There is an increasing number of rapidly growing repositories capturing the movement of people in space-time. Movement trajectory compression becomes an obvious necessity for coping with such growing data volumes. This paper introduces the concept of semantic trajectory compression (STC). STC allows for substantially compressing trajectory data with acceptable information loss. It exploits that human urban mobility typically occurs in transportation networks that define a geographic context for the movement. In STC, a semantic representation of the trajectory that consists of reference points localized in a transportation network replaces raw, highly redundant position information (e.g., from GPS receivers). An experimental evaluation with real and synthetic trajectories demonstrates the power of STC in reducing trajectories to essential information and illustrates how trajectories can be restored from compressed data. The paper discusses possible application areas of STC trajectories

Abstract. In the light of rapidly growing repositories capturing the movement trajectories of people in spacetime, the need for trajectory 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 trajectory consisting of a sequence of events. The paper explains the underlying principles of STC and presents an example use case.

Abstract. The availability of technology and tools enables the public to participate in the collection, contribution, editing, and usage of geographic information, a domain previously reserved for mapping agencies or companies. The data of Volunteered Geographic Information (VGI) systems, such as OpenStreetMap (OSM), is based on the availability of technology and participation of individuals. However, this combination also implies quality issues related to the data: some of the contributed entities can be assigned to wrong or implausible classes, due to individual interpretation of the submitted data, or due to misunderstanding about available classes. In this paper we propose two methods to check the integrity of VGI data with respect to hierarchical consistency and classification plausibility. These methods are based on constraint checking and machine learning methods. They can be used to check the validity of data during contribution or at a later stage for collaborative manual or automatic data correction.

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