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Visualizing Crowd Movement Patterns Using a Directed Kernel Density Estimation

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Earth Observation of Global Changes (EOGC)

Part of the book series: Lecture Notes in Geoinformation and Cartography ((LNGC))

Abstract

“Classic” kernel density estimations (KDE) can display static densities representing one point in time. It is not possible to visually identify which parts of the densities are moving. Therefore, within this paper we investigate how to display dynamic densities (and the density changes) to identify movement patterns. To deal with a temporal dimension (in our case study a dynamic crowd of individuals) we investigated the application of directed kernel density estimation (DKDE). In a case study we apply the DKDE to a point dataset presenting individuals approaching the Allianz Arena in Munich, Germany, with different speeds from different directions. Calculating the density using a directed kernel with this data, results in a density map indicating the movement direction with a visible “ripple” effect. Ripples move at different rates to the substances in which they occur. That tells us something about crowd dynamics and enables us to visually recognize the parts of the crowds that are moving plus the underlying movement directions.

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Acknowledgments

We would like to thank Dr. Matthias Butenuth for his valuable comments in the preparation of this paper, especially sharing his expertise in the acquisition process of the population data. This research has been partly supported by the TUM IGSSE project 7.07.

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Authors and Affiliations

  1. Technische Universität München (TUM), Bauingenieur- und Vermessungswesen, Kartographie (LFK), Arcisstraße 21, 80333, Munich, Germany

    Jukka M. Krisp & Stefan Peters

  2. Technische Universität München (TUM), Remote Sensing Technology, Arcisstraße 21, 80333, Munich, Germany

    Florian Burkert

Authors
  1. Jukka M. Krisp
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  2. Stefan Peters
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  3. Florian Burkert
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Corresponding author

Correspondence to Jukka M. Krisp .

Editor information

Editors and Affiliations

  1. Technische Universität München (TUM), Munich, Germany

    Jukka M. Krisp

  2. Technische Universität München (TUM), Munich, Germany

    Liqiu Meng

  3. Technische Universität München (TUM), Munich, Germany

    Roland Pail

  4. Technische Universität München (TUM), Munich, Germany

    Uwe Stilla

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Krisp, J.M., Peters, S., Burkert, F. (2013). Visualizing Crowd Movement Patterns Using a Directed Kernel Density Estimation. In: Krisp, J., Meng, L., Pail, R., Stilla, U. (eds) Earth Observation of Global Changes (EOGC). Lecture Notes in Geoinformation and Cartography. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32714-8_17

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