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Learning Mobility Patterns

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The Learning Mobility Patterns (LMP) project aims to learn mobility patterns from anonymous GSM information, teaching us how people and environments behave and change.

The Learning Mobility Patterns (LMP) project aims to learn mobility patterns from anonymous GSM information, teaching us how people and environments behave and change.

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  • 1. Location Pattern Recognition Eran Toch, Irad Ben Gal, Gabriella Cohen, and Roee Anuar http:toch.tau.ac.il
  • 2. Human Mobility ► Human mobility patterns reflect many aspects of people’s behavior. ► The Learning Mobility Patterns (LMP) project aims to learn patterns from anonymous GSM information. ► With Prof. Boaz Lerner (BGU) and Prof. Irad Ben-Gal (TAU), Funded by the Ministry of Science Infrastructure grant.
  • 3. Location Data Our Data: Geo-Annotated Phone Activity Records
  • 4. Locations Geo-tagged, time- stamped, anonymous location
  • 5. The Process Location data Models Analyzing People Analyzing Places Analyzing Trajectories Analyzing Communities
  • 6. Why Should we Understand People? Tailoring services and ads Understanding retail behavior Relating health and mobility Geographical Statistics
  • 7. Models of Mobility ► Probability models ► Gonzalez et al. (2008), Understanding individual human mobility patterns, Nature, 453(7196). ► T. Do and D. Gatica-Perez (2014),The Places of Our Lives:Visiting Patterns and Automatic Labeling from Longitudinal Smartphone Data, IEEE Trans. on Mobile Computing,13(3). ► Predictive models ► Monreale et al. (2009).Wherenext: a location predictor on trajectory pattern mining. KDD’09.ACM.
  • 8. Our Objective ► Developing concise and representative models for mobility pattern recognition and data mining. ► Multi-layered models ► Geographic layers ► Routine layers ► Semantic layers ► Understand and build towards privacy
  • 9. Preprocessing ► Transforming coordinates ► Cleaning data ► Clustering locations to places ► Handling missing data
  • 10. 10 Air-Port City Central TLV Hod-HaSharon mall A B C D E
  • 11. RoutineVector day_hour/ user1_01_11_21_31_41_51_61_71_81_91_101_111_121_131_141_151_16 1101AAAAAAAAAAAAAAAAA 1102AAAABBBBBBBBBBABA 1103CCDCCBBBBAAAAAAAA 1104AAAAAAAAAAAAAAAAA 1105AAAAAAAABBBBBBBBB 1106AAAAAAAAAAABBBBBB 1107AAAAAAAAAAAAAAAAA 1108AAAAAAAAAAAAAACAA 1109AAAAAAAAEEAAAAAAA 1110AAAAAAAAAAAAAAAAA 1111ZZZZZZZAAAAAAAAAA 1112AAAAAAAAAAAAAAAAA 1113AAAAAAAAAAABBBBBB 1114AAAAABBABBBBAAAAA 1115AAAAAAAAAAAAAAAAA On Sundays at 12:00 user 1113 in mostly located in location B 11
  • 12. Example: User X A D B C
  • 13. Example: User X 7 1 2 3 4 5 6 7 1 2 04/02/2012 05/02/2012 06/02/2012 07/02/2012 08/02/2012 09/02/2012 10/02/2012 11/02/2012 12/02/2012 13/02/2012 0 A A A A A A A A A 1 A A A A A A A A A 2 A A A A A A A A A 3 A A A A A A A A A 4 A A A A A A A A 5 A A A A A A A A A 6 A A A A A A A A A 7 A A A A A A A A D 8 A A A A D A A D 9 A B C D C B C C D 10 B B B B D C D 11 B B B B A C B A 12 B B B B B A A B B 13 D A B A A B 14 A B D B B A A B 15 A B D B B A A B B 16 A B B B B B A B 17 A B B A A B 18 A C C B B C B B D 19 A D D C C C C C 20 A A A D A A D A 21 A A A A A A A A 22 A A A A A A A A A 23 A A A A A A A A A
  • 14. A B C D
  • 15. Routines 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## 0 A A A A A A A A A F A A A A A A A A A A A A A A 1 A A A A A A A A A A A A A A A A A A A A A A A A A 2 A A A A A A A A A A A A A A A A A C A A A A A A A 3 A A A A A A A A A A A A A A A A C A A A A A A A A 4 A A A A A A A A A A A A A A A A A A A A A A A 5 A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A 6 A A A A A A C A A A A A A A A A A A E A A A C A A C A A A A A A A A A A A A A A A E A A A 7 C C A A A C A A A C A A A A A A A A A C A A A A A A A C A A A A A A G A A A A A A A C A C A 8 C A A C B B C A A C F A C C A A B B C A C B A B A B A C B A C C B C A A A A C C A A A C C A B C 9 C A A B B B B A A B B A B C A A B B B B A C B A B A B C B C B A B C B B A A C B A A A B B A A B B 10 B C A B B B B A A B B A B C A A B B B B A A B B A B A B B B B B A B B B B A B A C B B A 11 B B C A B B B B A A B B F B F A B B B B A B B B A B B B B B A B B B B A B B E B B B 12 B B C A B B B B F A B B B B A F B B B B F A B B A B B B B B A B B B B A B B A B B F A B 13 B B F B B B B A A B B B B A D B B B B A A B B B A B B B B B A B B B B A B B A F B B F D B B 14 B B F A B B B B A A B B B B F A D B B B A F B B B A B B B B B B B B B A B B D B B 15 B B A A B B B B A A B B B B F A A B B B B B A D B B B A B B B B B B B B B A B B A D B B A C B 16 B B A A B B B B E A B B B B F C A B B B B B A C B B B A B B B B B B B B A B B C F B B E B 17 B B A A B B B B E A B B B B A A A B B B B B A B B B A B B B B B A H B B B A A B B A E B B E 18 B B A A B B B C A A B B B B A A B B B B B B B B A B B B B B A A H B B A A A B B A E B B A E B B 19 C A A A B G B A A A B B G C C A A A B B C F A F C B A A C E A A A A H B C A A A A C B C A B A B B F B C 20 E A E A C C A A A B A A C A A A F A A F A A C F A A C C A A C C C C E A C E A A E A C A B 21 C A A A A A A A B A G A A A A A F A A A A A F A A A A A A A A C A A A A A G A A F A F A 22 A A A C A A C F A A G A A A A A A E A G A A A A A A A A A A A A C A A A A A A G A A 23 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### 0 A A A A A A A A A G G A A A B C A A A A A A A A 1 A A A A A A A A A A A A A B C A A A A A A A A A 2 A A A A A A A A A A A A B A A A A A A A A A A 3 A A A A A A A A A A A A A A A A A A A A A A A A 4 A A A A A A A A A A A A A A A A A A A A A A A A 5 A A A A A A A A A A A A A A B A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A 6 A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A 7 A A A A A A A A A A A A A A A A A A A A A A A A A A B A A A A B A A B C A A A A A A A A A A A A A A A A 8 B A A B B B B B A A A A A A A A B H B B A B B B A A E B B B A A B B B B A A A B A B B A B A B B B A B 9 B A A B B B B B A A A A A A A A B F B B A B B B B B B B B B B A A B B B B A A A B A B B B B A B B B A D B 10 B B A B B B B B A A A A A A A B F B B A B B B B B B B B B B A B B B B B A B A B B B B A B B B A H B 11 B B A B B B B B A A A A A A A B F B B B A B B B B B B B B B A B B B B B A B B B A B A B C B A D B 12 B B A B C B B B A A A A A A A C F B B B A B B B B B B B B B B A B B B B B A B A B B A B A B B C A D B 13 B A A B D B A B A C A A A A A A C F B B A A B B B A B B B B B A B B B A B A A A B B A B B C B B B A D B 14 B A A B D C A B A C A A A A A A F B B A A B A B A B A A B C A B B A B A B A A A B B B B A A B B A C A 15 A A A C D A A A A A A A B A A A C D B B A A A A B E A A A C A C A A B A A A C A B B B A B A A A A A C A 16 A A A C D A A A A A A B A A A C C B A A A A C A A A A A A A A A C A A A A A A C A B A A B A A A A A A A 17 A A A C A A A A A A C G A A A A A A C A C A A C A A A C A A A A A A C A A A A A B A A A A G A A A A A A G A 18 C A A C A A A C A A C A A A A C A C A C A A A G A C A A A A A A A A A A A B A A B A A A A A A C A A A 19 C A A C A A A C A A A C A A A A B A C A C A A A A A A A A A A A A A A A A A A B A A A A A A A A B C 20 B A A A A B A A A A A A A A A A C A A A A A A A A A A A A A A A A A A C A A A A A A A A A A B A 21 B C A A A C A A A A A A A A A A A C A A A A A A A A A A A A A A A A A A C A A A A A A A A A A C B A 22 A C A A A A C A A A A A C G A A A C A A A A A A A A A A A A A A A A A C A A A A A A A A A C C 23 User A User B
  • 16. Representation Layers Routine Layer Geographical Layer Semantic Layer
  • 17. Classification ► Classification and clustering is used to generate knowledge in different levels. ► Examples: ► Lifestyle from routine vectors ► Geographical density from geographical vectors
  • 18. 1029 7 1 2 3 4 5 6 7 1 2 04/02/2012 05/02/2012 06/02/2012 07/02/2012 08/02/2012 09/02/2012 10/02/2012 11/02/2012 12/02/2012 13/02/2012 0 A A A A A A D A B 1 A A A A A D A A 2 A A A A A A D A A 3 A A A A A A A A 4 A A A A A A 5 A A A A A A 6 A A A A A A A A A 7 A A A A A A A A 8 A A A A A A A A 9 A A B B A B A B 10 A B B B A A B 11 A B B B B A A B B 12 A B B B B B A A B B 13 A B B B A A B B 14 A B B B B C A B B 15 A B B B A B 16 A B B B B B A A B 17 A B B A A B 18 A A B C B B A B 19 A A A A A D 20 C A A A A D D 21 C A D A A C C D 22 C C A D D C A C 23 C A A A A D C B A 1007 7 1 2 3 4 5 6 7 1 2 04/02/2012 05/02/2012 06/02/2012 07/02/2012 08/02/2012 09/02/2012 10/02/2012 11/02/2012 12/02/2012 13/02/2012 0 A A A A C A A 1 A A A A A A A 2 A A A A 3 A A A 4 A A A 5 A B A A A A 6 B A A A B 7 A B A B A B 8 B B A B 9 A A B A B A B 10 A A B A B 11 A B A B A B 12 A A B A A B A 13 A A B A B A 14 A B B A A B A 15 A B A B A A B A 16 A B B A B A A B 17 A B B A B 18 A B A B A A B 19 A B A B B A A B 20 B A B C A A B 21 B A A A B 22 A A B A A A A B 23 A A B A A A A B 1004 7 1 2 3 4 5 6 7 1 2 04/02/2012 05/02/2012 06/02/2012 07/02/2012 08/02/2012 09/02/2012 10/02/2012 11/02/2012 12/02/2012 13/02/2012 0 C A A A A B A C A 1 C A A A B A C A 2 C A A A B A C A 3 C A A A A A 4 C A A A A A A A A 5 A A A A A A A 6 A A A A A A A A 7 A A A A A A A A A 8 A A A A A A A A 9 A A B A A A A A A 10 A A A A A A A 11 A A A A A A A A 12 A A B A A A A A A B 13 A A A A A A A 14 A B B B B A A A 15 A B B B B A A A B 16 A B B B A A 17 A B B A A 18 A B B B B B A A B 19 A B B B B A A A 20 A B B B B A A A 21 B B B A A A 22 A A A A B A A A 23 A A A A B A C A A 18
  • 19. Tel AvivVisitors 1059 7 1 2 3 4 5 6 7 1 2 04/02/2012 05/02/2012 06/02/2012 07/02/2012 08/02/2012 09/02/2012 10/02/2012 11/02/2012 12/02/2012 13/02/2012 0 C B G 1 C C 2 C F 3 C 4 5 6 7 8 9 A A A 10 A A A 11 A A D A 12 A A A D A 13 A A A 14 A B A A 15 A A A A A 16 H A A A A A 17 A 18 A A A A A 19 A A E 20 A D E 21 B A 22 B D 23 B G 1066 1 2 3 4 5 6 1 2 05/02/2012 06/02/2012 07/02/2012 08/02/2012 09/02/2012 10/02/2012 12/02/2012 13/02/2012 0 1 2 3 4 5 6 7 8 C C 9 A A A 10 A A B A 11 A A 12 A B A B C 13 A B 14 A B A 15 A A B 16 C A A A A 17 A A A 18 A A A B A 19 A B A 20 A A 21 B 22 23 1022 2 3 4 5 6 7 1 2 06/02/2012 07/02/2012 08/02/2012 09/02/2012 10/02/2012 11/02/2012 12/02/2012 13/02/2012 0 1 2 3 4 B 5 6 7 8 9 A A A 10 A A 11 A A B 12 A A A A 13 A A A 14 A A 15 A A A 16 A A 17 A 18 A 19 20 21 22 23
  • 20. Summary ► Location data as the basis for deeper understanding of human behavior. ► Groundbreaking applications. ► Many challenges: privacy, learning, analysis. http:toch.tau.ac.il