Development of geospatial analysis method to detect outlying data points

Hyun Ki Kim; Han Saem Kim; Si Yeol Shin; Choong Ki Chung

doi:10.1061/9780784412121.297

Development of geospatial analysis method to detect outlying data points

Hyun Ki Kim, Han Saem Kim, Si Yeol Shin, Choong Ki Chung

Department of Civil and Environmental Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Subsurface investigation results always reflect a level of soil uncertainty, which sometimes requires statistical corrections of the data for the appropriate engineering decision. This study suggests a closed-form framework to detect the outlying data points from the whole testing results by the statistical geo-spatial information analyses with extreme value distribution-based outlier analysis and kriging-based cross-validation. The spatial distribution of soil layer thickness in the area of Yeouido, Seoul, South Korea is analyzed to identify the outlying data points out of the given boring data

Original language	English
Title of host publication	GeoCongress 2012
Subtitle of host publication	State of the Art and Practice in Geotechnical Engineering
Pages	2904-2911
Number of pages	8
Edition	225 GSP
DOIs	https://doi.org/10.1061/9780784412121.297
State	Published - 2012
Event	GeoCongress 2012: State of the Art and Practice in Geotechnical Engineering - Oakland, CA, United States Duration: 25 Mar 2012 → 29 Mar 2012

Publication series

Name	Geotechnical Special Publication
Number	225 GSP
ISSN (Print)	0895-0563

Conference

Conference	GeoCongress 2012: State of the Art and Practice in Geotechnical Engineering
Country/Territory	United States
City	Oakland, CA
Period	25/03/12 → 29/03/12

Access to Document

10.1061/9780784412121.297

Cite this

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title = "Development of geospatial analysis method to detect outlying data points",

abstract = "Subsurface investigation results always reflect a level of soil uncertainty, which sometimes requires statistical corrections of the data for the appropriate engineering decision. This study suggests a closed-form framework to detect the outlying data points from the whole testing results by the statistical geo-spatial information analyses with extreme value distribution-based outlier analysis and kriging-based cross-validation. The spatial distribution of soil layer thickness in the area of Yeouido, Seoul, South Korea is analyzed to identify the outlying data points out of the given boring data",

author = "Kim, {Hyun Ki} and Kim, {Han Saem} and Shin, {Si Yeol} and Chung, {Choong Ki}",

year = "2012",

doi = "10.1061/9780784412121.297",

language = "English",

isbn = "9780784412121",

series = "Geotechnical Special Publication",

number = "225 GSP",

pages = "2904--2911",

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note = "GeoCongress 2012: State of the Art and Practice in Geotechnical Engineering ; Conference date: 25-03-2012 Through 29-03-2012",

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Kim, HK, Kim, HS, Shin, SY & Chung, CK 2012, Development of geospatial analysis method to detect outlying data points. in GeoCongress 2012: State of the Art and Practice in Geotechnical Engineering. 225 GSP edn, Geotechnical Special Publication, no. 225 GSP, pp. 2904-2911, GeoCongress 2012: State of the Art and Practice in Geotechnical Engineering, Oakland, CA, United States, 25/03/12. https://doi.org/10.1061/9780784412121.297

Development of geospatial analysis method to detect outlying data points. / Kim, Hyun Ki; Kim, Han Saem; Shin, Si Yeol et al.
GeoCongress 2012: State of the Art and Practice in Geotechnical Engineering. 225 GSP. ed. 2012. p. 2904-2911 (Geotechnical Special Publication; No. 225 GSP).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - Subsurface investigation results always reflect a level of soil uncertainty, which sometimes requires statistical corrections of the data for the appropriate engineering decision. This study suggests a closed-form framework to detect the outlying data points from the whole testing results by the statistical geo-spatial information analyses with extreme value distribution-based outlier analysis and kriging-based cross-validation. The spatial distribution of soil layer thickness in the area of Yeouido, Seoul, South Korea is analyzed to identify the outlying data points out of the given boring data

AB - Subsurface investigation results always reflect a level of soil uncertainty, which sometimes requires statistical corrections of the data for the appropriate engineering decision. This study suggests a closed-form framework to detect the outlying data points from the whole testing results by the statistical geo-spatial information analyses with extreme value distribution-based outlier analysis and kriging-based cross-validation. The spatial distribution of soil layer thickness in the area of Yeouido, Seoul, South Korea is analyzed to identify the outlying data points out of the given boring data

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Development of geospatial analysis method to detect outlying data points

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