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

Summary: KDV-Explorer enables near-real-time kernel density visualization and exploratory zooming, panning, and scaling over million-point spatial datasets. Built on efficient KDV computation, it demonstrates sub-5-second interaction and compares favorably with QGIS and ArcGIS. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
12626
Venue
VLDB
Year
2021
Pagerank
5.2434488e-05
Overall Rank
9,636 | 33.89%
DOI
10.14778/3476311.3476312

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{chan_vldb21,
        title = {{KDV-Explorer: A Near Real-Time Kernel Density Visualization System for Spatial Analysis}},
        author = {Chan, Tsz Nam and Ip, Pak Lon and U, Leong Hou and Tong, Weng Hou and Mittal, Shivansh and Li, Ye and Cheng, Reynold},
        journal = {PVLDB},
        series = {{VLDB} '21},
        volume = {14},
        number = {12},
        pages = {2655--2658},
        doi = {10.14778/3476311.3476312},
        url = {https://doi.org/10.14778/3476311.3476312},
        year = {2021}
}

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

Rank Cited Paper Year Venue Pagerank
11,755 QUAD: Quadratic-Bound-based Kernel Density Visualization 2020 SIGMOD 5.093636e-05
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