008, WWW.

[23]  Ryen W. White,et al.  Assessing the scenic route: measuring the value of search trails in web logs , 2010, SIGIR.

[24]  Krishna P. Gummadi,et al.  A measurement-driven analysis of information propagation in the flickr social network , 2009, WWW '09.

[25]  Ed H. Chi,et al.  Using information scent to model user information needs and actions and the Web , 2001, CHI.

[26]  Markus Strohmaier,et al.  Sequential Action Patterns in Collaborative Ontology-Engineering Projects: A Case-Study in the Biomedical Domain , 2014, CIKM.

[27]  Jure Leskovec,et al.  Human wayfinding in information networks , 2012, WWW.

[28]  J. Fortenberry,et al.  International Journal of Health Geographics Open Access Using Gps-enabled Cell Phones to Track the Travel Patterns of Adolescents , 2022 .

[29]  Krishna P. Gummadi,et al.  Growth of the flickr social network , 2008, WOSN '08.

[30]  Jon M. Kleinberg,et al.  Mapping the world's photos , 2009, WWW '09.

[31]  Cecilia Mascolo,et al.  Topological Properties and Temporal Dynamics of Place Networks in Urban Environments , 2015, WWW.

[32]  Carlo Ratti,et al.  Geo-located Twitter as proxy for global mobility patterns , 2013, Cartography and geographic information science.

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

[34]  Peter Pirolli,et al.  Distributions of surfers' paths through the World Wide Web: Empirical characterizations , 1999, World Wide Web.

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

[36]  A. Hotho,et al.  HypTrails: A Bayesian Approach for Comparing Hypotheses About Human Trails on the Web , 2014, WWW.

[37]  Huberman,et al.  Strong regularities in world wide web surfing , 1998, Science.

[38]  Jun Rekimoto,et al.  LifeTag: WiFi-Based Continuous Location Logging for Life Pattern Analysis , 2007, LoCA.

[39]  Fabian M. Suchanek,et al.  YAGO3: A Knowledge Base from Multilingual Wikipedias , 2015, CIDR.

[40]  Markus Strohmaier,et al.  Understanding How Users Edit Ontologies: Comparing Hypotheses About Four Real-World Projects , 2015, International Semantic Web Conference.

[41]  Rafael E. Banchs,et al.  Bicycle cycles and mobility patterns - Exploring and characterizing data from a community bicycle program , 2008, ArXiv.