Context Trees: Augmenting Geospatial Trajectories with Context

Exposing latent knowledge in geospatial trajectories has the potential to provide a better understanding of the movements of individuals and groups. Motivated by such a desire, this work presents the context tree, a new hierarchical data structure that summarises the context behind user actions in a single model. We propose a method for context tree construction that augments geospatial trajectories with land usage data to identify such contexts. Through evaluation of the construction method and analysis of the properties of generated context trees, we demonstrate the foundation for understanding and modelling behaviour afforded. Summarising user contexts into a single data structure gives easy access to information that would otherwise remain latent, providing the basis for better understanding and predicting the actions and behaviours of individuals and groups. Finally, we also present a method for pruning context trees, for use in applications where it is desirable to reduce the size of the tree while retaining useful information

Find your Way by Observing the Sun and Other Semantic Cues

In this paper we present a robust, efficient and affordable approach to self-localization which does not require neither GPS nor knowledge about the appearance of the world. Towards this goal, we utilize freely available cartographic maps and derive a probabilistic model that exploits semantic cues in the form of sun direction, presence of an intersection, road type, speed limit as well as the ego-car trajectory in order to produce very reliable localization results. Our experimental evaluation shows that our approach can localize much faster (in terms of driving time) with less computation and more robustly than competing approaches, which ignore semantic information

An approach to compute user similarity for GPS applications

The proliferation of GPS enabled devices has led people to share locations both consciously and unconsciously. Large spatio-temporal data comprising of shared locations and whereabouts are now being routinely collected for analysis. As user movements are generally driven by their interests, so mining these mobility patterns can reveal commonalities between a pair of users. In this paper, we present a framework for mining the published trajectories to identify patterns in user mobility. In this framework, we extract the locations where a user stays for a period of time popularly known as stay points. These stay points help to identify the interests of a user. The statistics of pattern and check-in distributions over the GPS data are used to formulate similarity measures for finding K-nearest neighbors of an active user. In this work, we categorize the neighbors into three groups namely strongly similar, closely similar and weakly similar. We introduce three similarity measures to determine them, one for each of the categories. We perform experiments on a real-world GPS log data to find the similarity scores between a pair of users and subsequently find the effective K-neighbors. Experimental results show that our proposed metric outperforms existing metrics in literature

Towards trajectory anonymization: a generalization-based approach

Trajectory datasets are becoming popular due to the massive usage of GPS and locationbased services. In this paper, we address privacy issues regarding the identification of individuals in static trajectory datasets. We first adopt the notion of k-anonymity to trajectories and propose a novel generalization-based approach for anonymization of trajectories. We further show that releasing anonymized trajectories may still have some privacy leaks. Therefore we propose a randomization based reconstruction algorithm for releasing anonymized trajectory data and also present how the underlying techniques can be adapted to other anonymity standards. The experimental results on real and synthetic trajectory datasets show the effectiveness of the proposed techniques