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題名 A Binary-based Approach for Detecting Irregularly Shaped Clusters
作者 余清祥
Wang, Tai-Chi ;Yue,Ching-Syang Jack
貢獻者 統計系
日期 2013.04
上傳時間 3-Dec-2013 18:16:23 (UTC+8)
摘要 Background:There are many applications for spatial cluster detection and more detection methods have been proposed in recent years. Most cluster detection methods are efficient in detecting circular (or circular-like) clusters, but the methods which can detect irregular-shaped clusters usually require a lot of computing time.Methods:We propose a new spatial detection algorithm for lattice data. The proposed method can be separated into two stages: the first stage determines the significant cells with unusual occurrences (i.e., individual clustering) by applying the Choynowski’s test, and the second stage determines if there are clusters based on the information of the first stage by a binomial approximate method. We first use computer simulation to evaluate the performance of the proposed method and compare it with the scan statistics. Furthermore, we take the Taiwan Cancer data in 2000 to illustrate the detection results of the scan statistics and the proposed method.Results:The simulation results support using the proposed method when the population sizes are large and the study regions are irregular. However, in general, the scan statistics still have better power in detecting clusters, especially when the population sizes are not large. For the analysis of cancer data, the scan statistics tend to spot more clusters, and the clusters’ shapes are close to circular (or elliptic). On the other hand, the proposed methods only find one cluster and cannot detect small-sized clusters.Conclusions:In brief, the proposed methods can detect both circular and non-circular clusters well when the significant cells are correctly detected by the Choynowski’s method. In addition, the binomial-based method can handle the problem of multiple testing and save the computing time. On the other hand, both the circular and elliptical scan statistics have good power in detecting clusters, but tend to detect more clusters and have lower accuracy in detecting non-circular clusters.
關聯 International Journal of Health Geographics, 12,25
資料類型 article
DOI http://dx.doi.org/10.1186/1476-072X-12-25
dc.contributor 統計系en_US
dc.creator (作者) 余清祥zh_TW
dc.creator (作者) Wang, Tai-Chi ;Yue,Ching-Syang Jacken_US
dc.date (日期) 2013.04en_US
dc.date.accessioned 3-Dec-2013 18:16:23 (UTC+8)-
dc.date.available 3-Dec-2013 18:16:23 (UTC+8)-
dc.date.issued (上傳時間) 3-Dec-2013 18:16:23 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/62094-
dc.description.abstract (摘要) Background:There are many applications for spatial cluster detection and more detection methods have been proposed in recent years. Most cluster detection methods are efficient in detecting circular (or circular-like) clusters, but the methods which can detect irregular-shaped clusters usually require a lot of computing time.Methods:We propose a new spatial detection algorithm for lattice data. The proposed method can be separated into two stages: the first stage determines the significant cells with unusual occurrences (i.e., individual clustering) by applying the Choynowski’s test, and the second stage determines if there are clusters based on the information of the first stage by a binomial approximate method. We first use computer simulation to evaluate the performance of the proposed method and compare it with the scan statistics. Furthermore, we take the Taiwan Cancer data in 2000 to illustrate the detection results of the scan statistics and the proposed method.Results:The simulation results support using the proposed method when the population sizes are large and the study regions are irregular. However, in general, the scan statistics still have better power in detecting clusters, especially when the population sizes are not large. For the analysis of cancer data, the scan statistics tend to spot more clusters, and the clusters’ shapes are close to circular (or elliptic). On the other hand, the proposed methods only find one cluster and cannot detect small-sized clusters.Conclusions:In brief, the proposed methods can detect both circular and non-circular clusters well when the significant cells are correctly detected by the Choynowski’s method. In addition, the binomial-based method can handle the problem of multiple testing and save the computing time. On the other hand, both the circular and elliptical scan statistics have good power in detecting clusters, but tend to detect more clusters and have lower accuracy in detecting non-circular clusters.-
dc.format.extent 8718944 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.relation (關聯) International Journal of Health Geographics, 12,25en_US
dc.title (題名) A Binary-based Approach for Detecting Irregularly Shaped Clustersen_US
dc.type (資料類型) articleen
dc.identifier.doi (DOI) 10.1186/1476-072X-12-25en_US
dc.doi.uri (DOI) http://dx.doi.org/10.1186/1476-072X-12-25en_US