Uses a classification scheme based on a quantile approach from the original rasterized survey analysis data with the Getis-Ord Gi* statistic (pronounced G-i-star) Z-scores (standard deviations.) The statistic tells you where features with either high or low values cluster spatially. This tool works by looking at each feature within the context of neighboring features. To be a statistically significant hot spot, a feature will have a high value and be surrounded by other features with high values as well. For statistically significant positive Z-scores, the larger the Z-score is, the more intense the clustering of high values (hot spot). For statistically significant negative Z-scores, the smaller the Z-score is, the more intense the clustering of low values (cold spot). As part of the raster to polygon conversion process, rounding of Z-Scores was required. This, plus adjustments to the classification to smooth and make a more logically intuitive product, neccessarily altered the extents of the priority zones when compared against the original data from the gridded survey. These smoothed and classifed zones should serve as a guide to strategically focus assests and efforts of a mapping program, but should not be taken to define hard and fast boundaries of collection or processing activities.