CheetahTraj: Efficient Visualization for Large Trajectory Dataset With Quality Guarantee

Visualizing large-scale trajectory dataset is a core subroutine for many applications. However, rendering all trajectories could result in severe visual clutter and incur long visualization delays due to large data volume. Naively sampling the trajectories reduces visualization time but usually harms visual quality, i.e., the generated visualizations may look substantially different from the exact ones without sampling. In this paper, we propose CheetahTraj, a principled sampling framework that achieves both high visualization quality and low visualization latency. We first define the
visual quality function
measuring the similarity between two visualizations, based on which we formulate the quality optimal sampling problem (QOSP). To solve QOSP, we design the
V
isual
Q
uality
G
uaranteed
S
ampling algorithms, which reduce visual clutter while guaranteeing visual quality by considering both trajectory data distribution and human perception properties. We also develop a quad-tree-based index (InvQuad) that allows using trajectory samples computed offline for interactive online visualization. Extensive experiments including case-, user-, and quantitative-studies are conducted on three real-world trajectory datasets, and the results show that CheetahTraj consistently provides higher visual quality and better efficiency than baseline methods. Compared with visualizing all trajectories, CheetahTraj reduces the visualization latency by up to 3 orders of magnitude while avoiding visual clutter.