modeling human mobility using location based social networks

Modeling Human MobilityUsing Location Based Social Networks

Gene Moo LeeUniversity of Texas at Austin

2012.08.11UKC 2012 - CSI Track

Introduction: LBSN

● LBSN (Location Based Social Network) becomes very popular○ Foursquare: 10M users, 1B checkins (Sept. 2011)○ Gowalla is acquired by Facebook○ Facebook, Twitter, Google+ also added location

features

● LBSN gives us a good chance to understand how people move around

Data CollectionCity (Data) Number of Users Number of Places Number of Check-ins

New York, NY (4SQ) 101,068 6,443 657,452

New York, NY (GWL) 5,121 13,651 116,376

Austin, TX (4SQ) 14,744 1,042 116,325

Austin, TX (GWL) 6,904 11,024 213,823

San Francisco, CA (4SQ) 41,114 2,325 237,666

San Francisco, CA (GWL) 6,386 14,234 220,405

● Foursquare: January 2012 ~ February 2012● Gowalla: May 2010 ~ July 2010

Airports in Foursquare

● LBSN is popular globally● Mainly US, Europe, South Asia

Manhattan in both datasets

● LBSN gives a representative coverage of cities

Understanding Human Movement

● Microscopic View○ We can focus on individual users and try to

understand their movements○ Many studies have been conducted on this line

● Macroscopic View○ We can focus on locations and analyze aggregated

user movements between locations○ In this study, we define "human traffic matrix" to

achieve this

Human Traffic Matrix

● U: set of users● V: set of venues/locations

● T is a traffic matrix where each row and column corresponds to locations

● The element of a human traffic matrix T(v1,v2;t) is defined as the number of users in U who moved from venue v1 to v2 during time t○ We use daily time snapshots for t○ Venues are collapsed into 0.01o x 0.01o grids

(around 1.1km x 0.7 km)

Gravity Model

● We model the human traffic matrix by gravity model○ Newton's law of gravitation: gravity between two

objects is proportional to the product of masses of two objects divided by the squared distance

● T(a,*) = number of check-ins at venue a● d(a,b) = the geographic distance between a and b● Try to find the optimal scaling factor "alpha" and

distance exponent "beta" (minimizing NMAE)

Error Metrics

● NMAE (Normalized Mean Average Error)○ Lower values are preferred

● CORR (Correlation Coefficient)○ +1 indicates strong positive correlation○ -1: negative correlation, 0: independence

Results: New York, NY

● New York (4SQ): CORR = 0.99, NMAE = 0.16● New York (GWL): CORR = 0.96, NMAE = 0.34* Green lines indicate 20% error boundaries

Results: San Francisco, CA

● SF (4SQ): CORR = 0.98, NMAE = 0.27● SF (GWL): CORR = 0.93, NMAE = 0.56

Results: Austin, TX

● Austin (4SQ): CORR = 0.97, NMAE = 0.31● Austin (GWL): CORR = 0.96, NMAE = 0.34

Evaluation Summary

● Gravity models give very good estimations○ High correlations: 0.93 ~ 0.99○ Good estimation errors: 16% ~ 56%

● Beta (distance exponent)○ 0.0 ~ 0.3 for Foursquare○ 0.4 ~ 0.6 for Gowalla○ Distance matters

● Alpha (constant factor)○ Vary due to the diverse properties of each city

Concluding Remarks

● Gravity models can be used to generate human traffic between locations

● LBSN is a great new data source● On going works to study more properties of

human movements using LBSN

Thank you!

Contact me atgene@cs.utexas.edu