Evaluation of the Gini Coefficient in Spatial Scan Statistics for Detecting Irregularly Shaped Clusters.

Jiyu Kim,Inkyung Jung,Jaymie Meliker

doi:10.1371/journal.pone.0170736

Abstract

Spatial scan statistics with circular or elliptic scanning windows are commonly used for cluster detection in various applications, such as the identification of geographical disease clusters from epidemiological data. It has been pointed out that the method may have difficulty in correctly identifying non-compact, arbitrarily shaped clusters. In this paper, we evaluated the Gini coefficient for detecting irregularly shaped clusters through a simulation study. The Gini coefficient, the use of which in spatial scan statistics was recently proposed, is a criterion measure for optimizing the maximum reported cluster size. Our simulation study results showed that using the Gini coefficient works better than the original spatial scan statistic for identifying irregularly shaped clusters, by reporting an optimized and refined collection of clusters rather than a single larger cluster. We have provided a real data example that seems to support the simulation results. We think that using the Gini coefficient in spatial scan statistics can be helpful for the detection of irregularly shaped clusters.

Highlights

Data Availability Statement: All relevant data are within the paper and its Supporting Information file
We evaluate the performance of the Gini coefficient for detecting irregularly shaped clusters, compared with the original circular and elliptic scan statistics
We evaluated the use of the Gini coefficient in the Poisson-based spatial scan statistic for detecting irregularly shaped clusters

Summary

Introduction

Data Availability Statement: All relevant data are within the paper and its Supporting Information file. Ribeiro and Costa [20] examined the effect of different values of MSWS via a simulation study and found that the performance of spatial scan statistics can be sensitive to the choice of MSWS Their findings do not imply that one may run the analysis multiple times with different values of MSWS to optimize the cluster detection results, as discussed by Han et al [21]. Han et al [21] mentioned that setting the MRCS at 50% often results in unnecessarily large and less informative clusters, and the authors concluded that the Gini coefficient can identify a more refined collection of non-overlapping clusters This method has been implemented in SaTScanTM version 9.3. We evaluate the performance of the Gini coefficient for detecting irregularly shaped clusters, compared with the original circular and elliptic scan statistics.

Methods

Results

Discussion