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Discovering Human Places of Interest from Multimodal Mobile Phone Data

2014/11/4 (Tue.)�Chang Wei-Yuan @ MakeLab Group Meeting

Raul Montoliu, Daniel Gatica-Perez �MUM‘13

+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

+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

+ Introduction 4

n A new framework to discover places-of-interest. �

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+Outline

n Introduction �

n Definition �

n Method �n  Operation modes �n  Time-based Clustering �n  Grid-based Clustering �

n Experiment �

n Conclusion �

n Thought

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+Definition

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

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+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])�

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+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])

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+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

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+Outline

n Introduction �

n Definition �

n Method �n  Operation modes �n  Time-based Clustering �n  Grid-based Clustering �

n Experiment �

n Conclusion �

n Thought

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+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 �

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+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

+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)�

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+Time-based Clustering

n SpaceDistance(ps; pe) < Dmax �

n TimeDifference(ps; pe) > Tmin �

n TimeDifference(pk; pk+1) < Tmax �n k ∈ [s, e]

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+Outline

n Introduction �

n Definition �

n Method �n  Operation modes �n  Time-based Clustering �n  Grid-based Clustering �

n Experiment �

n Conclusion �

n Thought

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+Grid-based Clustering 19

n grid-based clustering �n constrain the cluster size

+Grid-based Clustering 20

+Outline

n Introduction �

n Definition �

n Method �n  Operation modes �n  Time-based Clustering �n  Grid-based Clustering �

n Experiment �

n Conclusion �

n Thought

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+Extracting location points

GPS 4%

Wifi Map 35%

Static 24%

No location

37%

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n approximately for 63% of the day it is possible to estimate the location of a user

+Comparative results on place of�interest discovering

n Evaluation System�n Discovered �n Remembered �n Missed �n Correct �n Forgotten �n False

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+Comparative results on place of�interest discovering

n Evaluation System�n Discovered �n Remembered �n Missed �n Correct ↑�n Forgotten ↑�n False

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+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

+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.

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+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

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APP data

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