Dear scipy community
I would like to propose to add a versatile programming library[1] of Kernel density visualization to scipy and I am very interested in your opinion.
What is KDV?
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.
Why we develop LIBKDV[2]?
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. To overcome this issue, we ask a question: Can we develop the first library that can reduce the worst-case time complexity for supporting different types of KDV-based geospatial analytics? In order to answer this question, we develop the first complexity-optimized programming library, called LIBKDV, by adopting the new camp of KDV algorithms, including SLAM [3] and SWS [4], in our recent studies. Better still, we fully parallelize our KDV algorithms, SLAM and SWS, to further optimize the efficiency of LIBKDV.
What are the advantages of LIBKDV ?
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.
We have some demonstrations[5][6] now.
I am looking forward to your insights and thoughts.
All the best.
Refereces
[1] https://github.com/libkdv/libkdv
[2] Tsz Nam Chan, Pak Lon Ip, Kaiyan Zhao, Leong Hou U, Byron Choi, and Jianliang Xu. 2022. LIBKDV: a versatile kernel density visualization library for geospatial analytics. Proc. VLDB Endow. 15, 12 (August 2022), 3606–3609. https://doi.org/10.14778/3554821.3554855
[3] Tsz Nam Chan, Leong Hou U, Byron Choi, and Jianliang Xu. 2022. SLAM: Efficient Sweep Line Algorithms for Kernel Density Visualization. In Proceedings of the 2022 International Conference on Management of Data (SIGMOD '22). Association for Computing Machinery, New York, NY, USA, 2120–2134. https://doi.org/10.1145/3514221.3517823
[4] Tsz Nam Chan, Pak Lon Ip, Leong Hou U, Byron Choi, and Jianliang Xu. 2022. SWS: a complexity-optimized solution for spatial-temporal kernel density visualization. Proc. VLDB Endow. 15, 4 (December 2021), 814–827. https://doi.org/10.14778/3503585.3503591
[5] https://covid19.comp.hkbu.edu.hk/
[6] https://degroup.cis.um.edu.mo/covid-19/#
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