ArticlePloS one2017
Optimizing the maximum reported cluster size in the spatial scan statistic for ordinal data.
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 12 papers.
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Who cites it
12 citing papers in PubMed.
- Privy by the Bay: Emerging hotspot analysis of 311 reports of human/animal waste near San Francisco Pit Stop locations, 2009-2022.PloS one · 2025Article
- Spatial distribution and associated factors of cesarean section in Ethiopia using mini EDHS 2019 data: a community based cross-sectional study.Scientific reports · 2024Article
- Effectiveness of human immunodeficiency virus prevention strategies by mapping the geographic dispersion pattern of human immunodeficiency virus prevalence in Nanning, China.BMC public health · 2024Article
- Optimizing the maximum reported cluster size for the multinomial-based spatial scan statistic.International journal of health geographics · 2023Article
- Irregular Shaped Small Nodule Detection Using a Robust Scan Statistic.Statistics in biosciences · 2023Article
- Article
- Lymphatic filariasis in 2016 in American Samoa: Identifying clustering and hotspots using non-spatial and three spatial analytical methods.PLoS neglected tropical diseases · 2022Article
- Optimizing the maximum reported cluster size in the spatial scan statistic for survival data.International journal of health geographics · 2021Article
- Space-Time Clustering Characteristics of Malaria in Bhutan at the End Stages of Elimination.International journal of environmental research and public health · 2021Article
- Spatial Analysis of HIV Infection and Associated Risk Factors in Botswana.International journal of environmental research and public health · 2021Article
- Trends in prevalence and spatiotemporal distribution of gastroschisis in Arkansas, 1998-2015.Birth defects research · 2020Article
- Using the maximum clustering heterogeneous set-proportion to select the maximum window size for the spatial scan statistic.Scientific reports · 2020Article
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2 authors.
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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 ordinal model. The Gini coefficient most often picked the optimal maximum reported cluster sizes that were the same as or smaller than the true cluster sizes with very high accuracy. It seems that we can obtain a more refined collection of clusters by using the Gini coefficient. The Gini coefficient developed specifically for the ordinal model can be useful for optimizing the maximum reported cluster size for ordinal data and helpful for properly and informatively discovering cluster patterns.
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