Dr. rer. nat.

Hecker, Dirk

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Constructing semantic interpretation of routine and anomalous mobility behaviors from big data

2015 , Fuchs, Georg , Stange, Hendrik , Hecker, Dirk , Andrienko, Natalia , Andrienko, Gennady

Annually organized VAST Challenges provide a unique opportunity to analyze complex data with available ground truth. In 2014, one of the tasks was to interpret routine and anomalous patterns of human mobility based on big data: trajectories of cars and credit card transactions. We describe a scalable visual analytics approach to solving this problem. Repeatedly visited personal and public places were extracted from trajectories by finding spatial clusters of stop points. Temporal patterns of peoples presence in the places resulted from spatio-temporal aggregation of the data by the places and hourly intervals within the weekly cycle. Based on these patterns, we identified the meanings or purposes of the places: home, work, breakfast, lunch and dinner, etc. Meanings of some places could be refined using the credit card transaction data. By representing the place meanings as points on a 2D plane, we built an abstract semantic space and transformed the original trajectories to trajectories in the semantic space, i.e., performed semantic abstraction of the data. Spatio-temporal aggregation of the transformed trajectories into flows between the semantic places and subsequent clustering of time intervals by the similarity of the flow situations allowed us to reveal and analyze the routine movement behaviors. To detect anomalies, we (a) investigated the visits to the places with unknown meanings, and (b) looked for unusual presence times or visit durations at different semantic places. The analysis is scalable since all tools and methods can be applied to much larger data. Moreover, the semantic data abstraction can serve as a tool for protecting the personal privacy.

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Analytical workflow of monitoring human mobility in big event settings using bluetooth

2011 , Stange, Hendrik , Liebig, Thomas , Hecker, Dirk , Andrienko, Gennady , Andrienko, Natalia

In recent times, consumer research at major social events received significant interest by organizing companies. Understanding the movements and motivations of the customers enables new business strategies and is needed to minimize the risk of investment. The spatiotemporal complexity of major events poses high demands on survey and analytical methods. New technological advances in both event monitoring systems and evaluation methods of movement data provide new insights into the behavioral patterns of customers by preserving their privacy. In this paper we present a work that seeks to systematize the research process of design, collection, and analysis of visitor behavior in a mixed indoor-outdoor event setting using Bluetooth sensor technology. The defined workow is comprised of 5 steps and designed to answer heterogeneous business questions with respect to customer movement behavior in a single event context. Our approach is applied in a real-world business applicati on for a Formula 1 event.

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Using Bluetooth to track mobility patterns: Depicting its potential based on various case studies

2013 , Ellersiek, Timothy , Andrienko, Gennady , Andrienko, Natalia , Hecker, Dirk , Stange, Hendrik , Müller, Marc

During the past years the interest in the exploitation of mobility information has increased significantly. A growing number of companies and research institutions are interested in the analysis of mobility data with demand of a high level of spatial detail. Means of tracking persons in our environment can nowadays be fulfilled by utilizing several technologies, for example the Bluetooth technology, offering means to obtain movement data. This paper gives an overview of four case studies in the field of Bluetooth tracking which were conducted in order to provide helpful insights on movement aspects for decision makers in their specific microcosm. Aim is to analyse spatio-temporal validity of Bluetooth tracking, and in doing so, to describe the potential of Bluetooth in pedestrian mobility mining.

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Visual analytics for understanding spatial situations from episodic movement data

2012 , Andrienko, Natalia , Andrienko, Gennady , Stange, Hendrik , Liebig, Thomas , Hecker, Dirk

Continuing advances in modern data acquisition techniques result in rapidly growing amounts of georeferenced data about moving objects and in emergence of new data types.We define episodic movement data as a new complex data type to be considered in the research fields relevant to data analysis. In episodic movement data, position measurements may be separated by large time gaps, in which the positions of the moving objects are unknown and cannot be reliably reconstructed. Many of the existing methods for movement analysis are designed for data with fine temporal resolution and cannot be applied to discontinuous trajectories. We present an approach utilising Visual Analytics methods to explore and understand the temporal variation of spatial situations derived from episodic movement data b y means of spatio-temporal aggregation. The situations are defined in terms of the presence of moving objects in different places and in terms of flows (collective movements) between the places. The approach, which combines interactive visual displays with clustering of the spatial situations, is presented by example of a real dataset collected by Bluetooth sensors.

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