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.

本数据集旨在探究空间尺度变化对一组空间结构模型结果的影响，并评估多种贝叶斯空间平滑先验在空间依赖性方面的表现，包括内禀高斯马尔可夫随机场（IGMRF）、格网上的二阶随机游走（RW2D）以及具有Matérn相关函数的高斯场。当前数据集基于Humberside案例研究以完善此项调查。Humberside案例研究描绘自然现象，以探究空间尺度和空间平滑对建模结果的影响，并补充模拟研究。数据包含1974年至1986年间在英国North Humberside地区诊断出的62例儿童白血病和淋巴瘤病例，以及从同期出生登记中随机选取的141名对照者。数据集中提供了每位个体家庭住址的空间位置（实际上，为邮编的重心）。数据集具有多边形观测窗口；为分析目的，我们创建了一个72.1公里×60.8公里的矩形窗口，以包含所有事件。数据集展示了Humberside案例研究中所有非零单元格计数在不同空间尺度下网格单元格中事件数量的概要。