A Linked Visualization of Trajectory and Flow Quantity to Support Analysis of People Flow

Thanks to the recent evolution of movie- and sensor-based human tracking technologies, we can obtain and accumulate a set of walking paths ("trajectories" in this paper) of people over a long period in various places. Such people flow datasets are useful for many fields, including analyses of customer behavior, effectiveness of advertisements, and operational efficiency. This paper presents a linked visualization system to assist in the discovery of new knowledge by analyzing the accumulated people flow datasets, and a case study using this system. In this study we suppose the people flow datasets consist of a set of trajectories and temporal flow quantity. The system consists of two visualization components: classified trajectory visualization, and temporal flow quantity visualization. The former component classifies trajectories into several patterns applying the spectral clustering algorithm, and visualizes the patterns by colors on a physical space. The latter component displays temporal flow quantity of the above patterns applying a piled polygonal chart. This paper introduces a case study applying a movie-based human tracking dataset to the presented system.