KDV-Explorer: A Near Real-Time Kernel Density Visualization System for Spatial Analysis

Tsz Nam Chan, Pak Lon Ip, U. Leong Hou, Weng Hou Tong, Shivansh Mittal, Ye Li, Reynold Cheng

Research output: Contribution to journalConference articlepeer-review

15 Citations (Scopus)


Kernel density visualization (KDV) is a commonly used visualization tool for many spatial analysis tasks, including disease outbreak detection, crime hotspot detection, and traffic accident hotspot detection. Although the most popular geographical information systems, e.g., QGIS, and ArcGIS, can also support this operation, these solutions are not scalable to generate a single KDV for datasets with million-scale data points, let alone to support exploratory operations (e.g., zoom in, zoom out, and panning operations) with KDV in near real-time (< 5 sec). In this demonstration, we develop a near real-time visualization system, called KDV-Explorer, that is built on top of our prior study on the efficient kernel density computation. Participants will be invited to conduct some kernel density analysis on three large-scale datasets (up to 1.3 million data points), including the traffic accident dataset, crime dataset and COVID-19 dataset. We will also compare the performance of our solution and the solutions in QGIS and ArcGIS.

Original languageEnglish
Pages (from-to)2655-2658
Number of pages4
JournalProceedings of the VLDB Endowment
Issue number12
Publication statusPublished - Jul 2021
Event47th International Conference on Very Large Data Bases, VLDB 2021 - Virtual, Online
Duration: 16 Aug 202120 Aug 2021

Scopus Subject Areas

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


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