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The standard spatial scan statistic does not model any variability in the underlying risks of subregions belonging to a detected cluster. For a multilevel risk cluster, the isotonic spatial scan statistic could model a centralized high\u2010risk kernel in the cluster. Because variations in disease risks are anisotropic owing to different social, economical, or transport factors, the real high\u2010risk kernel will not necessarily take the central place in a whole cluster area. We propose a spatial scan statistic for a nonisotropic two\u2010level risk cluster, which could be used to detect a whole cluster and a noncentralized high\u2010risk kernel within the cluster simultaneously. The performance of the three methods was evaluated through an intensive simulation study. Our proposed nonisotropic two\u2010level method showed better power and geographical precision with two\u2010level risk cluster scenarios, especially for a noncentralized high\u2010risk kernel. Our proposed method is illustrated using the hand\u2013foot\u2013mouth disease data in Pingdu City, Shandong, China in May 2009, compared with two other methods. In this practical study, the nonisotropic two\u2010level method is the only way to precisely detect a high\u2010risk area in a detected whole cluster. 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