Analyzing urban mobility paths based on users' activity in social networks

Abstract This work presents an approach to model how the activity in social media of the citizens reflects the activity in the city. The proposal includes a gravitational model that deforms the surface of the city based on the intensity of the activity in different zones. The information is extracted from geolocated tweets ( n = 1 . 48 × 1 0 6 ). Furthermore, this activity affects how people move in a city. The path a user follows is calculated using the geolocation of the tweets that he or she publishes along the day. Several models are evaluated and compared using the Hausdorf’s distance ( d H ). The combination of gravitational potential with attraction to the destination points provides the best results, with d H = 1176 against the Manhattan ( d H = 1203 ) or the geodesic ( d H = 1417 ) alternatives. Finally, the analysis is repeated with the data segmented by gender (n=2,826 paths, men=1,910, women=916). The results validate (p=0.000334) the studies that affirm that men travel longer distances ( d M = 4 . 73 km, α M = 26 . 1 ° ) with rectilinear trajectories, whereas women have shorter and more angled paths ( d W = 4 . 5 km, α W = 32 . 2 ° ), obtaining p values in path lengths and p=0.006 in the angles.

[1]  Vuk Bogdanović,et al.  Gender and age differences in the travel behavior - a Novi Sad case study , 2016 .

[2]  P. Gordon,et al.  Gender Differences in Metropolitan Travel Behaviour , 1989 .

[3]  Javier Vázquez-Salceda,et al.  Discovery of Spatio-Temporal Patterns from Location Based Social Networks , 2014, CCIA.

[4]  A. Kölzsch,et al.  Segmenting Trajectories by Movement States , 2013 .

[5]  Marco Luca Sbodio,et al.  AllAboard: A System for Exploring Urban Mobility and Optimizing Public Transport Using Cellphone Data , 2013, ECML/PKDD.

[6]  G. Zipf.  The P 1 P 2 D Hypothesis: On the Intercity Movement of Persons , 1946 .

[7]  Pere Colet,et al.  Field theory for recurrent mobility , 2019, Nature Communications.

[8]  Injong Rhee,et al.  On the levy-walk nature of human mobility , 2011, TNET.

[9]  Günther Sagl,et al.  A Visual Analytics Approach for Extracting Spatio-Temporal Urban Mobility Information from Mobile Network Traffic , 2012, ISPRS Int. J. Geo Inf..

[10]  Vicent J. Botti,et al.  Using Genetic Algorithms to Optimize the Location of Electric Vehicle Charging Stations , 2018, SOCO-CISIS-ICEUTE.

[11]  Emilio Frazzoli,et al.  A review of urban computing for mobile phone traces: current methods, challenges and opportunities , 2013, UrbComp '13.

[12]  Zhongwei Li,et al.  Traffic-Known Urban Vehicular Route Prediction Based on Partial Mobility Patterns , 2009, 2009 15th International Conference on Parallel and Distributed Systems.

[13]  J. Gómez-Gardeñes,et al.  Critical regimes driven by recurrent mobility patterns of reaction–diffusion processes in networks , 2018 .

[14]  Elena Zucchini.  Género y transporte: análisis de la movilidad del cuidado como punto de partida para construir una base de conocimiento más amplia de los patrones de movilidad. El caso de Madrid , 2016 .

[15]  Christian Schneider,et al.  Spatiotemporal Patterns of Urban Human Mobility , 2012, Journal of Statistical Physics.

[16]  H. Stolum.  River Meandering as a Self-Organization Process , 1996, Science.

[17]  Orna Blumen,et al.  GENDER DIFFERENCES IN THE JOURNEY TO WORK , 1994 .

[18]  Tim Cresswell,et al.  Gendered Mobilities: Towards an Holistic Understanding , 2016 .

[19]  Xing Xie,et al.  Discovering regions of different functions in a city using human mobility and POIs , 2012, KDD.

[20]  Chaogui Kang,et al.  Intra-urban human mobility patterns: An urban morphology perspective , 2012 .

[21]  Elena del Val Noguera,et al.  U-Tool: A Urban-Toolkit for Enhancing City Maps Through Citizens' Activity , 2016, PAAMS.

[22]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[23]  Jordan Frith,et al.  Turning life into a game: Foursquare, gamification, and personal mobility , 2013 .

[24]  Jiajun Liu,et al.  Understanding Human Mobility from Twitter , 2014, PloS one.

[25]  Vicente Julián,et al.  A Multi-Agent System for the Dynamic Emplacement of Electric Vehicle Charging Stations , 2018 .

[26]  Zbigniew Smoreda,et al.  Unravelling daily human mobility motifs , 2013, Journal of The Royal Society Interface.

[27]  Sasu Tarkoma,et al.  Explaining the power-law distribution of human mobility through transportation modality decomposition , 2014, Scientific Reports.

[28]  A. Einstein,et al.  Die Grundlage der allgemeinen Relativitätstheorie , 1916 .

[29]  Antonio Lima,et al.  Understanding individual routing behaviour , 2016, Journal of The Royal Society Interface.

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

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

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

[33]  Daniel Villatoro,et al.  From Tweets to Semantic Trajectories: Mining Anomalous Urban Mobility Patterns , 2013, CitiSens.

[34]  Henning Best,et al.  Division of labour and gender differences in metropolitan car use : an empirical study in Cologne, Germany , 2004 .

[35]  Bruno Ribeiro,et al.  TribeFlow: Mining & Predicting User Trajectories , 2015, WWW.

[36]  Prosenjit Bose,et al.  A survey of geodesic paths on 3D surfaces , 2011, Comput. Geom..

[37]  S. Stouffer.  Intervening opportunities: a theory relating mobility and distance , 1940 .

[38]  Ciro Cattuto,et al.  Predicting human mobility through the assimilation of social media traces into mobility models , 2016, EPJ Data Science.

[39]  Dennis Luxen,et al.  Real-time routing with OpenStreetMap data , 2011, GIS.

[40]  Wen-Jing Hsu,et al.  Mining GPS data for mobility patterns: A survey , 2014, Pervasive Mob. Comput..

[41]  Tao Jia,et al.  An empirical study on human mobility and its agent-based modeling , 2012 .

[42]  A. Stewart Fotheringham,et al.  Analysis of human mobility patterns from GPS trajectories and contextual information , 2016, Int. J. Geogr. Inf. Sci..

[43]  Marta C. González,et al.  A universal model for mobility and migration patterns , 2011, Nature.

[44]  Mary Beth Rosson,et al.  Measuring Mobile Users' Concerns for Information Privacy , 2012, ICIS.

[45]  Daniel P. Huttenlocher,et al.  Comparing Images Using the Hausdorff Distance , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[46]  Marta Castilho Gomes,et al.  Defining mobility patterns in peri-urban areas: A contribution for spatial and transport planning policy , 2017 .

[47]  Yihong Yuan,et al.  Characterizing International Travel Behavior from Geotagged Photos: A Case Study of Flickr , 2016, PloS one.

[48]  M. Barthelemy,et al.  Human mobility: Models and applications , 2017, 1710.00004.

[49]  Daniele Quercia,et al.  Finger on the pulse: identifying deprivation using transit flow analysis , 2013, CSCW.

[50]  Víctor M. Eguíluz,et al.  Entangling Mobility and Interactions in Social Media , 2013, PloS one.

[51]  Susan Hanson,et al.  Gender and mobility: new approaches for informing sustainability , 2010 .

[52]  Aurora González-Vidal,et al.  Human Mobility Prediction Based on Social Media with Complex Event Processing , 2016, Int. J. Distributed Sens. Networks.

[53]  Carlo Ratti,et al.  Understanding individual mobility patterns from urban sensing data: A mobile phone trace example , 2013 .

[54]  Lun Wu,et al.  Intra-Urban Human Mobility and Activity Transition: Evidence from Social Media Check-In Data , 2014, PloS one.

[55]  Margaret Martonosi,et al.  ON CELLULAR , 2022 .