SAFE: A Share-and-Aggregate Bandwidth Exploration Framework for Kernel Density Visualization

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

Research output: Contribution to journalConference articlepeer-review

6 Citations (Scopus)


Kernel density visualization (KDV) has been the de facto method in many spatial analysis tasks, including ecological modeling, crime hotspot detection, traffic accident hotspot detection, and disease outbreak detection. In these tasks, domain experts usually generate multiple KDVs with different bandwidth values. However, generating a single KDV, let alone multiple KDVs, is time-consuming. In this paper, we develop a share-and-aggregate framework, namely SAFE, to reduce the time complexity of generating multiple KDVs given a set of bandwidth values. On the other hand, domain experts can specify bandwidth values on the fly. To tackle this issue, we further extend SAFE and develop the exact method SAFEall and the 2-approximation method SAFEexp which reduce the time complexity under this setting. Experimental results on four large-scale datasets (up to 4.33M data points) show that these three methods achieve at least one-order-of-magnitude speedup for generating multiple KDVs in most of the cases without degrading the visualization quality.

Original languageEnglish
Pages (from-to)513-526
Number of pages14
JournalProceedings of the VLDB Endowment
Issue number3
Publication statusPublished - Nov 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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