Stat The ISI’s Journal for the Rapid (wileyonlinelibrary.com) DOI: 10.1002/sta4.150 Dissemination of Statistics Research Visualizing uncertainty in areal data with bivariate choropleth maps, map pixelation and glyph rotation  Lydia R. Lucchesi and Christopher K. Wikle  Received 15 May 2017; Accepted 24 May 2017 In statistics, we quantify uncertainty to help determine the accuracy of estimates, yet this crucial piece of infor- mation is rarely included on maps visualizing areal data estimates. We develop and present three approaches to include uncertainty on maps: (1) the bivariate choropleth map repurposed to visualize uncertainty; (2) the pixela- tion of counties to include values within an estimate’s margin of error; and (3) the rotation of a glyph, located at a county’s centroid, to represent an estimate’s uncertainty. The second method is presented as both a static map and visuanimation. We use American Community Survey estimates and their corresponding margins of error to demonstrate the methods and highlight the importance of visualizing uncertainty in areal data. An extensive online supplement provides the R code necessary to produce the maps presented in this article as well as alternative versions of them. Copyright © 2017 John Wiley & Sons, Ltd. Keywords: sample surveys; spatial statistics; statistical graphics; visualization 1 Introduction In statistics, we quantify uncertainty to help determine the accuracy of estimates. Yet this crucial piece of information is rarely included on a map of areal data, and the best methods for displaying it are still not clearly defined (MacEachren et al., 2005). On a choropleth map, latitude and longitude require two dimensions, and colours symbolizing the esti- mates fill the defined spaces. Although it is difficult to effectively add more information to such a visual presentation, there is increasing interest in creating maps that include uncertainty and thus provide a more accurate depiction of the statistics (Bonneau et al., 2014). In this article, we present three approaches to visualizing uncertainty: (1) the bivariate choropleth map repurposed to visualize uncertainty; (2) the pixelation of counties to include values within an estimate’s margin of error; and (3) the rotation of a glyph, located at a county’s centroid, to represent an estimate’s uncertainty. In addition to static maps, we present a visuanimation, a concept developed by Genton et al. (2015). It is produced by animating the second method and embedding the dynamic map within this paper. We have also pro- duced an extensive vignette to demonstrate the R commands necessary to produce each of the visualizations. This is included in the online supplement. Department of Statistics, University of Missouri, Columbia, MO 65211, USA  Email: wiklec@missouri.edu  Requires Adobe Reader Stat 2017; 6: 292–302 Copyright © 2017 John Wiley & Sons, Ltd