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

Authors
 Jiyu Kim  ;  Inkyung Jung 
Citation
 PLOS ONE, Vol.12(1) : e0170736, 2017 
Journal Title
 PLOS ONE 
Issue Date
2017
MeSH
Cluster Analysis* ; Computer Simulation ; Disease Outbreaks/statistics & numerical data* ; Epidemiologic Studies* ; Humans ; Monte Carlo Method
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.
Files in This Item:
T201700307.pdf Download
DOI
10.1371/journal.pone.0170736
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) > 1. Journal Papers
Yonsei Authors
Jung, Inkyung(정인경) ORCID logo https://orcid.org/0000-0003-3780-3213
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/154320
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