title: The choice of spatial scales and spatial smoothness priors for various spatial patterns.
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.
authors.affiliation: Mathematical Sciences School, Queensland University of Technology, GPO Box 2434, Brisbane, QLD 4001, Australia; CRC for Spatial Information, 204 Lygon Street, Carlton, Victoria 3053, Australia. Electronic address: s7.kang@qut.edu.au.
authors.forename: Su Yun
authors.initials: SY
authors.lastname: Kang
authors.affiliation: Mathematical Sciences School, Queensland University of Technology, GPO Box 2434, Brisbane, QLD 4001, Australia; CRC for Spatial Information, 204 Lygon Street, Carlton, Victoria 3053, Australia.
authors.forename: James
authors.initials: J
authors.lastname: McGree
authors.affiliation: Mathematical Sciences School, Queensland University of Technology, GPO Box 2434, Brisbane, QLD 4001, Australia; CRC for Spatial Information, 204 Lygon Street, Carlton, Victoria 3053, Australia.
authors.forename: Kerrie
authors.initials: K
authors.lastname: Mengersen
mesh_terms: D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D062206:Spatial Analysis
keywords: Data aggregation; Moran’s I statistics; Spatial clustering; Spatial scales; Spatial smoothing