Ali Mohammad, Park Jin-Kyung, Thiem Vu Dinh, Canh Do Gia, Emch Michael, Clemens John D
International Vaccine Institute, SNU Research Park, San 4–8 Bongcheon-7 dong, Kwanak-gu, Seoul, Korea.
Int J Health Geogr. 2005 Jun 1;4(1):12. doi: 10.1186/1476-072X-4-12.
Spatial filtering using a geographic information system (GIS) is often used to smooth health and ecological data. Smoothing disease data can help us understand local (neighborhood) geographic variation and ecological risk of diseases. Analyses that use small neighborhood sizes yield individualistic patterns and large sizes reveal the global structure of data where local variation is obscured. Therefore, choosing an optimal neighborhood size is important for understanding ecological associations with diseases. This paper uses Hartley's test of homogeneity of variance (Fmax) as a methodological solution for selecting optimal neighborhood sizes. The data from a study area in Vietnam are used to test the suitability of this method. RESULTS: The Hartley's Fmax test was applied to spatial variables for two enteric diseases and two socioeconomic determinants. Various neighbourhood sizes were tested by using a two step process to implement the Fmaxtest. First the variance of each neighborhood was compared to the highest neighborhood variance (upper, Fmax1) and then they were compared with the lowest neighborhood variance (lower, Fmax2). A significant value of Fmax1 indicates that the neighborhood does not reveal the global structure of data, and in contrast, a significant value in Fmax2 implies that the neighborhood data are not individualistic. The neighborhoods that are between the lower and the upper limits are the optimal neighbourhood sizes. CONCLUSION: The results of tests provide different neighbourhood sizes for different variables suggesting that optimal neighbourhood size is data dependent. In ecology, it is well known that observation scales may influence ecological inference. Therefore, selecting optimal neigborhood size is essential for understanding disease ecologies. The optimal neighbourhood selection method that is tested in this paper can be useful in health and ecological studies.
使用地理信息系统（GIS）进行空间滤波常用于平滑健康和生态数据。平滑疾病数据有助于我们了解疾病的局部（邻域）地理变异和生态风险。使用小邻域大小的分析会产生个体化模式，而大邻域大小则揭示了局部变异被掩盖的数据全局结构。因此，选择最佳邻域大小对于理解疾病的生态关联很重要。本文使用哈特利方差齐性检验（Fmax）作为选择最佳邻域大小的方法解决方案。来自越南一个研究区域的数据用于检验该方法的适用性。
将哈特利Fmax检验应用于两种肠道疾病和两种社会经济决定因素的空间变量。通过两步过程应用Fmax检验来测试各种邻域大小。首先，将每个邻域的方差与最高邻域方差（上限，Fmax1）进行比较，然后将它们与最低邻域方差（下限，Fmax2）进行比较。Fmax1的显著值表明该邻域未揭示数据的全局结构，相反，Fmax2中的显著值意味着邻域数据不是个体化的。介于下限和上限之间的邻域是最佳邻域大小。
测试结果为不同变量提供了不同的邻域大小，表明最佳邻域大小取决于数据。在生态学中，众所周知观察尺度可能影响生态推断。因此，选择最佳邻域大小对于理解疾病生态学至关重要。本文测试的最佳邻域选择方法在健康和生态研究中可能有用。
