Dr. rer. nat.

Hecker, Dirk

Filters
2 1
Reset filters

Settings
Now showing 1 - 3 of 3
No Thumbnail Available
Publication

Topic modelling for spatial insights: Uncovering space use from movement data

2024-08-01 , Andriyenko, Gennadiy , Andriyenko, Nathaliya , Hecker, Dirk

We present a novel approach to understanding space use by moving entities based on repeated patterns of place visits and transitions. Our approach represents trajectories as text documents consisting of sequences of place visits or transitions and applies topic modelling to the corpus of these documents. The resulting topics represent combinations of places or transitions, respectively, that repeatedly co-occur in trips. Visualisation of the results in the spatial context reveals the regions of place connectivity through movements and the major channels used to traverse the space. This enables understanding of the use of space as a medium for movement. We compare the possibilities provided by topic modelling to alternative approaches exploiting a numeric measure of pairwise connectedness. We have extensively explored the potential of utilising topic modelling by applying our approach to multiple real-world movement data sets with different data collection procedures and varying spatial and temporal properties: GPS road traffic of cars, unconstrained movement on a football pitch, and episodic movement data reflecting social media posting events. The approach successfully demonstrated the ability to uncover meaningful patterns and interesting insights. We thoroughly discuss different aspects of the approach and share the knowledge and experience we have gained with people who might be potentially interested in analysing movement data by means of topic modelling methods.

View
No Thumbnail Available
Publication

Extracting Movement-based Topics for Analysis of Space Use

2023 , Andriyenko, Gennadiy , Andriyenko, Nathaliya , Hecker, Dirk

We present a novel approach to analyze spatio-temporal movement patterns using topic modeling. Our approach represents trajectories as sequences of place visits and moves, applies topic modeling separately to each collection of sequences, and synthesizes results. This supports the identification of dominant topics for both place visits and moves, the exploration of spatial and temporal patterns of movement, enabling understanding of space use. The approach is applied to two real-world data sets of car movements in Milan and UK road traffic, demonstrating the ability to uncover meaningful patterns and insights.

View
No Thumbnail Available
Publication

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.

View