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

doi:10.14778/3554821.3554855

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

Department of Computer Science

Research output: Contribution to journal › Conference article › peer-review

11 Citations (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 language	English
Pages (from-to)	3606-3609
Number of pages	4
Journal	Proceedings of the VLDB Endowment
Volume	15
Issue number	12
DOIs	https://doi.org/10.14778/3554821.3554855
Publication status	Published - Aug 2022
Event	48th International Conference on Very Large Data Bases, VLDB 2022 - Sydney, Australia Duration: 5 Sept 2022 → 9 Sept 2022

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Access to Document

10.14778/3554821.3554855

https://www.vldb.org/pvldb/vol15/p3606-chan.pdf

Cite this

@article{419022c20f2e4ba99787309db442d98a,

title = "LIBKDV: A Versatile Kernel Density Visualization Library for Geospatial Analytics",

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.",

author = "Chan, \{Tsz Nam\} and Ip, \{Pak Lon\} and Kaiyan Zhao and U, \{Leong Hou\} and Byron Choi and Jianliang Xu",

note = "Funding Information: This work was supported by the National Key Research and Development Plan of China (No.2019YFB2102100), the Science and Technology Development Fund Macau (SKL-IOTSC-20212023, 0015/2019/AKP), University of Macau (MYRG2019-00119FST), IRCMS/19-20/H01, Hong Kong RGC Projects RIF R2002-20F, 12202221, and C2004-21GF. Pak Lon Ip, Kaiyan Zhao, and Leong Hou U are also affiliated with the State Key Laboratory of Internet of Things for Smart City. Publisher Copyright: Copyright is held by the owner/author(s). Publication rights licensed to the VLDB Endowment.; 48th International Conference on Very Large Data Bases, VLDB 2022 ; Conference date: 05-09-2022 Through 09-09-2022",

year = "2022",

month = aug,

doi = "10.14778/3554821.3554855",

language = "English",

volume = "15",

pages = "3606--3609",

journal = "Proceedings of the VLDB Endowment",

issn = "2150-8097",

publisher = "Very Large Data Base Endowment Inc.",

number = "12",

}

TY - JOUR

T1 - LIBKDV: A Versatile Kernel Density Visualization Library for Geospatial Analytics

AU - Chan, Tsz Nam

AU - Ip, Pak Lon

AU - Zhao, Kaiyan

AU - U, Leong Hou

AU - Choi, Byron

AU - Xu, Jianliang

N1 - Funding Information: This work was supported by the National Key Research and Development Plan of China (No.2019YFB2102100), the Science and Technology Development Fund Macau (SKL-IOTSC-20212023, 0015/2019/AKP), University of Macau (MYRG2019-00119FST), IRCMS/19-20/H01, Hong Kong RGC Projects RIF R2002-20F, 12202221, and C2004-21GF. Pak Lon Ip, Kaiyan Zhao, and Leong Hou U are also affiliated with the State Key Laboratory of Internet of Things for Smart City. Publisher Copyright: Copyright is held by the owner/author(s). Publication rights licensed to the VLDB Endowment.

PY - 2022/8

Y1 - 2022/8

N2 - 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.

AB - 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.

UR - http://vldb.org/pvldb/volumes/15/paper/LIBKDV%3A%20A%20Versatile%20Kernel%20Density%20Visualization%20Library%20for%20Geospatial%20Analytics

UR - https://www.scopus.com/pages/publications/85137975372

U2 - 10.14778/3554821.3554855

DO - 10.14778/3554821.3554855

M3 - Conference article

AN - SCOPUS:85137975372

SN - 2150-8097

VL - 15

SP - 3606

EP - 3609

JO - Proceedings of the VLDB Endowment

JF - Proceedings of the VLDB Endowment

IS - 12

T2 - 48th International Conference on Very Large Data Bases, VLDB 2022

Y2 - 5 September 2022 through 9 September 2022

ER -

LIBKDV: A Versatile Kernel Density Visualization Library for Geospatial Analytics

Abstract

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this