Joint Modeling of Multiple Spatial Temporal Outcomes

在公共卫生、环境和生态研究中，在相同空间位置测量的变量可能相互关联，因此，在所考虑的整个区域，这些变量的空间结构非常相似，这表明它们可能具有共同的空间风险面。利用风险的这种共性可能有助于获得局部区域风险估计的准确性，特别是对于罕见疾病。在科学研究的许多领域中，作为进一步流行病学调查的有用筛选工具，在空间上聚类功能曲线的方法也有很高的需求。

In public health, environmental and ecological studies, variables measured at the same spatial locations may be correlated so that the spatial structures of such variables across the region under consideration are very similar, indicating that they may be characterized by a common spatial risk surface. Employing such a commonality in risks may be useful for gaining precision of local area risk estimates, especially for rare diseases. Methods for clustering functional curves over space are also in high demand in many areas of scientific research as a useful screening tool for further epidemiological investigations.