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LIBKDV: A Versatile Kernel Density Visualization Library for Geospatial Analytics

Summary: LIBKDV: a versatile kernel density visualization library for geospatial analytics. Complexity-optimized algorithms enable high-resolution KDV on large-scale data, accelerating computation and enabling bandwidth-tuning and spatiotemporal analyses beyond existing tools. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
h9fcddb4f71b9d674
Venue
VLDB
Year
2022
Pagerank
-
Overall Rank
13,737 | 7.64%
DOI
10.14778/3554821.3554855

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

BibTeX Citation

@article{chan_vldb22,
        title = {{LIBKDV: A Versatile Kernel Density Visualization Library for Geospatial Analytics}},
        author = {Chan, Tsz Nam and Ip, Pak Lon and Zhao, Kaiyan and U, Leong Hou and Choi, Byron and Xu, Jianliang},
        journal = {PVLDB},
        series = {{VLDB} '22},
        volume = {15},
        number = {12},
        pages = {3606--3609},
        doi = {10.14778/3554821.3554855},
        url = {https://doi.org/10.14778/3554821.3554855},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
9,809 Fast Network K-function-based Spatial Analysis 2022 VLDB 5.1257999e-05
11,551 LION: Fast and High-Resolution Network Kernel Density Visualization 2024 VLDB 4.9793485e-05
11,652 LARGE: A Length-Aggregation-based Grid Structure for Line Density Visualization 2024 VLDB 4.9793485e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 5 of 5 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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