Spatiotemporal Analysis of Circulation Behaviors Using Path and Residing Time displaY (PARTY)

Abstract

Spatiotemporal data displayed in a spatial layout are not the best visualization for simultaneously exploring visitor paths and comparing their residing time. A challenging problem is the visual analytics of circulation patterns in varying layouts commonly found in both real and virtual environments. A circulation pattern is defined as how visitors move within space regarding a sequence of visited items. Understanding and discovering the circulation patterns has received much attention from space designers for increasing visitor satisfaction. This paper proposes a layout-independent visualization approach that transforms the four dimensional data of visitor trajectories (3D-position + time) into time series data. One time series datum represents a visitor path and his/her time spent residing in each particular region. In our visualization, we encode a time interval residing in an item boundary into a line segment. The length of a segment is in proportion to the total time spent in the layout. The time segment is placed in the row corresponding to its item boundary. A path of visited items is illustrated by connecting the time segments with vertical lines. The resulting visualization technique, called Path And Residing Time displaY (PARTY), enables users to find trends of circulation behaviors in a consistent fashion regardless of the targeted layout. The scalability of PARTY can be enhanced by a clustering technique which we enable PARTY to handle real data of more than two thousand visitors. We demonstrate the effectiveness of PARTY on these datasets. First, we show circulation behaviors of visiting styles in a 3D virtual museum. Second, we illustrate a flow of people who escaped from an explosive in a building. Third, we analyze players' patterns in a massively multiplayer online game, named Angel Love Online.

Footprints in the sky: using student track logs from a “bird's eye view” virtual field trip to enhance learning

“…Tracking data has been gathered on avatars moving in 2-D virtual space in games, virtual museums and VFTs, with various different techniques used to visualise the data (Börner & Penumarthy, 2003;Coulton et al, 2008;Oda et al, 2009;Sookhanaphibarn et al, 2011). Most pertinent for visualising our 4-D data, Chittaro and Ieronutti (2004) split the floor of a virtual museum into a grid of cells, recording the tracks of virtual visitors through the grid.…”