Optimizing the maximum reported cluster size in the spatial scan statistic for ordinal data.

ABSTRACT: The spatial scan statistic is an important tool for spatial cluster detection. There have been numerous studies on scanning window shapes. However, little research has been done on the maximum scanning window size or maximum reported cluster size. Recently, Han et al. proposed to use the Gini coefficient to optimize the maximum reported cluster size. However, the method has been developed and evaluated only for the Poisson model. We adopt the Gini coefficient to be applicable to the spatial scan statistic for ordinal data to determine the optimal maximum reported cluster size. Through a simulation study and application to a real data example, we evaluate the performance of the proposed approach. With some sophisticated modification, the Gini coefficient can be effectively employed for the

SUBMITTER: Kim S 

PROVIDER: S-EPMC5533428 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature