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Volume 15, No. 12

LIBKDV: A Versatile Kernel Density Visualization Library for Geospatial Analytics

Tsz Nam Chan (Hong Kong Baptist University)* Pak Lon Ip (University of Macau) kaiyan zhao (University of Macau) Leong Hou U (University of Macau) Byron Choi (Hong Kong Baptist University) Jianliang Xu (Hong Kong Baptist University)


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.

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