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The choice of spatial scales and spatial smoothness priors for various spatial patterns
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.
Spatial and Spatio-Temporal Epidemiology
|August 13, 2014
Summary
Analyzing data at a grid level offers a flexible alternative to geo-political mapping. This study guides selecting spatial scales and smoothness priors for effective grid-based spatial smoothing and modeling.
Area of Science:
- Spatial statistics
- Geographic Information Systems (GIS)
- Environmental modeling
Background:
- Traditional geo-political mapping can be inadequate for non-political data analysis.
- Grid-level data aggregation allows for spatial smoothing and modeling at relevant biological or physical scales.
Purpose of the Study:
- To address challenges in grid-based spatial analysis: selecting spatial smoothness priors and determining appropriate grid scales.
- To provide guidance on choosing spatial scales and smoothness priors for various point patterns.
Main Methods:
- Describing and discussing various spatial smoothness priors for grid data.
- Analyzing the influence of spatial patterns on grid size selection.
- Developing a guide for selecting spatial scales and smoothness priors.
Main Results:
- Different spatial smoothness priors are suitable for different analytical aims, such as identifying clustering or modeling spatial dependence.
- The optimal grid size is dependent on observed spatial patterns.
- A framework is presented for selecting appropriate spatial scales and priors.
Conclusions:
- Grid-level analysis offers advantages over geo-political mapping for certain data types.
- Careful selection of spatial scales and smoothness priors is crucial for effective spatial modeling and analysis.
- The study provides practical guidance for researchers utilizing grid-based spatial data.
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