The choice of spatial scales and spatial smoothness priors for various spatial patterns
Related Experiment Video
Updated: Apr 26, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
Su Yun Kang1, James McGree1, Kerrie Mengersen1
1Mathematical Sciences School, Queensland University of Technology, GPO Box 2434, Brisbane, QLD 4001, Australia; CRC for Spatial Information, 204 Lygon Street, Carlton, Victoria 3053, Australia.
Abstract:
Given the drawbacks for using geo-political areas in mapping outcomes unrelated to geo-politics, a compromise is to aggregate and analyse data at the grid level. This has the advantage of allowing spatial smoothing and modelling at a biologically or physically relevant scale. This article addresses two consequent issues: the choice of the spatial smoothness prior and the scale of the grid. Firstly, we describe several spatial smoothness priors applicable for grid data and discuss the contexts in which these priors can be employed based on different aims. Two such aims are considered, i.e., to identify regions with clustering and to model spatial dependence in the data. Secondly, the choice of the grid size is shown to depend largely on the spatial patterns. We present a guide on the selection of spatial scales and smoothness priors for various point patterns based on the two aims for spatial smoothing.
Related Concept Videos
Depth Perception and Spatial Vision
Selected Data About Geographic Locations
Curvilinear Motion: Rectangular Components
As the car advances, its position evolves over time. Quantifying the car's velocity involves computing the...
Distribution and Dispersion
Levels of Use of a GIS
Residuals and Least-Squares Property
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...

