modeling human mobility using location based social networks
TRANSCRIPT
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
