Scan statistics typically involve sliding a window of varying sizes and shapes over the study area or period to count the number of cases within the window. The observed counts are then compared to what would be expected under a null hypothesis of no clustering. If the observed counts exceed the expected counts by a significant margin, a cluster is identified. The significance is usually tested using a Monte Carlo simulation or other statistical methods to ensure robustness.