Vizumap: An R package for visualizing uncertainty in spatial data
Date
23 July 2019
Time and Venues
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 |
Speaker
Dr Petra Kuhnert, Statistician Data61 Canberra
Synopsis
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