Dr. rer. nat.

Hecker, Dirk

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  • Publication
    Modeling micro-movement variability in mobility studies
    ( 2011)
    Hecker, Dirk
    ;
    Körner, Christine
    ;
    Stange, Hendrik
    ;
    Schulz, Daniel
    ;
    May, Michael
    During the past years the interest in the exploitation of mobility information has increased significantly. Along with these interests, new demands on mobility data sets have been posed. One particular demand is the evaluation of movement data on a high level of spatial detail. The high dimensionality of geographic space, however, makes this requirement hard to fulfill. Even large mobility studies cannot guarantee to comprise all movement variation on a high level of detail. In this paper we present an approach to increase the variability of movement data on microscopic scale in order to achieve a better representation of population movement. Our approach consists of two steps. First, we perform a spatial aggregation of trajectory data in order to counteract sparseness and to preserve movement on macroscopic scale. Second, we disaggregate the data in geographic space based on traffic distribution knowledge using repeated simulation. Our approach is applied in a real-world business application for the Ger-man outdoor advertising industry to measure the performance of poster sites.
  • Publication
    Spatial data mining in practice
    ( 2010)
    Körner, Christine
    ;
    Hecker, Dirk
    ;
    Krause-Traudes, Maike
    ;
    May, Michael
    ;
    Scheider, Simon
    ;
    Schulz, Daniel
    ;
    Stange, Hendrik
    ;
    Wrobel, Stefan
    Almost any data can be referenced in geographic space. Such data permit advanced analyses that utilize the position and relationships of objects in space as well as geographic background information. Even though spatial data mining is still a young research discipline, in the past years research advances have shown that the particular challenges of spatial data can be mastered and that the technology is ready for practical application when spatial aspects are treated as an integrated part of data mining and model building. In this chapter in particular, we give a detailed description of several customer projects that we have carried out and which all involve customized data mining solutions for business relevant tasks. The applications range from customer segmentation to the prediction of traffic frequencies and the analysis of GPS trajectories. They have been selected to demonstrate key challenges, to provide advanced solutions and to arouse further research questions.
  • Publication
    A vector-geometry based spatial kNN-algorithm for traffic frequency predictions
    ( 2008)
    May, Michael
    ;
    Hecker, Dirk
    ;
    Körner, Christine
    ;
    Scheider, Simon
    ;
    Schulz, Daniel
    We introduce s-kNN, a nearest neighbor based spatial data mining algorithm. It belongs to the class of vector-geometry based algorithms that reason on complex spatial objects instead of point measurements. In contrast to most methods in this class, it does on the fly spatial computations that cannot be replaced by a preprocessing step without sacrificing efficiency. The key is a partial evaluation scheme for efficient computations. The algorithm is fully integrated into an object-relational spatial database. It is the basis for traffic frequency predictions (vehicles and pedestrians) for all German cities larger than 50,000 inhabitants and is the basis for pricing of posters in Germany.