Discovering new socio-demographic regional patterns in cities

Proceedings of the 9th ACM SIGSPATIAL Workshop on Location-Based Social Networks, LBSN 2016

Volumn , Issue , 2016, Pages

  Assem, Haytham ^a   O'Sullivan, Declan ^b  

^a Smarter Cities Technology Centre   (Ireland)

^b TRINITY COLLEGE DUBLIN   (Ireland)

Author keywords

Activity patterns; Deep learning; Location based social networks; Smart cities

Indexed keywords

ARTIFICIAL INTELLIGENCE; DEEP LEARNING; GEOGRAPHICAL REGIONS; LEARNING SYSTEMS; LOCATION; POPULATION STATISTICS; STATISTICS;

ACTIVITY PATTERNS; DEEP BELIEF NETWORKS; EMBEDDED PATTERNS; LATENT DIRICHLET ALLOCATIONS; LOCATION-BASED SOCIAL NETWORKS; MACHINE LEARNING TECHNIQUES; PROBABILISTIC TOPIC MODELS; SPATIOTEMPORAL PATTERNS;

SMART CITY;

EID: 85020013713     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/3021304.3021305     Document Type: Conference Paper

References (41)

1. 5 boroughs: Compare and contrast. http://www.newyork.com/articles/real-estate/5-boroughs-compare-and-contrast-17601/. Accessed: 16-01-2016.
2. Foursquare application. http://www.foursquare.com/.
3. Y. Anzai. Pattern Recognition & Machine Learning. Elsevier, 2012.
4. R. Arun, V. Suresh, C. V. Madhavan, and M. N. Murthy. On finding the natural number of topics with latent dirichlet allocation: Some observations. In KDD. 2010.
5. H. Assem and D. O'Sullivan. Towards bridging the gap between machine learning researchers and practitioners. In 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity), pages 702-708. IEEE, 2015.
6. H. Assem, L. Xu, T. S. Buda, and D. O'Sullivan. Machine learning as a service for enabling internet of things and people. Personal and Ubiquitous Computing, pages 1-16.
7. H. Assem, L. Xu, T. S. Buda, and D. O'Sullivan. Spatio-temporal clustering approach for detecting functional regions in cities. In ICTAI, 2016.
8. Y. Bengio. Learning deep architectures for ai. Foundations and trendsR in Machine Learning, 2(1):1-127, 2009.
9. Y. Bengio, P. Lamblin, D. Popovici, H. Larochelle, et al. Greedy layer-wise training of deep networks. In NIPS, 2007.
10. N. Bicocchi, G. Castelli, M. Mamei, A. Rosi, and F. Zambonelli. Supporting location-aware services for mobile users with the whereabouts diary. In MobilWare, 2008.
11. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. the Journal of machine Learning research, 3:993-1022, 2003.
12. J. Cao, T. Xia, J. Li, Y. Zhang, and S. Tang. A density-based method for adaptive lda model selection. Neurocomputing, 72(7):1775-1781, 2009.
13. Z. Cheng, J. Caverlee, K. Lee, and D. Z. Sui. Exploring millions of footprints in location sharing services. 2011.
14. C. Collins, S. Hasan, and S. V. Ukkusuri. A novel transit rider satisfaction metric: Rider sentiments measured from online social media data. Journal of Public Transportation, 16(2):2, 2013.
15. R. Collobert and J. Weston. A unified architecture for natural language processing: Deep neural networks with multitask learning. In ICML, 2008.
16. N. Eagle and A. S. Pentland. Eigenbehaviors: Identifying structure in routine. Behavioral Ecology and Sociobiology, 63(7):1057-1066, 2009.
17. K. Farrahi and D. Gatica-Perez. Discovering routines from large-scale human locations using probabilistic topic models. ACM Transactions on Intelligent Systems and Technology (TIST), 2(1):3, 2011.
18. L. Ferrari and M. Mamei. Discovering daily routines from google latitude with topic models. In PERCOM, 2011.
19. L. Ferrari, A. Rosi, M. Mamei, and F. Zambonelli. Extracting urban patterns from location-based social networks. In SIGSPATIAL, 2011.
20. Y. Freund and D. Haussler. Unsupervised learning of distributions of binary vectors using two layer networks, 1994.
21. Y. Fu, H. Xiong, Y. Ge, Z. Yao, Y. Zheng, and Z.-H. Zhou. Exploiting geographic dependencies for real estate appraisal: A mutual perspective of ranking and clustering. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1047-1056. ACM, 2014.
22. H. Gao, J. Tang, and H. Liu. gscorr: modeling geo-social correlations for new check-ins on location-based social networks. In CIKM, 2012.
23. A. Halevy, P. Norvig, and F. Pereira. The unreasonable effectiveness of data. Intelligent Systems, IEEE, 24(2):8-12, 2009.
24. S. Hasan and S. V. Ukkusuri. Urban activity pattern classification using topic models from online geo-location data. Transportation Research Part C: Emerging Technologies, 44:363-381, 2014.
25. G. Hinton. A practical guide to training restricted boltzmann machines. Momentum, 9(1):926, 2010.
26. G. E. Hinton. Training products of experts by minimizing contrastive divergence. Neural computation, 14(8):1771-1800, 2002.
27. G. E. Hinton and R. R. Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, 313(5786):504-507, 2006.
28. J. J. Hopfield. Neural networks and physical systems with emergent collective computational abilities. Proceedings of the national academy of sciences, 79(8):2554-2558, 1982.
29. F. Kling and A. Pozdnoukhov. When a city tells a story: urban topic analysis. In SIGSPATIAL, 2012.
30. H. Larochelle, D. Erhan, A. Courville, J. Bergstra, and Y. Bengio. An empirical evaluation of deep architectures on problems with many factors of variation. In ICML, 2007.
31. Y. LeCun, S. Chopra, R. Hadsell, M. Ranzato, and F. Huang. A tutorial on energy-based learning. Predicting structured data, 1:0, 2006.
32. L. Maaloe, M. Arngren, and O. Winther. Deep belief nets for topic modeling. arXiv preprint arXiv:1501.04325, 2015.
33. H. Mobahi, R. Collobert, and J. Weston. Deep learning from temporal coherence in video. In ICML, 2009.
34. A. Noulas, S. Scellato, C. Mascolo, and M. Pontil. Exploiting semantic annotations for clustering geographic areas and users in location-based social networks. 2011.
35. R. Salakhutdinov, A. Mnih, and G. Hinton. Restricted boltzmann machines for collaborative filtering. In ICML, 2007.
36. J. Shang, Y. Zheng, W. Tong, E. Chang, and Y. Yu. Inferring gas consumption and pollution emission of vehicles throughout a city. In SIGKDD, 2014.
37. S. Sigg, S. Haseloff, and K. David. An alignment approach for context prediction tasks in ubicomp environments. PERCOM, 2010.
38. D. Yang, D. Zhang, V. W. Zheng, and Z. Yu. Modeling user activity preference by leveraging user spatial temporal characteristics in lbsns. Systems, Man, and Cybernetics: Systems, IEEE Transactions on, 45(1):129-142, 2015.
39. N. J. Yuan, Y. Zheng, X. Xie, Y. Wang, K. Zheng, and H. Xiong. Discovering urban functional zones using latent activity trajectories. Knowledge and Data Engineering, IEEE Transactions on, 27(3):712-725, 2015.
40. E. Zavitsanos, S. Petridis, G. Paliouras, and G. A. Vouros. Determining automatically the size of learned ontologies. In ECAI, 2008.
41. F. Zhang, D. Wilkie, Y. Zheng, and X. Xie. Sensing the pulse of urban refueling behavior. In UbiComp, 2013.