Testing Popular Visualization Techniques for Representing Model Uncertainty

Abstract

Many land allocation issues, such as land-use planning, require input from extensive spatial databases and involve complex decision-making. Spatial decision support systems (SDSS) are designed to make these issues more transparent and to support the design and evaluation of land allocation alternatives. In this paper we analyze techniques for visualizing uncertainty of an urban growth model called SLEUTH, which is designed to aid decision-makers in the field of urban planning and fits into the computational framework of an SDSS. Two simple visualization techniques for portraying uncertainty-static comparison and toggling-are applied to SLEUTH results and rendered with different background information and color schemes. In order to evaluate the effectiveness of the two visualization techniques, a web-based survey was developed showing the visualizations along with questions about the usefulness of the two techniques. The web survey proved to be quickly accessible and easy to understand by the participants. Participants in the survey were mainly recruited among planners and decision-makers. They acknowledged the usefulness of portraying uncertainty for decision-making purposes. They slightly favored the static comparison technique over toggling. Both visualization techniques were applied to an urban growth case study for the greater Santa Barbara area in California, USA.

How to Assess Visual Communication of Uncertainty? A Systematic Review of Geospatial Uncertainty Visualisation User Studies

“…Studies dealing with uncertainty information mainly cover the first component, attribute (also called thematic) uncertainty. Typically, they relate to uncertain model outputs (Aerts et al, 2003;Alberti, 2013), classification uncertainty (Blenkinsop et al, 2000;Drecki, 2002;Kinkeldey et al, 2014) or reliability (e.g. variance) of statistical data (MacEachren et al, 1998).…”

“…Examples include colour hue and colour value (a term under which we subsume value, lightness and brightness) (e.g. Aerts et al, 2003;Edwards and Nelson, 2001;Leitner and Buttenfield, 2000;MacEachren et al, 1998;Nadav-Greenberg et al, 2008;Retchless, 2012;Schweizer and Goodchild, 1992;Slocum et al, 2003), transparency (e.g. Drecki, 2002;Newman and Lee, 2004;Slocum et al, 2003;Viard et al, 2011), or colour saturation that is used in a number of studies (e.g.…”

“…While most studies assess coincident approaches, there are a number of studies that involve a direct comparison between adjacent and coincident views (Aerts et al, 2003;Edwards and Nelson, 2001;Evans, 1997;Gerharz and Pebesma, 2009;Kardos, 2003;Kardos, 2007;Kubíček and Šašinka, 2011;Kunz et al, 2011;MacEachren et al, 1998;Retchless, 2012;Senaratne et al, 2012;Viard et al, 2011).…”

“…As the display typically can become complex when uncertainty is added to data depictions, there have also been numerous attempts to utilize dynamic views. Some of these use non-interactive animation (Aerts et al, 2003;Blenkinsop et al, 2000;Evans, 1997;Kardos et al, 2003;Kardos et al, 2007;Zhang et al, 2008) and some incorporate interactive interfaces (Alberti, 2013;Blenkinsop et al, 2000;Evans, 1997;Gerharz and Pebesma, 2009;Slocum et al, 2003;Senaratne et al, 2012).…”

“…Past research has suggested that communicating information about data uncertainty has the potential to increase trust in the results when analyses are conducted (Fisher et al, 2012) and to support decision making that uses the data (Aerts et al, 2003;Deitrick and Edsall, 2006;Leitner and Buttenfield, 2000). Visualisation of uncertainty has attracted substantial attention over more than two decades.…”