This paper studies unusual phenomena by discovering anomalous windows in multivariate spatial data. Such an anomalous window is a group of contiguous spatial objects indicating the occurrence of unusual phenomenon in terms of multiple variables. The paper presents a novel Robust non-parametric Multivariate Scan Statistic (RMSS). In contrast to the existing work, the authors’ approach is designe...

SUMMARY Cook, Gold, and Li (2007, Biometrics 63, 540-549) extended the Kulldorff (1997, Communications in Statistics 26, 1481-1496) scan statistic for spatial cluster detection to survival-type observations. Their approach was based on the score statistic and they proposed a permutation distribution for the maximum of score tests. The score statistic makes it possible to apply the scan statisti...

Spatial distributions find applications in various fields, such as public health, biology, ecology, geography, economics or sociology. The DAC statistic is the difference between the empirical cumulative distribution of cases and that of non-cases at a particular point. Simulations indicated that the location of the maximum DAC statistic is not unique. Previous studies attempting to use the DAC...

In geo-statistics, the Durbin-Watson test is frequently employed to detect the presence of residual serial correlation from least squares regression analyses. However, the Durbin-Watson statistic is only suitable for ordered time or spatial series. If the variables comprise cross-sectional data coming from spatial random sampling, the test will be ineffectual because the value of Durbin-Watson'...

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Introduction Kulldorff’s spatial scan statistic1 detects significant spatial clusters of disease by maximizing a likelihood ratio statistic over circular spatial regions. The fast localized subset scan2 enables scalable detection of proximity-constrained subsets and increases power to detect irregularly-shaped clusters, However, unconstrained subset scanning within each circular neighborhood2, ...

The discovery of anomalous regions within spatially oriented data can provide valuable insights to the study of disease prevention, crime, and socioeconomic inequality. Spatial scan statistic methods are a common tool used within these fields to discover areas of a data set that have significant variation when compared to the background distribution. However, the effects of data representation ...