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.