LIBKDV: A Versatile Kernel Density Visualization Library for Geospatial Analytics

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

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)

Abstract

Kernel density visualization (KDV) has been widely used in many geospatial analysis tasks, including traffic accident hotspot detection, crime hotspot detection, and disease outbreak detection. Although KDV can be supported by many scientific, geographical, and visualization software tools, none of these tools can support high-resolution KDV with large-scale datasets. Therefore, we develop the first versatile programming library, called LIBKDV, based on the set of our complexity-optimized algorithms. Given the high efficiency of these algorithms, LIBKDV not only accelerates the KDV computation but also enriches KDV-based geospatial analytics, including bandwidth-tuning analysis and spatiotemporal analysis, which cannot be natively and feasibly supported by existing software tools. In this demonstration, participants will be invited to use our programming library to explore interesting hotspot patterns on large-scale traffic accident, crime, and COVID-19 datasets.

Original languageEnglish
Pages (from-to)3606-3609
Number of pages4
JournalProceedings of the VLDB Endowment
Volume15
Issue number12
DOIs
Publication statusPublished - Aug 2022
Event48th International Conference on Very Large Data Bases, VLDB 2022 - Sydney, Australia
Duration: 5 Sep 20229 Sep 2022

Scopus Subject Areas

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

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