File Download

There are no files associated with this item.

  • Find it @ UNIST can give you direct access to the published full text of this article. (UNISTARs only)
Related Researcher

김성일

Kim, Sungil
Data Analytics Lab.
Read More

Views & Downloads

Detailed Information

Cited time in webofscience Cited time in scopus
Metadata Downloads

Spatial cluster detection in mobility networks: a copula approach

Author(s)
Kim, HeeyoungDuan, RongKim, SungilLee, JaehwanMa, Guang-Qin
Issued Date
2019-01
DOI
10.1111/rssc.12307
URI
https://scholarworks.unist.ac.kr/handle/201301/24734
Fulltext
https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/rssc.12307
Citation
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, v.68, no.1, pp.99 - 120
Abstract
In mobility network capacity planning, characterizing the mobility network traffic is one of the most challenging tasks. Besides the growth trend and multiple periodic temporal patterns for normal traffic, the problem is complicated by the occasionally intense traffic for special events and its dynamic spatial relationships. Identifying the areas that have different traffic patterns compared with their neighbouring areas is a problem of spatial hotspot detection. In the paper, a copula‐based method is proposed: using a multivariate extreme value copula, the upper tail dependence of the traffic distributions of neighbouring cell towers is evaluated, and then a cluster of multiple time series (i.e. multiple cell towers) with high upper tail dependence is detected. The method proposed is validated by using synthetic data as well as real mobility traffic data.
Publisher
WILEY-BLACKWELL
ISSN
0035-9254
Keyword (Author)
Hotspot detectionMobility networkMultivariate extreme value copulaSpatial event detection
Keyword
ARCHIMEDEAN COPULAS

qrcode

Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.