ArticlePloS one2017
Evaluation of the Gini Coefficient in Spatial Scan Statistics for Detecting Irregularly Shaped Clusters.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Comparing circular and flexibly-shaped scan statistics for disease clustering detection.Frontiers in public health · 2024Article
- Evaluating the ability of a locally focused culling program in removing chronic wasting disease infected free-ranging white-tailed deer in Illinois, USA, 2003-2020.Transboundary and emerging diseases · 2022Article
- A spatiotemporal data mining study to identify high-risk neighborhoods for out-of-hospital cardiac arrest (OHCA) incidents.Scientific reports · 2022Article
- Optimizing the maximum reported cluster size in the spatial scan statistic for survival data.International journal of health geographics · 2021Article
- Identification of high-risk and low-risk clusters and estimation of the relative risk of acute lymphoblastic leukemia in provinces of Iran during 2006-2014 period: A geo-epidemiological study.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2021Article
- Identifying high-risk areas for nonfatal opioid overdose: a spatial case-control study using EMS run data.Annals of epidemiology · 2019Article
- Spatial scan statistics for matched case-control data.PloS one · 2019Article
- Inequalities in the geographic distribution of hospital beds and doctors in traditional Chinese medicine from 2004 to 2014.International journal for equity in health · 2018Article
- Spatial clustering of metal and metalloid mixtures in unregulated water sources on the Navajo Nation - Arizona, New Mexico, and Utah, USA.The Science of the total environment · 2018Article
- Optimizing the maximum reported cluster size in the spatial scan statistic for ordinal data.PloS one · 2017Article
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2 authors.
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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.
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