Understanding Urban Human Activity and Mobility Patterns Using Large-scale Location-based Data from Online Social Media

摘要

Location-based check-in services enable individuals to share their activity-related choicesproviding a new source of human activity data for researchers. In this paper urban humanmobility and activity patterns are analyzed using location-based data collected from socialmedia applications (e.g. Foursquare and Twitter). We rst characterize aggregate activitypatterns by nding the distributions of dierent activity categories over a city geographyand thus determine the purpose-specic activity centers. We then characterize individualactivity patterns by nding the timing distribution of visiting dierent places depending onactivity category. We also explore the frequency of visiting a place with respect to the rankof the place in individual's visitation records and show interesting match with other resultsbased on mobile phone mobility data. We nally propose a physics-based model of humanmobility patterns that can explain the scaling laws observed in the data.