Vizumap: an R package for visualising uncertainty in spatial data

Le résumé fourni par la source

To make a sound data-driven decision, it is necessary to consider the quality and strength of the evidence.Therefore, it is important that the uncertainty of statistical estimates be effectively communicated to decision makers to ensure it is properly considered in the decision-making process.Generally, this uncertainty information is shared through visualisation (e.g., error bars).However, in spatial applications, finding methods that can communicate additional information, about the spatial estimates, in an understandable and meaningful way can be challenging.To address this visualisation shortcoming in spatial statistics, we developed the Vizumap R package.It is a toolkit designed for statisticians, scientists, data journalists, etc., discussing uncertainty in spatial data.Vizumap contains a series of straightforward functions that can be used to create four different types of maps, which are discussed in detail in Lucchesi & Wikle (2017) and Kuhnert et al. (2018).The visualisation methods include the bivariate choropleth map, pixel map, glyph map, and exceedance probability map.Following is a brief description of each method.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.