Inferring Unusual Crowd Events From Mobile Phone Call ...

© 2015 IBM Corporation

Inferring Unusual Crowd Events From Mobile Phone Call Detail Records

Yuxiao Dong, Fabio Pinelli, Yiannis Gkoufas, Zubair Nabi, Francesco Calabrese, Nitesh V. Chawla

IBM Research, Dublin, Ireland University of Notre Dame, USA

© 2015 IBM Corporation 1.  http://bansteadathletic.co.uk/?p=32 2.  https://www.spriza.com/contest/213 3.  http://gaymarriage.procon.org/

What are Unusual Crowd Events?

Game



Game Concert



Game Concert Protest



Game Concert Protest Riot


Goal

•  Help cities better manage such unusual events •  Transport •  Public safety

•  Identify and quantify impact of events


As of 2014, there were 7.3 billion mobile subscriptions, larger than the global population[1,2]. Users average 22 calls, 23 messages, and 110 status checks per day[3].

1.  Yuxiao Dong, et. al . Inferring User Demographics and Social Strategies in Mobile Social Networks. In KDD’14. 2.  International Telecommunications Union (ITU) at the 2013 Mobile World Congress. 3.  http://www.dailymail.co.uk/sciencetech/article-2449632/How-check-phone-The-average-person-does-110-times-DAY-6-seconds-evening.html 4.  https://www.enisa.europa.eu/media/press-releases/using-national-roaming-to-mitigate-mobile-network-outages201d-new-report-by-eu-cyber-security-agency-enisa

Why from Mobile Phone Data?


Why from Mobile Phone Data?

ü High level of mobile phone penetration ü Data accessible from mobile operators

ü  Little percentage of GPS devices ü Difficult to access GPS data


Outline

•  Mobile phone network data and their challenges •  Related work •  Dealing with spatio-temporal sparseness •  Dealing with non-routine events •  Methodology •  Demonstration on large scale data •  Conclusions



Inferring Unusual Crowd Events from CDR Data

Cylindrical*Cluster*Detec0on*

User*Mobility*Profile*Construc0on*

Closed*Crowd*Detec0on*

Unusual*Crowd*Detec0on*

Unusual*Event*Detec0on*

CDR Data Unusual Events



1.  http://viewfromll2.com/2015/01/12/serial-the-failure-of-the-prosecutions-cellphone-theory-in-one-simple-chart/

Challenges?



1.  http://viewfromll2.com/2015/01/12/serial-the-failure-of-the-prosecutions-cellphone-theory-in-one-simple-chart/

Challenges?

Temporal Sparseness

CDR data only records the user location when a call is made

Spatial Sparseness

A user location is recorded as the location of the antenna

Non-routine events

Unusual movements and daily routines are coupled with each other


Related work Moving clusters

Panos Kalnis, Nikos Mamoulis, and Spiridon Bakiras. On discovering moving clusters in spatio-temporal data. In SSTD'05. M. Spiliopoulou, I. Ntoutsi, Y. Theodoridis, and R. Schult. MONIC: Modeling and Monitoring Cluster Transitions. In KDD'06.

Flocks Marcos Vieira,Petko Bakalov,Vassilis Tsotras. On-Line Discovery of Flock Patterns in Spatio-Temporal Data. In GIS'09.

Convoy Hoyoung Jeung, Man Lung Yiu, Xiaofang Zhou, Christian S. Jensen, Heng Tao Shen. Discovery of Convoys in Trajectory Databases. In VLDB'08.

Swarm Zhenhui Li, Bolin Ding, Jiawei Han, Roland Kays. Swarm: Mining Relaxed Temporal Moving Object Clusters. In VLDB'10.

Gathering Kai Zheng, Yu Zheng, Nicholas Jing Yuan, Shuo Shang. On discovery of gathering patterns from trajectories. In ICDE'13.

Inability to address the CDR limitations v  No specific location info in CDR. VS. Precise locations in GPS data. v  Sparse calls by users VS. GPS collects the user locations

periodically (e.g. every 30s) v  Not able to distinguish routine from non-routine events



Cylindrical Cluster

x x x x

y y y y

t t1 t2 t3 t4

ɛt ɛt

User 1

User 2

User 3

User 4



Crowd: existence probability

The existence probability of one user locating in a cluster at timestamp t: the proportion of #users in CCt to #users in CCt-1.

user3: 1 1/3 1 2/3

user4: 0 1 1/2 1/2*2/3

x x x x

y y y y

t t1 t2 t3 t4

ɛt ɛt

User 1

User 2

User 3

User 4

Temporal Sparseness

Spatial Sparseness



Crowd

x x x x

y y y y

t t1 t2 t3 t4

ɛt ɛt

User 1

User 2

User 3

User 4

Complexity O(|A|2*|T|), where |A| is #antennas and |T| is #timestamps


Mobility Profile

Home

Workplace

We are looking for unusual crowds


Mobility Profile

Given a CDR database DBCDR with individual trajectories, one’s mobility profile is the groups of locations she visited for each time unit every day.

8am 9am 10am 11am 12pm 1pm 2pm 3pm …

l1: 9 l2: 1

l1: 1 l7: 11

l7: 5 l8: 1 l9: 2

l7: 1 l5: 2

l6: 1

l2: 2 l7: 12

l11: 2 l7: 1

l7: 1

user4’s mobility profile: user4’s location distribution in one month


Unusual Crowd

8am 9am 10am 11am 12pm 1pm 2pm 3pm …

l1: 9 l2: 1

l1: 1 l7: 11

l7: 5 l8: 1 l9: 2

l7: 1 l5: 2

l6: 1

l2: 2 l16: 12

l11: 2 l7: 1

l7: 1

user4: 0 1 1/2 1/2*2/3

l8 l13 l14 l16

user4: 1 0 0 12 mobility profile

user4’s mobility profile: user4’s location distribution in one month

crowd

x x x x

y y y y

t t1 t2 t3 t4

ɛt ɛt

User 1

User 2

User 3

User 4

cosine similarity

Non-routine events

l8 l13

l14 l16


Unusual Crowd

Complexity O(|C|*|V|), where |C| is #crowds and |V| is #users


Unusual Crowd Event

Complexity O(|UC|2), where |UC| is #unusual crowds


Mobile CDR Data

D4D Orange Challenge 2012 ü  Date: five months expanding from Dec. 2011 to Apr. 2012 ü  Country: Cote d’Ivoire ü  #users: 50,000 per two weeks ü  #CDR: ~ 2 million per two weeks ü  Format: <UserID, Day, Time, Antenna>

<Antenna, Location> Observations from Data

ü  63% users don’t make calls in two consecutive hours ü  83% two consecutive calls have a less than two-hour interval

1.  http://www.d4d.orange.com/

Temporal Sparseness

Spatial Sparseness


Visual Analytics Prototype System

Real-time view of city map and statistics


Visual Analytics Prototype System

Analyst statistics view


Experiments: time series analysis

2011−12−06 Tue 00:00 12−09 Fri 06:00 12−12 Mon 12:00 12−15 Thu 18:00 12−18 Sun 23:000

1

2

3

4

5

date

#Unusual−crowds

#Unusual−events

Time series of detected unusual crowd events

06th Dec. to 18th, Dec. Parliament election Dec 10th


5000

10000

15000

date

UsersCalls


100

200

300

400

500

date

antenna 808antenna 670antenna 1040antenna 274antenna 1027

Overall CDR volumes Activities of involved five antennas

No irregular phone activity during events


Experiments: comparison


1

2

3

4

5

date

#Unusual−crowds

#Unusual−events

date2011-12-06 Tue 00:00 12-09 Fri 06:00 12-12 Mon 12:00 12-15 Thu 18:00 12-18 Sun 23:00

#gat

herin

gs

0

100

200

300

400

500kp=3, mp=10, d=0.0kp=3, mp=10, d=0.5kp=3, mp=10, d=1.0kp=3, mp=10, d=1.5kp=2, mp=10, d=1kp=3, mp=10, d=1kp=4, mp=10, d=1kp=3, mp=5, d=1kp=3, mp=10, d=1kp=3, mp=15, d=1

Time series of detected unusual crowd events

Time series of detected gatherings [1]

06th Dec. to 18th, Dec. Parliament election

[1] Kai Zheng, Yu Zheng, Nicholas Jing Yuan, Shuo Shang. On discovery of gathering patterns from trajectories. In ICDE'13


Experiments

1.  P. Paraskevopoulos et al. Identification and characterization of human behavior patterns from mobile phone data. In NetMob’13.


Experiments: comparison

1.  P. Paraskevopoulos et al. Identification and characterization of human behavior patterns from mobile phone data. In NetMob’13. 2.  K. Zheng et al. On discovery of gathering patterns from trajectories. In ICDE’13.


ü  Problem: Inferring unusual crowd events from CDR data.

ü  Framework: effective unusual crowd event detection

ü  System: visual analytics prototype tool

ü  Parameter setting and optimization

ü  Multiple-sources of data for detection, such as social media

ü  Real-time unusual event detection

Conclusions

Future Work


Thanks Francesco Calabrese

[email protected]

@fcalabre

Inferring Unusual Crowd Events From Mobile Phone Call ...

Documents

Transcript of Inferring Unusual Crowd Events From Mobile Phone Call ...