Location contexts of user check-ins to model urban geo life-style patterns

PLoS ONE

Volumn 10, Issue 5, 2015, Pages

  Hasan, Samiul ^a   Ukkusuri, Satish V ^b  

^a CSIRO   (Australia)

^b PURDUE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; CONTROLLED STUDY; DATA ANALYSIS; GEOGRAPHIC DISTRIBUTION; GEOGRAPHY; GEOLIFESTYLE PATTERN; LIFESTYLE; LIFESTYLE AND RELATED PHENOMENA; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; MOBILE APPLICATION; MOBILE PHONE; PREDICTION; PROBABILITY; PROCESS DEVELOPMENT; SOCIAL MEDIA; COMPUTER SIMULATION; DECISION MAKING; GEOGRAPHIC INFORMATION SYSTEM; HUMAN; NEW YORK; STATISTICAL MODEL; URBAN POPULATION;

CHOICE BEHAVIOR; COMPUTER SIMULATION; GEOGRAPHIC INFORMATION SYSTEMS; HUMANS; LIFE STYLE; MODELS, STATISTICAL; NEW YORK CITY; SOCIAL MEDIA; URBAN POPULATION;

EID: 84929378835     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0124819     Document Type: Article

References (35)

1. Salomon I (1983) Life styles-a broader perspective on travel behavior. Recent Advances in Travel Demand Analysis Gower, Aldershot: 290-310.
2. Salomon I, Ben-Akiva M (1983) The use of the life-style concept in travel demand models. Environment and Planning A 15: 623-638. doi: 10.1068/a150623
3. Kitamura R (1988) Life-style and travel demand. Technical Report 220, Transportation Research Board.
4. Kitamura R (2009) Life-style and travel demand. Transportation 36: 679-710. doi: 10.1007/s11116-009-9244-6
5. Krizek KJ, Waddell P (2002) Analysis of lifestyle choices: Neighborhood type, travel patterns, and activity participation. Transportation Research Record 1807: 119-128. doi: 10.3141/1807-15
6. Vredin Johansson M, Heldt T, Johansson P (2006) The effects of attitudes and personality traits on mode choice. Transportation Research Part A: Policy and Practice 40: 507-525.
7. Vij A, Carrel A, Walker JL (2011) Latent modal preferences: behavioral mixture models with longitudinal data. In: International Choice Modeling Conference, Leeds, UK.
8. Choo S, Mokhtarian PL (2004) What type of vehicle do people drive? the role of attitude and lifestyle in influencing vehicle type choice. Transportation Research Part A: Policy and Practice 38: 201-222.
9. Lian D, Xie X (2011) Collaborative activity recognition via check-in history. In: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks. LBSN'11, pp. 45-48.
10. Phitha kkitnukoon S, Olivier P (2011) Sensing urban social geography using online social networking data. In: Proceedings of the International Conference on the Weblogs and Social Media (ICWSM).
11. Cheng Z, Caverlee J, Lee K, Sui DZ (2011) Exploring millions of footprints in location sharing services. In: Proceedings of the 5th International AAAI Conference on Weblogs and Social Media (ICWSM).
12. Noulas A, Scellato S, Mascolo C, Pontil M (2011) An empirical study of geographic user activity patterns in foursquare. In: Proceedings of the 5th International AAAI Conference on Weblogs and Social Media (ICWSM).
13. Noulas A, Scellato S, Lambiotte R, Pontil M, Mascolo C (2012) A tale of many cities: universal patterns in human urban mobility. PloS one 7: e37027. doi: 10.1371/journal.pone.0037027 PMID: 22666339
14. Ukkusuri SV, Hasan S, Zhan X (2013) Checking the urban pulse: Social media data analytics for transportation applications. In: Bregman S, Watkins KE, editors, Best Practices for Transportation Agency Use of Social Media, CRC Press.
15. Hasan S, Zhan X, Ukkusuri SV (2013) Understanding urban human activity and mobility patterns using large-scale location-based data from online social media. In: Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing. ACM.
16. Pianese F, An X, Kawsar F, Ishizuka H (2013) Discovering and predicting user routines by differential analysis of social network traces. In: World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2013 IEEE 14th International Symposium and Workshops. pp. 1-9.
17. Hasan S, Ukkusuri SV (2014) Urban activity pattern classification using topic models from online geolocation data. Transportation Research Part C: Emerging Technologies 44: 363-381. doi: 10.1016/j.trc.2014.04.003
18. Wu L, Zhi Y, Sui Z, Liu Y (2014) Intra-urban human mobility and activity transition: Evidence from social media check-in data. PloS one 9: e97010. doi: 10.1371/journal.pone.0097010 PMID: 24824892
19. Liu Y, Sui Z, Kang C, Gao Y (2014) Uncovering patterns of inter-urban trip and spatial interaction from social media check-in data. PloS one 9: e86026. doi: 10.1371/journal.pone.0086026 PMID: 24465849
20. Hasan S (2013) Modeling urban mobility dynamics using geo-location data. Ph.D. Dissertation, Purdue University.
21. Chang J, Sun E (2011) Location3 : How users share and respond to location-based data on social networking sites. In: Proceedings of the International Conference on the Weblogs and Social Media (ICWSM).
22. Cho E, Myers SA, Leskovec J (2011) Friendship and mobility: user movement in location-based social networks. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp. 1082-1090.
23. Cranshaw J, Schwartz R, Hong JI, Sadeh NM (2012) The livehoods project: Utilizing social media to understand the dynamics of a city. In: Proceedings of the 6th International AAAI Conference on Weblogs and Social Media (ICWSM).
24. Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. The Journal of Machine Learning Research 3: 993-1022.
25. Ferrari L, Rosi A, Mamei M, Zambonelli F (2011) Extracting urban patterns from location-based social networks. In: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks. LBSN'11, pp. 9-16.
26. Joseph K, Tan CH, Carley KM (2012) Beyond "local", "categories" and "friends": Clustering foursquare users with latent "topics". In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing. UbiComp '12, pp. 919-926.
27. Griffiths TL, Steyvers M (2004) Finding scientific topics. Proceedings of the National Academy of Sciences 101: 5228-5235. doi: 10.1073/pnas.0307752101
28. Donnelly F (2013) Introduction to the NYC geodatabase (nyc-gdb) ArcGIS version. Available at http://www.baruch.cuny.edu/geoportal/nyc-gdb/.
29. González MC, Hidalgo CA, Barabási AL (2008) Understanding individual human mobility patterns. Nature 453: 779-782. doi: 10.1038/nature06958 PMID: 18528393
30. Gao H, Tang J, Liu H (2012) gscorr: modeling geo-social correlations for new check-ins on locationbased social networks. In: Proceedings of the 21st ACM international conference on Information and knowledge management. ACM, pp. 1582-1586.
31. Collins C, Hasan S, Ukkusuri SV (2013) A novel transit rider satisfaction metric: Rider sentiments measured from online social media data. Journal of Public Transportation 16. doi: 10.5038/2375-0901.16.2.2
32. Quercia D, Saez D (2014) Mining urban deprivation from foursquare: Implicit crowdsourcing of city land use. Pervasive Computing, IEEE 13: 30-36. doi: 10.1109/MPRV.2014.31
33. De Choudhury M, Gamon M, Counts S, Horvitz E (2013) Predicting depression via social media. In: Proceedings of the 7th International AAAI Conference on Weblogs and Social Media (ICWSM).
34. Harris R, Sleight P, Webber R (2005) Geodemographics, GIS and neighbourhood targeting, volume 7. Wiley.
35. Comscore (2011). Comscore press release may 12, 2011. http://www.comscore.com/Press-Events/ Press-Releases/2011/5/Nearly-1-in-5-Smartphone-Owners-Access-Check-In-Services-Via- their-Mobile-Device. Accessed: Jan 12, 2012.