Infostop: Scalable stop-location detection in multi-user mobility data

Understand

Data-driven research in mobility has prospered in recent years, providing solutions to real-world challenges including forecasting epidemics and planning transportation.

• These advancements were facilitated by computational tools enabling the analysis of large-scale data-sets of digital traces.
• One of the challenges when pre-processing spatial trajectories is the so-called stop location detection, that entails the reduction of raw time series to sequences of destinations where an individual was stationary.
• The most widely adopted solution to this problem was proposed by Hariharan and Toyama (2004) and involves filtering out non-stationary measurements, then applying agglomerative clustering on the stationary points.