Vizumap: An R package for visualizing uncertainty in spatial data


23 July 2019

Time and Venues


Local Time

Adelaide Waite Campus – B101-FG-R00-SmallWICWest

12:00 pm

Armidale – B55-FG-R00-Small

12:30 pm

Bribie Island – B01-FG-Small

12:30 pm

Brisbane St Lucia QBP – Room 3.323

12:30 pm

Canberra Black Mountain – Discovery Lecture Theatre

12:30 pm

Irymple (See Natalie Strickland)

12:30 pm

Narrabri B03-FG-R00-ATCA

12:30 pm

Perth Floreat B40-F1-R46-Rossiter Room

10:30 am

Sandy Bay (Hobart) – B2 F1 R22 Forest View Room

12:30 pm

Toowoomba – Media Lab Room

12:30 pm

Townsville (see Liz Do)

12:30 pm

Werribee (Melbourne) – Peacock Room

12:30 pm


Dr Petra Kuhnert, Statistician Data61 Canberra


The quantification, visualization and communication of uncertainty in spatial and spatio-temporal data is important for decision-making. It can highlight regions on a map that are poorly predicted and identify a need for further sampling. Uncertainty can also help to prioritise regions in terms of where to focus remediation efforts and allocate investment. It can also provide some assurance on where modelling efforts are working well and where it fails to trigger further investigation. Unfortunately, uncertainty is rarely included on maps that convey spatial or spatio-temporal estimates.

Approaches for visualizing uncertainty in spatial and spatio-temporal data will be presented. These include the bivariate choropleth map, map pixelation, glyph rotation and exceedance probability maps. Bivariate choropleth maps explore the “blending” of two colour schemes, one representing the estimate and a second representing the margin of error. The second approach uses map pixelation to convey uncertainty. The third approach uses a glyph to represent uncertainty and is what we refer to as glyph rotation. The final map based exploration of uncertainty is through exceedance probabilities.

Vizumap, is an R package that has been developed within Digiscape for visualising uncertainty in spatial and spatio-temporal data. To illustrate the methods, I use an example from the Great Barrier Reef, where sediment loads were quantified from a Bayesian Hierarchical Model (BHM) that assimilated estimates of sediment concentration and flow with modelled output from a catchment model spanning 21 years of daily outputs and 411 spatial locations.

Keywords: spatial data; margin of error; decision-making; visuanimation; Great Barrier Reef

About the speaker

Petra is a Research Statistician in Data61 with over 40 published articles in high ranking applied journals. She has a PhD in applied statistics with her interests focussing more recently on managing model uncertainty and its communication for decision making, the development of data assimilation methods for blending modelled output with measurements, investigating elicitation practices with experts on risk related issues and the translation and synthesis of expert opinion into priors to inform Bayesian models. Petra was a CSIRO Julius Award recipient in 2010, which provided her with an opportunity to develop strong linkages with international collaborators from leading statistics institutions in the areas of spatio-temporal modelling, data assimilation, Bayesian Hierarchical Modelling, expert elicitation and managing model uncertainty. Petra was awarded the 2013 Abdel El-Shaarawi Young Investigator Award for significant interdisciplinary collaboration and impact, the promotion and development of cutting edge statistical methods in the environmental sciences, particularly in water quality, fisheries, and ecological research, and strong contributions to expert elicitation, Bayesian hierarchical modelling, and non-parametric regression.

This is a public seminar.

Open-access to The CSIRO Discovery Theatre @ Black Mountain