Kernel Density Visualization for Big Geospatial Data: Algorithms and Applications

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

doi:10.1109/MDM58254.2023.00046

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

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

2 Citations (Scopus)

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 language	English
Title of host publication	Proceedings - 2023 24th IEEE International Conference on Mobile Data Management, MDM 2023
Publisher	IEEE
Pages	231-234
Number of pages	4
Edition	1st
ISBN (Electronic)	9798350341010
ISBN (Print)	9798350341027
DOIs	https://doi.org/10.1109/MDM58254.2023.00046
Publication status	Published - 3 Jul 2023
Event	24th IEEE International Conference on Mobile Data Management, MDM 2023 - , Singapore Duration: 3 Jul 2023 → 6 Jul 2023 https://ieeexplore.ieee.org/xpl/conhome/10214688/proceeding (Conference proceedings)

Publication series

Name	Proceedings - IEEE International Conference on Mobile Data Management
Volume	2023-July
ISSN (Print)	1551-6245
ISSN (Electronic)	2375-0324

Conference

Conference	24th IEEE International Conference on Mobile Data Management, MDM 2023
Country/Territory	Singapore
Period	3/07/23 → 6/07/23
Internet address	https://ieeexplore.ieee.org/xpl/conhome/10214688/proceeding (Conference proceedings)

Scopus Subject Areas

General Engineering

User-Defined Keywords

Kernel density visualization
efficient algorithms
efficient software development

UN SDGs

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

Access to Document

10.1109/MDM58254.2023.00046

Cite this

Chan, T. N., Leong , H. U., Choi, B., Xu, J., & Cheng, R. (2023). Kernel Density Visualization for Big Geospatial Data: Algorithms and Applications. In Proceedings - 2023 24th IEEE International Conference on Mobile Data Management, MDM 2023 (1st ed., pp. 231-234). (Proceedings - IEEE International Conference on Mobile Data Management; Vol. 2023-July). IEEE. https://doi.org/10.1109/MDM58254.2023.00046

@inproceedings{5f4306e34d164c4f87f8f3d5a8b5e81c,

title = "Kernel Density Visualization for Big Geospatial Data: Algorithms and Applications",

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.",

keywords = "Kernel density visualization, efficient algorithms, efficient software development",

author = "Chan, {Tsz Nam} and Leong, {Hou U.} and Byron Choi and Jianliang Xu and Reynold Cheng",

note = "Funding Information: This work was supported by the NSFC grant 62202401, the Science and Technology Development Fund Macau SAR 0015/2019/AKP, 0031/2022/A, SKL-IOTSC-2021-2023, the Research Grant of University of Macau MYRG2022-00252-FST, Wuyi University Hong Kong and Macau joint Research Fund 2021WGALH14, HKRGC RIF R2002-20F, HKRGC C2004-21GF, GDNSF 2019B1515130001, the University of Hong Kong (Projects 104005858 and 10400599), the Guangdong–Hong Kong-Macau Joint Laboratory Program 2020 (Project No: 2020B1212030009), and the Hong Kong Jockey Club Charities Trust 260920140. Leong Hou U is also affiliated with the State Key Laboratory of Internet of Things for Smart City, UM, and with the Centre for Data Science, UM. Publisher Copyright: {\textcopyright} 2023 IEEE.; 24th IEEE International Conference on Mobile Data Management, MDM 2023 ; Conference date: 03-07-2023 Through 06-07-2023",

year = "2023",

month = jul,

day = "3",

doi = "10.1109/MDM58254.2023.00046",

language = "English",

isbn = "9798350341027",

series = "Proceedings - IEEE International Conference on Mobile Data Management",

publisher = "IEEE",

pages = "231--234",

booktitle = "Proceedings - 2023 24th IEEE International Conference on Mobile Data Management, MDM 2023",

address = "United States",

edition = "1st",

url = "https://ieeexplore.ieee.org/xpl/conhome/10214688/proceeding",

}

Chan, TN, Leong , HU, Choi, B , Xu, J & Cheng, R 2023, Kernel Density Visualization for Big Geospatial Data: Algorithms and Applications. in Proceedings - 2023 24th IEEE International Conference on Mobile Data Management, MDM 2023. 1st edn, Proceedings - IEEE International Conference on Mobile Data Management, vol. 2023-July, IEEE, pp. 231-234, 24th IEEE International Conference on Mobile Data Management, MDM 2023, Singapore, 3/07/23. https://doi.org/10.1109/MDM58254.2023.00046

Kernel Density Visualization for Big Geospatial Data: Algorithms and Applications. / Chan, Tsz Nam; Leong , Hou U.; Choi, Byron et al.
Proceedings - 2023 24th IEEE International Conference on Mobile Data Management, MDM 2023. 1st. ed. IEEE, 2023. p. 231-234 (Proceedings - IEEE International Conference on Mobile Data Management; Vol. 2023-July).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

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AU - Leong , Hou U.

AU - Choi, Byron

AU - Xu, Jianliang

AU - Cheng, Reynold

N1 - Funding Information: This work was supported by the NSFC grant 62202401, the Science and Technology Development Fund Macau SAR 0015/2019/AKP, 0031/2022/A, SKL-IOTSC-2021-2023, the Research Grant of University of Macau MYRG2022-00252-FST, Wuyi University Hong Kong and Macau joint Research Fund 2021WGALH14, HKRGC RIF R2002-20F, HKRGC C2004-21GF, GDNSF 2019B1515130001, the University of Hong Kong (Projects 104005858 and 10400599), the Guangdong–Hong Kong-Macau Joint Laboratory Program 2020 (Project No: 2020B1212030009), and the Hong Kong Jockey Club Charities Trust 260920140. Leong Hou U is also affiliated with the State Key Laboratory of Internet of Things for Smart City, UM, and with the Centre for Data Science, UM. Publisher Copyright: © 2023 IEEE.

PY - 2023/7/3

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N2 - 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.

AB - 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.

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KW - efficient software development

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DO - 10.1109/MDM58254.2023.00046

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Kernel Density Visualization for Big Geospatial Data: Algorithms and Applications

Abstract

Publication series

Conference

Scopus Subject Areas

User-Defined Keywords

UN SDGs

Access to Document

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