Name
Abstract | ||
|---|---|---|
Mobility data records the change of location and time about the crowd activities, reflecting a large amount of semantic knowledge about human mobility and hot regions. From the perspective of regional semantic knowledge, mining anomalous regions of overcrowded area is essential for disaster-aware resilience system scheme. This paper studies how to discover anomalous regions of moving crowds over the mobility data. From the perspective of spatial information analysis about the location sequence of moving crowds, the paper introduces grid structure to index activity space and proposes a density calculation method of grid cells based on kernel function. By adopting Top-k sorting method, the algorithm determines the density thresholds to detect the anomalous regions. Finally, experimental results validate the feasibility and effectiveness of the above method on practical data sets. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/CIT/IUCC/DASC/PICOM.2015.252 | CIT/IUCC/DASC/PICOM 2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION TECHNOLOGY - UBIQUITOUS COMPUTING AND COMMUNICATIONS - DEPENDABLE, AUTONOMIC AND SECURE COMPUTING - PERVASIVE INTELLIGENCE AND COMPUTING |
Keywords | Field | DocType |
anomalous detection, grid, density, kernel | Spatial analysis,Data mining,Crowds,Data set,Algorithm design,Computer science,Sorting,Cluster analysis,Grid,Kernel (statistics) | Conference |
Citations | PageRank | References |
0 | 0.34 | 12 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huan Huo | 1 | 35 | 10.00 |
| Shangye Chen | 2 | 1 | 0.70 |
| Liang Song | 3 | 0 | 0.34 |
| Leiyu Ban | 4 | 0 | 0.34 |
| Zonghan Wu | 5 | 240 | 9.78 |
| Liang Liu | 6 | 6 | 0.86 |
| Liping Gao | 7 | 9 | 1.10 |