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Spatial smoothing in Bayesian models: a comparison of weights matrix specifications and their impact on inference

Overview of attention for article published in International Journal of Health Geographics, December 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

Mentioned by

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7 tweeters
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1 Facebook page

Citations

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27 Dimensions

Readers on

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62 Mendeley
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1 CiteULike
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Title
Spatial smoothing in Bayesian models: a comparison of weights matrix specifications and their impact on inference
Published in
International Journal of Health Geographics, December 2017
DOI 10.1186/s12942-017-0120-x
Pubmed ID
Authors

Earl W. Duncan, Nicole M. White, Kerrie Mengersen

Abstract

When analysing spatial data, it is important to account for spatial autocorrelation. In Bayesian statistics, spatial autocorrelation is commonly modelled by the intrinsic conditional autoregressive prior distribution. At the heart of this model is a spatial weights matrix which controls the behaviour and degree of spatial smoothing. The purpose of this study is to review the main specifications of the spatial weights matrix found in the literature, and together with some new and less common specifications, compare the effect that they have on smoothing and model performance. The popular BYM model is described, and a simple solution for addressing the identifiability issue among the spatial random effects is provided. Seventeen different definitions of the spatial weights matrix are defined, which are classified into four classes: adjacency-based weights, and weights based on geographic distance, distance between covariate values, and a hybrid of geographic and covariate distances. These last two definitions embody the main novelty of this research. Three synthetic data sets are generated, each representing a different underlying spatial structure. These data sets together with a real spatial data set from the literature are analysed using the models. The models are evaluated using the deviance information criterion and Moran's I statistic. The deviance information criterion indicated that the model which uses binary, first-order adjacency weights to perform spatial smoothing is generally an optimal choice for achieving a good model fit. Distance-based weights also generally perform quite well and offer similar parameter interpretations. The less commonly explored options for performing spatial smoothing generally provided a worse model fit than models with more traditional approaches to smoothing, but usually outperformed the benchmark model which did not conduct spatial smoothing. The specification of the spatial weights matrix can have a colossal impact on model fit and parameter estimation. The results provide some evidence that a smaller number of neighbours used in defining the spatial weights matrix yields a better model fit, and may provide a more accurate representation of the underlying spatial random field. The commonly used binary, first-order adjacency weights still appear to be a good choice for implementing spatial smoothing.

Twitter Demographics

The data shown below were collected from the profiles of 7 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 62 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 27%
Researcher 12 19%
Student > Master 7 11%
Student > Bachelor 5 8%
Student > Doctoral Student 3 5%
Other 4 6%
Unknown 14 23%
Readers by discipline Count As %
Medicine and Dentistry 14 23%
Social Sciences 6 10%
Agricultural and Biological Sciences 4 6%
Environmental Science 4 6%
Economics, Econometrics and Finance 3 5%
Other 9 15%
Unknown 22 35%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 April 2021.
All research outputs
#4,662,205
of 19,180,943 outputs
Outputs from International Journal of Health Geographics
#176
of 610 outputs
Outputs of similar age
#111,982
of 427,249 outputs
Outputs of similar age from International Journal of Health Geographics
#13
of 46 outputs
Altmetric has tracked 19,180,943 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 610 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.0. This one has gotten more attention than average, scoring higher than 71% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 427,249 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 46 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.