Large-Scale Spatiotemporal Kernel Density Visualization

Tsz Nam Chan, Pak Lon Ip, Bojian Zhu, Leong Hou U, Dingming Wu, Jianliang Xu, Christian S. Jensen

Abstract: 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.

External IDs:dblp:conf/icde/ChanIZUWXJ25