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Anomalous Region Detection on the Mobility Data
University of Shanghai Science and technol, Peoples R China.
Northwest University, Peoples R China.
University of Shanghai Science and technol, Peoples R China.
University of Shanghai Science and technol, Peoples R China.
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2015 (English)In: 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, Institute of Electrical and Electronics Engineers (IEEE), 2015, 1669-1674 p.Conference paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2015. 1669-1674 p.
Keyword [en]
anomalous detection, grid, density, kernel
National Category
Economic Geography
Identifiers
URN: urn:nbn:se:liu:diva-131212DOI: 10.1109/CIT/IUCC/DASC/PICOM.2015.252ISI: 000380514500256ISBN: 978-1-5090-0154-5OAI: oai:DiVA.org:liu-131212DiVA: diva2:971849
Conference
IEEE International Conference on Computer and Information Technology, Liverpool, United Kingdom, October 26-28, 2015
Available from: 2016-09-19 Created: 2016-09-12 Last updated: 2016-09-26Bibliographically approved

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Wu, Zonghan
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Department of Computer and Information ScienceFaculty of Science & Engineering
Economic Geography

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