SWS: A Complexity-Optimized Solution for Spatial-Temporal Kernel Density Visualization

Tsz Nam Chan, Pak Lon Ip, Leong Hou U, Byron Choi, Jianliang Xu

Research output: Contribution to journalConference articlepeer-review

10 Citations (Scopus)


Spatial-temporal kernel density visualization (STKDV) has been extensively used in a wide range of applications, e.g., disease outbreak analysis, traffic accident hotspot detection, and crime hotspot detection. While STKDV can provide accurate and comprehensive data visualization, computing STKDV is time-consuming, which is not scalable to large-scale datasets. To address this issue, we develop a new sliding-window-based solution (SWS), which theoretically reduces the time complexity for generating STKDV, without increasing the space complexity. Moreover, we incorporate SWS with the progressive visualization framework, which can continuously output partial visualization results to users (from coarse to fine), until users satisfy the visualization. Our experimental studies on five large-scale datasets show that SWS achieves 1.71x to 24x speedup compared with the state-of-the-art methods.
Original languageEnglish
Pages (from-to)814-827
Number of pages14
JournalProceedings of the VLDB Endowment
Issue number4
Publication statusPublished - Dec 2021
Event48th International Conference on Very Large Data Bases, VLDB 2022 - Sydney, Australia
Duration: 5 Sept 20229 Sept 2022

Scopus Subject Areas

  • Computer Science (miscellaneous)
  • Computer Science(all)


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