Checking in or checked in: comparing large-scale manual and automatic location disclosure patterns

Studies on human mobility are built on two fundamentally different data sources: manual check-in data that originates from location-based social networks and automatic check-in data that can be automatically collected through various smartphone sensors. In this paper, we analyze the differences and similarities of manual check-ins from Foursquare and automatic check-ins from Nokia's Mobile Data Challenge. Several new findings follow from our analysis: (1) While automatic checking-in overall results in more visits than manual checking-in, the check-in levels are comparable when visiting new places. (2) Daily and weekly check-in activity patterns are similar for both systems except for Saturdays -- when manual check-ins are relatively more probable. (3) A recently proposed rank distribution to describe human mobility, so far validated on manual check-in data, also holds for automatic check-in data given a slight modification to the definition of rank. (4) The patterns described by automatic check-ins are in general more predictable. We also address the question of whether it is possible to find matching places across the two check-in systems. Our analysis shows that while this is challenging in areas such as city centers, our method achieves an accuracy of 51% for places that are not homes of phone users.