Detecting Spatial Outliers with Multiple Attributes

Chang Tien Lu*, Dechang Chen, Yufeng Kou

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

61 Scopus citations


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 languageEnglish
Pages (from-to)122-128
Number of pages7
JournalProceedings of the International Conference on Tools with Artificial Intelligence
StatePublished - 2003
Externally publishedYes
EventProceedings: 15th IEEE International Conference on Tools with artificial Intelligence - Sacramento, CA, United States
Duration: 3 Nov 20035 Nov 2003


Dive into the research topics of 'Detecting Spatial Outliers with Multiple Attributes'. Together they form a unique fingerprint.

Cite this