Hierarchical Regions of Interest

Mining crowd-sourced movement trajectories is a useful tool in urban computing. Common mobility patterns of the visitors or residents of a city can be exploited in applications such as disaster management, transportation planning and ad placement. In recommendation systems, individual behaviour is of special interest. To extract the visiting behaviour of individuals, the trajectories need to be semantically annotated. We describe how hierarchical regions of interest (ROIs) can be used for semantic annotation. By combining multiple layers of smaller and larger regions we can flexibly detect both visits to dense hotspots and trajectory segments visiting larger areas, such as an old town, a park or an island. Extending the annotation beyond common hotspots captures more information about the behaviour.