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

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  • Sehwi Kim
  • Inkyung Jung

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.

Suggested Citation

  • Sehwi Kim & Inkyung Jung, 2017. "Optimizing the maximum reported cluster size in the spatial scan statistic for ordinal data," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-15, July.
    • Handle: RePEc:plo:pone00:0182234
    DOI: 10.1371/journal.pone.0182234
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    References listed on IDEAS

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    1. Huang, Lan & Tiwari, Ram C. & Zou, Zhaohui & Kulldorff, Martin & Feuer, Eric J., 2009. "Weighted Normal Spatial Scan Statistic for Heterogeneous Population Data," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 886-898.
    2. Tony H. Grubesic & Ran Wei & Alan T. Murray, 2014. "Spatial Clustering Overview and Comparison: Accuracy, Sensitivity, and Computational Expense," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 104(6), pages 1134-1156, November.
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    1. Malebogo Solomon & Luis Furuya-Kanamori & Kinley Wangdi, 2021. "Spatial Analysis of HIV Infection and Associated Risk Factors in Botswana," IJERPH, MDPI, vol. 18(7), pages 1-13, March.
    2. Ali Abolhassani & Marcos O. Prates & Safieh Mahmoodi, 2023. "Irregular Shaped Small Nodule Detection Using a Robust Scan Statistic," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(1), pages 141-162, April.
    3. Kinley Wangdi & Kinley Penjor & Tobgyal & Saranath Lawpoolsri & Ric N. Price & Peter W. Gething & Darren J. Gray & Elivelton Da Silva Fonseca & Archie C. A. Clements, 2021. "Space–Time Clustering Characteristics of Malaria in Bhutan at the End Stages of Elimination," IJERPH, MDPI, vol. 18(11), pages 1-12, May.
    4. Silva, Ivair R. & Duczmal, Luiz & Kulldorff, Martin, 2021. "Confidence intervals for spatial scan statistic," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    5. Lee, Myeonggyun & Jung, Inkyung, 2019. "Modified spatial scan statistics using a restricted likelihood ratio for ordinal outcome data," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 28-39.

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