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Multiple-scale and broad-scale assessments often require rescaling theoriginal data to a consistent grain size for analysis. Rescaling categoricalraster data by spatial aggregation is common in large area ecologicalassessments. However, distortion and loss of information are associated withaggregation. Using a majority rule generally results in dominant classesbecoming more pronounced and rare classes becoming less pronounced. Usingnearest neighbor techniques generally maintains the global proportion of eachcategory in the original map but can lead to disaggregation. In this paper weimplement the spatial scan statistic for spatial aggregation of categoricalraster maps and describe the behavior of the technique at the local level(aggregation unit) and global level (map). We also contrast the spatial scanstatistic technique with the majority rule and nearest neighbor approaches. Ingeneral, the scan statistic technique behaved inverse the majority ruleapproach in that rare classes rather than abundant classes were preserved. Wesuggest the scan statistic techniques should be used for spatial aggregation ofcategorical maps when preserving heterogeneity and information from rareclasses are important goals of the study or assessment.

The spatial scan statistic: A new method for spatial aggregation of categorical raster maps