Summary of the number of events in the grid cells for all the non-zero cell counts at various spatial scales for the Humberside dataset

The dataset was gathered to investigate the impact of changes in spatial scale on model outcome for a set of spatial structures and to evaluate the performance of various Bayesian spatial smoothness priors for
spatial dependence, namely an intrinsic Gaussian Markov random field
(IGMRF), a second-order random walk (RW2D) on a lattice, and a Gaussian
field with Matérn correlation function.The current dataset draws upon the Humberside case study to complete the investigation. The Humberside case study portrays natural phenomena to
investigate the impact of spatial scales and spatial smoothing on
modelling outcomes to complement a simulation study. The data
contained 62 cases of childhood leukaemia and lymphoma diagnosed in the
North Humberside region of England between 1974 and 1986, and 141
controls selected at random from the birth register for the same period.
Spatial location of each individual's home address (actually, the
centroid for the postal code) was given in the dataset. The dataset had a polygonal observation window; for the analysis, we
created a 72.1 km×60.8 km rectangular window to enclose all events.The dataset presents a summary of the
number of events in the grid cells for all the non-zero cell counts at
various spatial scales for the Humberside case study.

本数据集的采集旨在探究空间尺度变化对一系列空间结构的模型输出的影响，并评估多种用于空间相关性建模的贝叶斯空间平滑先验的性能，即内在高斯马尔可夫随机场（intrinsic Gaussian Markov random field, IGMRF）、格点上的二阶随机游走（second-order random walk, RW2D）以及带有马特恩相关函数（Matérn correlation function）的高斯场。

本数据集基于亨伯赛德案例研究完成相关探究。亨伯赛德案例研究通过刻画自然现象，以探究空间尺度与空间平滑对建模结果的影响，以此补充模拟研究的内容。

数据包含1974年至1986年间英格兰北亨伯赛德地区确诊的62例儿童白血病与淋巴瘤病例，以及同期从出生登记册中随机选取的141例对照个体。数据集中收录了每位个体家庭住址的空间位置（实际为对应邮政编码区域的质心坐标）。

该数据集原本采用多边形观测窗口；为开展分析工作，我们构建了一个72.1 km×60.8 km的矩形窗口以覆盖所有研究事件。

本数据集汇总了亨伯赛德案例研究中，不同空间尺度下所有非零网格单元内的事件数量。