TY - JOUR
T1 - LIBKDV
T2 - 48th International Conference on Very Large Data Bases, VLDB 2022
AU - Chan, Tsz Nam
AU - Ip, Pak Lon
AU - Zhao, Kaiyan
AU - U, Leong Hou
AU - Choi, Byron
AU - Xu, Jianliang
N1 - Funding Information:
This work was supported by the National Key Research and Development Plan of China (No.2019YFB2102100), the Science and Technology Development Fund Macau (SKL-IOTSC-20212023, 0015/2019/AKP), University of Macau (MYRG2019-00119FST), IRCMS/19-20/H01, Hong Kong RGC Projects RIF R2002-20F, 12202221, and C2004-21GF. Pak Lon Ip, Kaiyan Zhao, and Leong Hou U are also affiliated with the State Key Laboratory of Internet of Things for Smart City.
Publisher Copyright:
Copyright is held by the owner/author(s). Publication rights licensed to the VLDB Endowment.
PY - 2022/8
Y1 - 2022/8
N2 - 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. 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. Therefore, we develop the first versatile programming library, called LIBKDV, based on the set of our complexity-optimized algorithms. 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. In this demonstration, participants will be invited to use our programming library to explore interesting hotspot patterns on large-scale traffic accident, crime, and COVID-19 datasets.
AB - 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. 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. Therefore, we develop the first versatile programming library, called LIBKDV, based on the set of our complexity-optimized algorithms. 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. In this demonstration, participants will be invited to use our programming library to explore interesting hotspot patterns on large-scale traffic accident, crime, and COVID-19 datasets.
UR - http://vldb.org/pvldb/volumes/15/paper/LIBKDV%3A%20A%20Versatile%20Kernel%20Density%20Visualization%20Library%20for%20Geospatial%20Analytics
UR - http://www.scopus.com/inward/record.url?scp=85137975372&partnerID=8YFLogxK
U2 - 10.14778/3554821.3554855
DO - 10.14778/3554821.3554855
M3 - Conference article
AN - SCOPUS:85137975372
SN - 2150-8097
VL - 15
SP - 3606
EP - 3609
JO - Proceedings of the VLDB Endowment
JF - Proceedings of the VLDB Endowment
IS - 12
Y2 - 5 September 2022 through 9 September 2022
ER -
