Enhancing data privacy with semantic trajectories: A raster‐based framework for GPS stop/move management

Tracking facilities on smartphones generate enormous amounts of GPS trajectories, which provide new opportunities to study movement patterns and improve transportation planning. Converting GPS trajectories into semantically meaningful trips is attracting increasing research effort with respect to the development of algorithms, frameworks, and software tools. There are, however, few works focused on designing new semantic enrichment functionalities taking privacy into account. This article presents a raster‐based framework which not only detects significant stop locations, segments GPS records into stop/move structures, and brings semantic insights to trips, but also provides possibilities to anonymize users’ movements and sensitive stay/move locations into raster cells/regions so that a multi‐level data sharing structure is achieved for a variety of data sharing purposes.