In the light of rapidly growing repositories capturing the
movement tra jectories of people in spacetime, the need for tra jectory compression becomes obvious. This paper argues for semantic trajectory compression (STC) as a means of substantially compressing the movement trajectories in an urban environment with acceptable information loss. STC exploits that human urban movement and its large–scale use
(LBS, navigation) is embedded in some geographic context, typically deﬁned by transportation networks. STC achieves its compression rate by replacing raw, highly redundant position information from, for example, GPS sensors with a semantic representation of the tra jectory consisting
of a sequence of events. The paper explains the underlying principles of STC and presents an example use case.