Detecting Spatial Outliers with Multiple Attributes

Chang Tien Lu; Dechang Chen; Yufeng Kou

doi:10.1109/TAI.2003.1250179

Detecting Spatial Outliers with Multiple Attributes

Chang Tien Lu^*
, Dechang Chen
, Yufeng Kou

^*Corresponding author for this work

Epidemiology and Biostatistics

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

64 Scopus citations

Abstract

A spatial outlier is a spatially referenced object whose non-spatial attribute values are significantly different from the values of its neighborhood. Identification of spatial outliers can lead to the discovery of unexpected, interesting, and useful spatial patterns for further analysis. Previous work in spatial outlier detection focuses on detecting spatial outliers with a single attribute. In the paper, we propose two approaches to discover spatial outliers with multiple attributes. We formulate the multi-attribute spatial outlier detection problem in a general way, provide two effective detection algorithms, and analyze their computation complexity. In addition, using a real-world census data, we demonstrate that our approaches can effectively identify local abnormality in large spatial data sets.

Original language	English
Title of host publication	Proceedings - 15th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2003
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	122-128
Number of pages	7
ISBN (Print)	0769520383
DOIs	https://doi.org/10.1109/TAI.2003.1250179
State	Published - 2003
Event	15th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2003 - Sacramento, CA, United States Duration: 3 Nov 2003 → 5 Nov 2003

Publication series

Name	Proceedings of the International Conference on Tools with Artificial Intelligence
ISSN (Print)	1063-6730

Conference

Conference	15th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2003
Country/Territory	United States
City	Sacramento, CA
Period	3/11/03 → 5/11/03

Access to Document

10.1109/TAI.2003.1250179

Cite this

Lu, C. T., Chen, D., & Kou, Y. (2003). Detecting Spatial Outliers with Multiple Attributes. In Proceedings - 15th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2003 (pp. 122-128). (Proceedings of the International Conference on Tools with Artificial Intelligence). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TAI.2003.1250179

@inproceedings{56b81bb2c0504c99a6635b7aca0bea57,

title = "Detecting Spatial Outliers with Multiple Attributes",

abstract = "A spatial outlier is a spatially referenced object whose non-spatial attribute values are significantly different from the values of its neighborhood. Identification of spatial outliers can lead to the discovery of unexpected, interesting, and useful spatial patterns for further analysis. Previous work in spatial outlier detection focuses on detecting spatial outliers with a single attribute. In the paper, we propose two approaches to discover spatial outliers with multiple attributes. We formulate the multi-attribute spatial outlier detection problem in a general way, provide two effective detection algorithms, and analyze their computation complexity. In addition, using a real-world census data, we demonstrate that our approaches can effectively identify local abnormality in large spatial data sets.",

author = "Lu, \{Chang Tien\} and Dechang Chen and Yufeng Kou",

year = "2003",

doi = "10.1109/TAI.2003.1250179",

language = "English",

isbn = "0769520383",

series = "Proceedings of the International Conference on Tools with Artificial Intelligence",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "122--128",

booktitle = "Proceedings - 15th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2003",

note = "15th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2003 ; Conference date: 03-11-2003 Through 05-11-2003",

}

Lu, CT, Chen, D & Kou, Y 2003, Detecting Spatial Outliers with Multiple Attributes. in Proceedings - 15th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2003. Proceedings of the International Conference on Tools with Artificial Intelligence, Institute of Electrical and Electronics Engineers Inc., pp. 122-128, 15th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2003, Sacramento, CA, United States, 3/11/03. https://doi.org/10.1109/TAI.2003.1250179

Detecting Spatial Outliers with Multiple Attributes. / Lu, Chang Tien; Chen, Dechang; Kou, Yufeng.
Proceedings - 15th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2003. Institute of Electrical and Electronics Engineers Inc., 2003. p. 122-128 (Proceedings of the International Conference on Tools with Artificial Intelligence).

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

TY - GEN

T1 - Detecting Spatial Outliers with Multiple Attributes

AU - Lu, Chang Tien

AU - Chen, Dechang

AU - Kou, Yufeng

PY - 2003

Y1 - 2003

N2 - A spatial outlier is a spatially referenced object whose non-spatial attribute values are significantly different from the values of its neighborhood. Identification of spatial outliers can lead to the discovery of unexpected, interesting, and useful spatial patterns for further analysis. Previous work in spatial outlier detection focuses on detecting spatial outliers with a single attribute. In the paper, we propose two approaches to discover spatial outliers with multiple attributes. We formulate the multi-attribute spatial outlier detection problem in a general way, provide two effective detection algorithms, and analyze their computation complexity. In addition, using a real-world census data, we demonstrate that our approaches can effectively identify local abnormality in large spatial data sets.

AB - A spatial outlier is a spatially referenced object whose non-spatial attribute values are significantly different from the values of its neighborhood. Identification of spatial outliers can lead to the discovery of unexpected, interesting, and useful spatial patterns for further analysis. Previous work in spatial outlier detection focuses on detecting spatial outliers with a single attribute. In the paper, we propose two approaches to discover spatial outliers with multiple attributes. We formulate the multi-attribute spatial outlier detection problem in a general way, provide two effective detection algorithms, and analyze their computation complexity. In addition, using a real-world census data, we demonstrate that our approaches can effectively identify local abnormality in large spatial data sets.

UR - https://www.scopus.com/pages/publications/0344235446

U2 - 10.1109/TAI.2003.1250179

DO - 10.1109/TAI.2003.1250179

M3 - Conference contribution

AN - SCOPUS:0344235446

SN - 0769520383

T3 - Proceedings of the International Conference on Tools with Artificial Intelligence

SP - 122

EP - 128

BT - Proceedings - 15th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2003

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 15th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2003

Y2 - 3 November 2003 through 5 November 2003

ER -

Detecting Spatial Outliers with Multiple Attributes

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