Finding REMO — Detecting Relative Motion Patterns in Geospatial Lifelines

Abstract

Technological advances in position aware devices increase the availability of tracking data of everyday objects such as animals, vehicles, people or football players. We propose a geographic data mining approach to detect generic aggregation patterns such as flocking behaviour and convergence in geospatial lifeline data. Our approach considers the object's motion properties in an analytical space as well as spatial constraints of the ob- ject's lifelines in geographic space. We discuss the geometric properties of the formalised patterns with respect to their efficient computation.