Department of Statistics, National Chengchi University, Wenshan District, Taipei City 11605, Taipei, Taiwan, ROC.
Int J Health Geogr. 2013 May 6;12:25. doi: 10.1186/1476-072X-12-25.
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.
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.
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.
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.
空间聚类检测有许多应用，近年来提出了更多的检测方法。大多数聚类检测方法在检测圆形（或类似圆形）聚类方面非常有效，但能够检测不规则形状聚类的方法通常需要大量的计算时间。
我们提出了一种新的晶格数据空间检测算法。该方法可以分为两个阶段：第一阶段通过应用 Choynowski 检验确定具有异常发生的显著单元（即个体聚类），第二阶段通过二项式近似方法根据第一阶段的信息确定是否存在聚类。我们首先使用计算机模拟来评估所提出方法的性能，并将其与扫描统计进行比较。此外，我们还以 2000 年台湾癌症数据为例说明了扫描统计和所提出方法的检测结果。
模拟结果支持在群体规模较大且研究区域不规则的情况下使用所提出的方法。然而，一般来说，扫描统计在检测聚类方面仍然具有更好的功效，特别是在群体规模不大的情况下。对于癌症数据分析，扫描统计倾向于发现更多的聚类，并且聚类的形状接近圆形（或椭圆形）。另一方面，所提出的方法仅发现一个聚类，并且无法检测到小尺寸的聚类。
简而言之，当 Choynowski 方法正确检测到显著单元时，所提出的方法可以很好地检测圆形和非圆形聚类。此外，基于二项式的方法可以处理多重检验问题并节省计算时间。另一方面，圆形和椭圆形扫描统计在检测聚类方面都具有很好的功效，但倾向于检测更多的聚类，并且在检测非圆形聚类时准确性较低。
