Large-Scale Spatiotemporal Kernel Density Visualization
Tsz Nam Chan, Pak Lon Ip, Bojian Zhu, Leong Hou U, Dingming Wu, Jianliang Xu, Christian S. Jensen · 2025
Spatiotemporal kernel density visualization (STKDV) is used extensively for many geospatial analysis tasks, including traffic accident hotspot detection, crime hotspot detection, and disease outbreak detection. However, STKDV is a computationally expensive operation, which does not scale to large-scale datasets, high resolutions, and a large number of timestamps. Although a recent approach, the sliding-window-based solution (SWS), reduces the time complexity of STKDV, it (i) is unable to reduce the time complexity for supporting STKDV-based exploratory analysis, (ii) is not theoretically efficient, and (iii) does not provide optimization techniques for bandwidth tuning. To eliminate these drawbacks, we propose a prefix-set-based solution (PREFIX) that encompasses three methods, namely PREFIXsingle(addressing (i)), PREFIXmultiple(addressing (ii)), and PREFIXtuning(addressing (iii)). We offer theoretical and practical evidence that PREFIX is capable of outperforming the state-of-the-art solution (SWS). In particular, PREFIX achieves at least 115x to 1,906x speedups and is the first solution that can efficiently generate multiple high-resolution STKDVs for the large-scale New York taxi dataset with 13.6 million data points.