Visualizing Crowd Movement Patterns Using a Directed Kernel Density Estimation

“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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