Spatial cluster detection in mobility networks: a copula approach

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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
Issue Date
2019-01
Language
English
Article Type
Article
Citation

JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, v.68, no.1, pp.99 - 120

ISSN
0035-9254
DOI
10.1111/rssc.12307
URI
http://hdl.handle.net/10203/249796
Appears in Collection
IE-Journal Papers(저널논문)
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