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Outlier Detection and Comparison of Origin-Destination Flows Using Data Depth

Authors Myeong-Hun Jeong , Junjun Yin , Shaowen Wang

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Author Details

Myeong-Hun Jeong
  • Department of Civil Engineering, Chosun University, Gwangju, Republic of Korea
Junjun Yin
  • Social Science Research Institute
  • Institute for CyberScience, Penn State University, PA, USA
Shaowen Wang
  • CyberGIS Center for Advanced Digital and Spatial Studies
  • Department of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, IL, USA

Funding

  • Jeong, Myeong-Hun: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2018R1C1B5043892).
  • Yin, Junjun: This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by the National Science Foundation grant number ACI-1548562
  • Wang, Shaowen: This work was supported by the U.S. National Science Foundation (grant numbers: 1047916 and 1443080)

Cite AsGet BibTex

Myeong-Hun Jeong, Junjun Yin, and Shaowen Wang. Outlier Detection and Comparison of Origin-Destination Flows Using Data Depth. In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 6:1-6:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)
https://doi.org/10.4230/LIPIcs.GISCIENCE.2018.6

Abstract

Advances in location-aware technology have resulted in massive trajectory data. Origin-destination (OD) trajectories provide rich information on urban flow and transport demand. This study describes a new method for detecting OD flows outliers and conducting hypothesis testing between two OD flow datasets in terms of the variations of spatial extent, that is, spread. The proposed method is based on data depth, which measures the centrality and outlyingness of a point with respect to a given dataset in R^d. Based on the center-outward ordering property, the proposed method analyzes the underlying characteristics of OD flows, such as location, outlyingness, and spread. The ability of the method to detect OD anomalies is compared with that of the Mahalanobis distance approach, and an F-test is used to verify the difference in scale. Empirical evaluation has demonstrated that our method effectively identifies OD flows outliers in an interactive way. Furthermore, the method can provide new perspectives such as spatial extent by considering the overall structure of data when comparing two different OD flows in terms of scale.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Anomaly detection
Keywords
  • Movement Analysis
  • Trajectory Data Mining
  • Data Depth
  • Outlier Detection

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