Exploring scale-dependent correlations between cancer mortality rates using factorial kriging and population-weighted semivariograms
Exploring scale-dependent correlations between cancer mortality rates using factorial kriging and population-weighted semivariograms
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DOI：
10.1111/j.1538-4632.2005.00634.x
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发表时间：
2005-04-01
影响因子：
3.6
通讯作者：
Greiling, D
中科院分区：
文献类型：
--
作者：
Goovaerts, P;Jacquez, GM;Greiling, D
这篇文章提出了一种地统计学方法，在处理癌症死亡率数据时，解释了空间上不同的人口规模。该方法分两步进行：(1)首先使用总体加权半方差函数估计器来描述和建模空间模式，(2)然后使用阶乘克立格法的变体来估计和映射与在SerniVarig图上识别的嵌套结构相对应的空间分量。与传统空间平滑器相比的主要好处是，空间变异性的模式(即，依赖于方向的变异性、相关范围、存在嵌套的变异性尺度)直接纳入分配给周围观测的权重的计算中。此外，除了过滤数据中的噪声外，该过程还允许在半方差函数模型的基础上将结构化分量分解成几个空间分量(即，局部与区域变异性)。一项模拟研究表明，就预测误差而言，空间分量地图更接近潜在风险地图，并提供了比原始死亡率地图或使用加权线性平均平滑的地图更好的区域模式可视化。建议的方法也减弱了对各种癌症发病率之间相关性大小的低估，这些相关性是由附加在数据上的噪声引起的。这种方法在探索癌症发展风险之间的尺度相关性和在不同空间尺度上检测聚集性方面具有巨大的潜力，这将导致更准确地表示癌症风险的地理变异，并最终更好地理解因果关系。
This article presents a geostatistical methodology that accounts for spatially varying population size in the processing of cancer mortality data. The approach proceeds in two steps: (1) spatial patterns are first described and modeled using population-weighted semivariogram estimators, (2) spatial components corresponding to nested structures identified on sernivariograms are then estimated and mapped using a variant of factorial kriging. The main benefit over traditional spatial smoothers is that the pattern of spatial variability (i.e., direction -dependent variability, range of correlation, presence of nested scales of variability) is directly incorporated into the computation of weights assigned to surrounding observations. Moreover, besides filtering the noise in the data, the procedure allows the decomposition of the structured component into several spatial components (i.e., local versus regional variability) on the basis of semivariogram models. A simulation study demonstrates that maps of spatial components are closer to the underlying risk maps in terms of prediction errors and provide a better visualization of regional patterns than the original maps of mortality rates or the maps smoothed using weighted linear averages. The proposed approach also attenuates the underestimation of the magnitude of the correlation between various cancer rates resulting from noise attached to the data. This methodology has great potential to explore scale-dependent correlation between risks of developing cancers and to detect clusters at various spatial scales, which should lead to a more accurate representation of geographic variation in cancer risk, and ultimately to a better understanding of causative relationships.