Classification and visualization for symbolic people flow data

Abstract

People flow information brings us useful knowledge in various industrial and social fields including traffic, disaster prevention, and marketing. However, it is still an open problem to develop effective people flow analysis techniques. We considered compression and data mining techniques are especially important for analysis and visualization of large-scale people flow datasets. This paper presents a visualization method for large-scale people flow dataset featuring compression and data mining techniques. This method firstly compresses the people flow datasets using UniversalSAX, an extended method of SAX (Symbolic Aggregate Approximation). Next, we apply algorithms inspired by natural language processing to extract movement patterns and classify walking routes. After this process, users can interactively observe trajectories and extracted features such as congestions and popular walking routes using a visualization tool. We had experiments of classifying and visualizing walking routes using two types of people flow dataset recorded at an exhibition and a corridor applying our method. The results allow us to discover characteristic movements such as stopping in front of particular exhibits, or persons who passed same places but walked at different speeds.