Reframing Spatial Uncertainty Visualisation with a Layered Grammar of Graphics
Abstract
Communicating both estimated values and their associated uncertainties presents a particular challenge in spatial visualisation because spatial coordinates already occupy the primary visual channels, leaving relatively few channels available for encoding uncertainty alongside the estimated values. A range of visualisation techniques has been developed to address this challenge, including bivariate choropleth maps, pixel maps, and glyph maps. However, existing implementations typically treat these techniques as dedicated visualisations rather than expressing them through the composable elements of a grammar of graphics. We reframe uncertainty visualisation as an extension of the layered grammar of graphics, allowing uncertainty-specific representations to be composed, customised, and extended alongside other graphical components. We demonstrate this perspective by integrating uncertainty visualisation into the layered architecture of ggplot2, accommodating these techniques while also motivating new approaches such as the dual map for selective attention between estimated values and their associated uncertainties. We implement the framework in the R package ggincerta and demonstrate its application through three case studies.
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BibTeX
@misc{ma2026reframing,
title = {{Reframing Spatial Uncertainty Visualisation with a Layered Grammar of Graphics}},
author = {Maggie Xueqi Ma and Weihao Li and Quan Vu and Francis Hui and Emi Tanaka},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.40258},
eprint = {2610.40258},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.40258},
}
RIS
TY - UNPB
TI - Reframing Spatial Uncertainty Visualisation with a Layered Grammar of Graphics
AU - Maggie Xueqi Ma
AU - Weihao Li
AU - Quan Vu
AU - Francis Hui
AU - Emi Tanaka
PY - 2026
DA - 2026/10/29
PB - arXiv
DO - 10.48550/arxiv.2610.40258
UR - https://arxiv.org/abs/2610.40258
AB - Communicating both estimated values and their associated uncertainties presents a particular challenge in spatial visualisation because spatial coordinates already occupy the primary visual channels, leaving relatively few channels available for encoding uncertainty alongside the estimated values. A range of visualisation techniques has been developed to address this challenge, including bivariate choropleth maps, pixel maps, and glyph maps. However, existing implementations typically treat these techniques as dedicated visualisations rather than expressing them through the composable elements of a grammar of graphics. We reframe uncertainty visualisation as an extension of the layered grammar of graphics, allowing uncertainty-specific representations to be composed, customised, and extended alongside other graphical components. We demonstrate this perspective by integrating uncertainty visualisation into the layered architecture of ggplot2, accommodating these techniques while also motivating new approaches such as the dual map for selective attention between estimated values and their associated uncertainties. We implement the framework in the R package ggincerta and demonstrate its application through three case studies.
ER -