LIBKDV: A Versatile Kernel Density Visualization Library for Geospatial Analytics
48th International Conference on Very Large Data Bases, VLDB 2022
; 15(12):3606-3609, 2022.
Article
in English
| Scopus | ID: covidwho-2056499
ABSTRACT
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. © 2022, VLDB Endowment. All rights reserved.
Accidents; Computational complexity; Computational efficiency; Computer software; Crime; Large dataset; Disease outbreaks; Geo-spatial; Geo-spatial analysis; Hotspot detections; Kernel density; Outbreak detection; Programming library; Scientific softwares; Software-tools; Visualization software; Visualization
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
48th International Conference on Very Large Data Bases, VLDB 2022
Year:
2022
Document Type:
Article
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