TY - GEN
T1 - Outlier detection and comparison of origin-destination flows using data depth
AU - Jeong, Myeong Hun
AU - Yin, Junjun
AU - Wang, Shaowen
N1 - Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2018R1C1B5043892). 2 This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by the National Science Foundation grant number ACI-1548562 3 This work was supported by the U.S. National Science Foundation (grant numbers: 1047916 and 1443080)
Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2018R1C1B5043892). 2 This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by the National Science Foundation grant number ACI-1548562 3 This work was supported by the U.S. National Science Foundation (grant numbers: 1047916 and
PY - 2018/8/1
Y1 - 2018/8/1
N2 - 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 Rd. 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.
AB - 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 Rd. 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.
UR - http://www.scopus.com/inward/record.url?scp=85051360626&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85051360626&partnerID=8YFLogxK
U2 - 10.4230/LIPIcs.GIScience.2018.6
DO - 10.4230/LIPIcs.GIScience.2018.6
M3 - Conference contribution
AN - SCOPUS:85051360626
SN - 9783959770835
T3 - Leibniz International Proceedings in Informatics, LIPIcs
BT - 10th International Conference on Geographic Information Science, GIScience 2018
A2 - Griffin, Amy L.
A2 - Winter, Stephan
A2 - Sester, Monika
PB - Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
T2 - 10th International Conference on Geographic Information Science, GIScience 2018
Y2 - 28 August 2018 through 31 August 2018
ER -