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