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A Power-Enhanced Algorithm for Spatial Anomaly Detection in Binary Labelled Point Data Using the Spatial Scan Statistic [postprint]

By S. Read, P.A. Bath, P. Willett and R. Maheswaran


This paper presents a novel modification to an existing algorithm for spatial anomaly detection in binary labeled point data sets, using the Bernoulli version of the Spatial Scan Statistic. We identify a potential ambiguity in p-values produced by Monte Carlo testing, which (by the selection of the most conservative p-value) can lead to sub-optimal power. When such ambiguity occurs, the modification uses a very inexpensive secondary test to suggest a less conservative p-value. Using benchmark tests, we show that this appears to restore power to the expected level, whilst having similarly retest variance to the original. The modification also appears to produce a small but significant improvement in overall detection performance when multiple anomalies are present

Publisher: Springer Verlag
Year: 2010
OAI identifier:

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