spconfShiny: An R Shiny application for calculating the spatial scale of smoothing splines for point data

Epidemiological analyses of environmental exposures often benefit from including spatial splines in models to account for confounding by spatial location. Understanding how the number of splines relates to physical spatial differences is not always intuitive and can be context-dependent. To address...

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Bibliographic Details
Published in:PloS one Vol. 19; no. 10; p. e0311440
Main Authors: Rainey, Maddie J., Keller, Kayleigh P.
Format: Journal Article
Language:English
Published: United States Public Library of Science 04.10.2024
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ISSN:1932-6203, 1932-6203
Online Access:Get full text
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Summary:Epidemiological analyses of environmental exposures often benefit from including spatial splines in models to account for confounding by spatial location. Understanding how the number of splines relates to physical spatial differences is not always intuitive and can be context-dependent. To address this, we developed a R Shiny application, spconfShiny, that provides a user-friendly platform to calculate an effective bandwidth metric that quantifies the relationship between spatial splines and the range of implied spatial smoothing. spconfShiny can be accessed at https://g2aging.shinyapps.io/spconfShiny/ . We illustrate the procedure to compute the effective bandwidth and demonstrate its use for different numbers of spatial splines across England, India, Ireland, Northern Ireland, and the United States. Using spconfShiny, we show the effective bandwidth increases with the size of the region and decreases with the number of splines. Including 10 splines on a 10km grid corresponds to effective bandwidths of 92.2km in Ireland and 927.7km in the United States.
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ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0311440