About: Pixelate to communicate: visualising uncertainty in maps of disease risk and other spatial continua

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About: Pixelate to communicate: visualising uncertainty in maps of disease risk and other spatial continua Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Pixelate to communicate: visualising uncertainty in maps of disease risk and other spatial continua
Creator	Buckee, Caroline Taylor, Aimee Watson, James
source	ArXiv
abstract	Maps have long been been used to visualise estimates of spatial variables, in particular disease burden and risk. Predictions made using a geostatistical model have uncertainty that typically varies spatially. However, this uncertainty is difficult to map with the estimate itself and is often not included as a result, thereby generating a potentially misleading sense of certainty about disease burden or other important variables. To remedy this, we propose simultaneously visualising predictions and their associated uncertainty within a single map by varying pixel size. We illustrate our approach using examples of malaria incidence, but the method could be applied to predictions of any spatial continua with associated uncertainty.
has issue date	2020-05-25(xsd:dateTime)
has license	arxiv
sha1sum (hex)	0c1fd058b624af98542525f9ab3768ad1b586605
resource representing a document's title	Pixelate to communicate: visualising uncertainty in maps of disease risk and other spatial continua
resource representing a document's body	covid:0c1fd058b624af98542525f9ab3768ad1b586605#body_text
is schema:about of	named entity 'uncertainty' named entity 'risk' named entity 'communicate' named entity 'COULD BE' named entity 'PARTICULAR' »more»

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