SLOM: A new measure for local spatial outliers

Sanjay Chawla, Pei Sun

Research output: Contribution to journalArticlepeer-review

96 Scopus citations


We propose a measure, spatial local outlier measure (SLOM), which captures the local behaviour of datum in their spatial neighbourhood. With the help of SLOM, we are able to discern local spatial outliers that are usually missed by global techniques, like "three standard deviations away from the mean". Furthermore, the measure takes into account the local stability around a data point and suppresses the reporting of outliers in highly unstable areas, where data are too heterogeneous and the notion of outliers is not meaningful. We prove several properties of SLOM and report experiments on synthetic and real data sets that show that our approach is novel and scalable to large datasets.

Original languageEnglish
Pages (from-to)412-429
Number of pages18
JournalKnowledge and Information Systems
Issue number4
StatePublished - Apr 2006
Externally publishedYes


  • Complexity
  • Oscillating parameter
  • R-trees index
  • Spatial local outlier
  • Spatial neighbourhood


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