Modeling Checkpoint-Based Movement with the Earth Mover's Distance

Movement data comes in various forms, including trajectory data and checkpoint data. While trajectories give detailed information about the movement of individual entities, checkpoint data in its simplest form does not give identities, just counts at checkpoints. However, checkpoint data is of increasing interest since it is readily available due to privacy reasons and as a by-product of other data collection. In this paper we propose to use the Earth Mover’s Distance as a versatile tool to reconstruct individual movements or flow based on checkpoint counts at different times. We analyze the modeling possibilities and provide experiments that validate model predictions, based on coarse-grained aggregations of data about actual movements of couriers in London, UK. While we cannot expect to reconstruct precise individual movements from highly granular checkpoint data, the evaluation does show that the approach can generate meaningful estimates of object movements. B. Speckmann and K. Verbeek are supported by the Netherlands Organisation for Scientific Research (NWO) under project nos. 639.023.208 and 639.021.541, respectively. This paper arose from work initiated at Dagstuhl seminar 12512 “Representation, analysis and visualization of moving objects”, December 2012. The authors gratefully acknowledge Schloss Dagstuhl for their support

Player Valuation in European Football

As the success of a team depends on the performance of individual players, the valuation of player performance has become an important research topic. In this paper, we compare and contrast which attributes and skills best predict the success of individual players in their positions in five European top football leagues. Further, we evaluate different machine learning algorithms regarding prediction performance. Our results highlight features distinguishing top-tier players and show that prediction performance is higher for forwards than for other positions, suggesting that equally good prediction of defensive players may require more advanced metrics

Movement patterns in spatio-temporal data

2nd editio

Predicting and Explaining Privacy Risk Exposure in Mobility Data

Mobility data is a proxy of different social dynamics and its analysis enables a wide range of user services. Unfortunately, mobility data are very sensitive because the sharing of people\u2019s whereabouts may arise serious privacy concerns. Existing frameworks for privacy risk assessment provide tools to identify and measure privacy risks, but they often (i) have high computational complexity; and (ii) are not able to provide users with a justification of the reported risks. In this paper, we propose expert, a new framework for the prediction and explanation of privacy risk on mobility data. We empirically evaluate privacy risk on real data, simulating a privacy attack with a state-of-the-art privacy risk assessment framework. We then extract individual mobility profiles from the data for predicting their risk. We compare the performance of several machine learning algorithms in order to identify the best approach for our task. Finally, we show how it is possible to explain privacy risk prediction on real data, using two algorithms: Shap, a feature importance-based method and Lore, a rule-based method. Overall, expert is able to provide a user with the privacy risk and an explanation of the risk itself. The experiments show excellent performance for the prediction task