Kernel Density Visualization for Big Geospatial Data: Algorithms and Applications

Tsz Nam Chan*, Hou U. Leong , Byron Choi, Jianliang Xu, Reynold Cheng

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

Abstract

The use of Kernel Density Visualization (KDV) has become widespread in a number of disciplines, including geography, crime science, transportation science, and ecology, for analyzing geospatial data. However, the growing scale of massive geospatial data has rendered many commonly used software tools unable of generating high-resolution KDVs, leading to concerns about the inefficiency of KDV. This 90-minute tutorial aims to raise awareness among database researchers about this important, emerging, database-related, and interdisciplinary topic. It is structured into four parts: a thorough discussion of the background of KDV, a review of state-of-the-art methods for generating KDVs, a discussion of key variants of KDV, including network kernel density visualization (NKDV) and spatiotemporal kernel density visualization (STKDV), and an outline of future directions for this topic.

Original languageEnglish
Title of host publicationProceedings - 2023 24th IEEE International Conference on Mobile Data Management, MDM 2023
PublisherIEEE
Pages231-234
Number of pages4
Edition1st
ISBN (Electronic)9798350341010
ISBN (Print)9798350341027
DOIs
Publication statusPublished - 3 Jul 2023
Event24th IEEE International Conference on Mobile Data Management, MDM 2023 - , Singapore
Duration: 3 Jul 20236 Jul 2023
https://ieeexplore.ieee.org/xpl/conhome/10214688/proceeding (Conference proceedings)

Publication series

NameProceedings - IEEE International Conference on Mobile Data Management
Volume2023-July
ISSN (Print)1551-6245
ISSN (Electronic)2375-0324

Conference

Conference24th IEEE International Conference on Mobile Data Management, MDM 2023
Country/TerritorySingapore
Period3/07/236/07/23
Internet address

Scopus Subject Areas

  • Engineering(all)

User-Defined Keywords

  • Kernel density visualization
  • efficient algorithms
  • efficient software development

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