License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
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DOI: 10.4230/LIPIcs.GISCIENCE.2018.6
URN: urn:nbn:de:0030-drops-93341
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2018/9334/
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Jeong, Myeong-Hun ; Yin, Junjun ; Wang, Shaowen

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

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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.

BibTeX - Entry

@InProceedings{jeong_et_al:LIPIcs:2018:9334,
  author =	{Myeong-Hun Jeong and Junjun Yin and Shaowen Wang},
  title =	{{Outlier Detection and Comparison of Origin-Destination Flows Using Data Depth}},
  booktitle =	{10th International Conference on Geographic Information  Science (GIScience 2018)},
  pages =	{6:1--6:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Stephan Winter and Amy Griffin and Monika Sester},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2018/9334},
  URN =		{urn:nbn:de:0030-drops-93341},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.6},
  annote =	{Keywords: Movement Analysis, Trajectory Data Mining, Data Depth, Outlier Detection}
}

Keywords: Movement Analysis, Trajectory Data Mining, Data Depth, Outlier Detection
Collection: 10th International Conference on Geographic Information Science (GIScience 2018)
Issue Date: 2018
Date of publication: 02.08.2018

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