3D indoor movement analysis and visualization utilizing bluetooth tracking and spatial bayesian networks

Visual analysis of trajectory data became a common approach during the past years. Considering advances in pedestrian tracking technology, Bluetooth tracking data received recent attention. In this paper we present a fast, model-based approach for computationally enabled visual exploration of location dependencies in Bluetooth tracking data sets. Existing approaches are not suitable for visual dependency analysis as the size and complexity of trajectory data constrain ad-hoc and advance computations. Also recent developments in the area of trajectory data warehouses cannot be applied because the spatial correlations are lost during trajectory aggregation. Our approach builds a compact Spatial Bayesian Network model, which represents the dependency structures of the data. The user queries a re answered using this intermediate model instead of the complete data set. Visualization is connected by Open Geographic Consortium compliant protocols and uses 3D Dirichlet-Voronoi tessellation. This paper presents the approach and applies it on a soccer match dataset.