LION: Fast and High-Resolution Network Kernel Density Visualization
Authors: 
Tsz Nam Chan, Rui Zang, Bojian Zhu, Leong Hou U, Dingming Wu, Jianliang XuAuthors Info & Claims
Tsz Nam Chan, Rui Zang, Bojian Zhu, Leong Hou U, Dingming Wu, Jianliang XuAuthors Info & ClaimsPages 1255 - 1268
Abstract
Network Kernel Density Visualization (NKDV) has often been used in a wide range of applications, e.g., criminology, transportation science, and urban planning. However, NKDV is computationally expensive, which cannot be scalable to large-scale datasets and high resolution sizes. Although a recent work, called aggregate distance augmentation (ADA), has been developed for improving the efficiency to generate NKDV, this method is still slow and does not take the resolution size into account for optimizing the efficiency. In this paper, we develop a new solution, called LION, which can reduce the worst-case time complexity for generating high-resolution NKDV, without increasing the space complexity. Experiment results on four large-scale location datasets verify that LION can achieve 2.86x to 35.36x speedup compared with the state-of-the-art ADA method.
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Published: 03 May 2024
Published in PVLDB Volume 17, Issue 6
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