Discovering human places of interest from multimodal mobile phone data @ MUM'10

1. +
Discovering Human Places of Interest from
Multimodal Mobile Phone Data
2014/11/4 (Tue.)
Raul Montoliu, Daniel Gatica-Perez
MUM‘13
Chang Wei-Yuan @ MakeLab Group Meeting

2. +Outline
n Introduction
n Definition
n Method
n Operation modes
n Time-based Clustering
n Grid-based Clustering
n Experiment
n Conclusion
n Thought
2

3. +Outline
n Introduction
n Definition
n Method
n Operation modes
n Time-based Clustering
n Grid-based Clustering
n Experiment
n Conclusion
n Thought
3

4. +Introduction
4
n A new framework to discover places-of-interest.

5. + 5

6. +Outline
n Introduction
n Definition
n Method
n Operation modes
n Time-based Clustering
n Grid-based Clustering
n Experiment
n Conclusion
n Thought
6

7. +Definition
n Location Point
n a measurement about the location of a user
n e.g. ([46:6N; 6:5E], [16:34:57])
7

8. +Definition
n Stay Point
n a geographic region in which the user stayed
for a while
n e.g. ([46:6N; 6:5E], [16:30:00], [17:54:34])
8

9. +Definition
n Stay Region
n a cluster of stay points with the same
semantic meaning
n e.g. ([46:6N; 6:5E]; [46:595N;-46:599N];
[6:498E; 6:502E])
9

10. +Outline
n Introduction
n Definition
n Method
n Operation modes
n Time-based Clustering
n Grid-based Clustering
n Experiment
n Conclusion
n Thought
10

11. + 11

12. +Outline
n Introduction
n Definition
n Method
n Operation modes
n Time-based Clustering
n Grid-based Clustering
n Experiment
n Conclusion
n Thought
12

13. +Operation modes
n Multimodal Mobile Phone Data
n Wifi-map Mode
n a map of geo-referenced Wifi APs
n Static Mode
n GPS Mode
13

14. +Outline
n Introduction
n Definition
n Method
n Operation modes
n Time-based Clustering
n Grid-based Clustering
n Experiment
n Conclusion
n Thought
14

15. +Time-based Clustering
n Location Point = lp = (p1, p2, …, pN)
n Pi = (lat, long, T)
n obtained from the multimodal sensor
n Stay points = lsp = (sp1, sp2, …, spM)
n spi = (lat, long, Tstart, Tend)
15

16. +Time-based Clustering
n SpaceDistance(ps; pe) Dmax
n TimeDifference(ps; pe) Tmin
n TimeDifference(pk; pk+1) Tmax
n k ∈ [s, e]
16

17. + 17

18. +Outline
n Introduction
n Definition
n Method
n Operation modes
n Time-based Clustering
n Grid-based Clustering
n Experiment
n Conclusion
n Thought
18

19. +Grid-based Clustering
19
n grid-based clustering
n constrain the cluster size

20. +Grid-based Clustering
20

21. +Outline
n Introduction
n Definition
n Method
n Operation modes
n Time-based Clustering
n Grid-based Clustering
n Experiment
n Conclusion
n Thought
21

22. +Extracting location points
GPS
4%
Wifi
Map
35%
Static
24%
No
location
37%
22
n approximately for
63% of the day it
is possible to
estimate the
location of a user

23. +Comparative results on place of
interest discovering
n Evaluation System
n Discovered
n Remembered
n Missed
n Correct
n Forgotten
n False
23

24. +Comparative results on place of
interest discovering
n Evaluation System
n Discovered
n Remembered
n Missed
n Correct ↑
n Forgotten ↑
n False
24

25. + 25

26. + 26

27. +Outline
n Introduction
n Definition
n Method
n Operation modes
n Time-based Clustering
n Grid-based Clustering
n Experiment
n Conclusion
n Thought
27

28. +Conclusion
n Thanks to the use of this framework, it
is possible to obtain location data for
63% of the day in real life.
n This approach is multimodal since
location information is obtained from
multiple sensor.
28

29. +Outline
n Introduction
n Definition
n Method
n Operation modes
n Time-based Clustering
n Grid-based Clustering
n Experiment
n Conclusion
n Thought
29

30. + 30
APP
data

31. + 31

32. +
Thanks for listening.
2014 / 11 / 4 (Tue.) @ MakeLab Group Meeting
v123582@gmail.com