Effect of Geospatial Uncertainty Borderization on Users' Heuristic Reasoning

Abstract. A set of mental strategies called "heuristics" – logical shortcuts that we use to make decisions under uncertainty – has become the subject of a growing number of studies. However, the process of heuristic reasoning about uncertain geospatial data remains relatively under-researched. With this study, we explored the relation between heuristics-driven decision-making and the visualization of geospatial data in states of uncertainty, with a specific focus on the visualization of borders, here termed "borderization". Therefore, we tested a set of cartographic techniques to visualize the boundaries of two types of natural hazards across a series of maps through a user survey. Respondents were asked to assess the safety and desirability of several housing locations potentially affected by air pollution or avalanches. Maps in the survey varied by "borderization" method, background color and type of information about uncertain data (e.g., extrinsic vs. intrinsic). Survey results, analyzed using a mixed quantitative-qualitative approach, confirmed previous suggestions that heuristics play a significant role in affecting users' map experience, and subsequent decision-making

Applying insights on categorisation, communication, and dynamic decision-making: A case study of a ‘simple’ maritime military decision

A complete understanding of decision-making in military domains requires gathering insights from several fields of study. To make the task tractable, here we consider a specific example of short-term tactical decisions under uncertainty made by the military at sea. Through this lens, we sketch out relevant literature from three psychological tasks each underpinned by decision-making processes: categorisation, communication, and choice. From the literature, we note two general cognitive tendencies that emerge across all three stages: the effect of cognitive load and individual differences. Drawing on these tendencies, we recommend strategies, tools and future research that could improve performance in military domains—but, by extension, would also generalise to other high-stakes contexts. In so doing, we show the extent to which domain general properties of high order cognition are sufficient in explaining behaviours in domain specific contexts

Sonifying data uncertainty with sound dimensions

The communication of data uncertainty is a crucial problem in data science, information visualization, and geographic information science (GIScience). Effective ways to communicate the uncertainty of data enables data consumers to interpret the data as intended by the producer, reducing the possibilities of misinterpretation. In this article, we report on an empirical investigation of how sound can be used to convey information about data uncertainty in an intuitive way. To answer the research question How intuitive are sound dimensions to communicate uncertainty?, we carry out a cognitive experiment, where participants were asked to interpret the certainty/uncertainty level in two sounds A and B (N=33). We produce sound stimuli by varying sound dimensions, including loudness, duration, location, pitch, register, attack, decay, rate of change, noise, timbre, clarity, order, and harmony. In the stimuli, both synthetic and natural sounds are used to allow comparison. The experiment results identify three sound dimensions (loudness, order, and clarity) as significantly more intuitive to communicate uncertainty, providing guidelines for sonification and information visualization practitioners

Semi-automated modeling approaches to route selection in GIS

As an alternative to traditional graphical intuitive approaches (GIA), a semi-automated modeling approach (SMA) can more efficiently identify linear routes by using powerful iterative and automated methods. In this research, two case studies were investigated to examine critical issues relating to the accuracy and effectiveness of raster-defined algorithmic approaches to linear route location. The results illustrate that different shortest-path algorithms do not necessarily result in markedly different linear routes. However, differing results can occur when using different neighboring-cell links in the cell-based route network construction. Cell-based algorithmic approaches in both Arc/Info and IDRISI software generate very similar results which are comparable to linear modeling with greater than eight neighboring-cell links. Given a specific shortest-path algorithm and its route searching technique, the use of a finer spatial resolution only results in a narrower and smoother route corridor. Importantly, cost surface models can be generated to represent differing cumulative environmental \u27costs\u27 or impacts in which different perceptions of environmental cost can be simulated and evaluated.;Three different simulation techniques comprising Ordered Weighted Combination models (OWC), Dynamic Decision Space (DDS), and Gateway-based approaches, were used to address problems associated with concurrent and dynamic changes in multi-objective decision space. These approaches provide efficient and flexible simulation capability within a dynamic and changing decision space. When aggregation data models were used within a Gateway approach the match of resulting routes between GIA and SMA analyses is close. The effectiveness of SMA is greatly limited when confronted by extensive linear and impermeable barriers or where data is sparse. Overall, achieving consensus on environmental cost surface generation and criteria selection is a prerequisite for a successful SMA outcome. It is concluded that SMA has several positive advantages that certainly complement a GIA in linear route siting and spatial decision-making

Assisted Viewpoint Interaction for 3D Visualization

Many three-dimensional visualizations are characterized by the use of a mobile viewpoint that offers multiple perspectives on a set of visual information. To effectively control the viewpoint, the viewer must simultaneously manage the cognitive tasks of understanding the layout of the environment, and knowing where to look to find relevant information, along with mastering the physical interaction required to position the viewpoint in meaningful locations. Numerous systems attempt to address these problems by catering to two extremes: simplified controls or direct presentation. This research attempts to promote hybrid interfaces that offer a supportive, yet unscripted exploration of a virtual environment.Attentive navigation is a specific technique designed to actively redirect viewers' attention while accommodating their independence. User-evaluation shows that this technique effectively facilitates several visualization tasks including landmark recognition, survey knowledge acquisition, and search sensitivity. Unfortunately, it also proves to be excessively intrusive, leading viewers to occasionally struggle for control of the viewpoint. Additional design iterations suggest that formalized coordination protocols between the viewer and the automation can mute the shortcomings and enhance the effectiveness of the initial attentive navigation design.The implications of this research generalize to inform the broader requirements for Human-Automation interaction through the visual channel. Potential applications span a number of fields, including visual representations of abstract information, 3D modeling, virtual environments, and teleoperation experiences

Visualizing and communicating uncertainty for map-based decision-making: The case of uncertainty depiction in debris flow predictions

Any type of data is subject to uncertainty in one way or another. The prediction of natural hazards such as debris flows is no exception to this rule, especially in the face of ongoing climate change. Since maps are a valuable tool to depict scientific results, the visualization of uncertainty has occupied cartographers and visualization experts over the past decades. In this research, a large variety of different uncertainty visualization methods have been developed. However, testing their effectiveness and their impact on the decision-making process has not been on the forefront of research. Therefore, the study at hand aimed at testing two types of uncertainty visualization methods (single-hue and multi-hue colour scheme; within-group variable) as well as two ways of communicating uncertainty in the map legend (numerical and verbal expressions; between-group variable) in debris flow prediction maps. A key aspect investigated in this study are the strategies applied to make decisions based on uncertain information. Additionally, the study makes use of eye tracking technology to infer on cognitive processes. Two research questions investigated the influence of the uncertainty visualization and communication methods on decision outcome, response time and decision-making strategy. The goal of the last research question was to gain insight into the sources of information which guide decision-making with uncertainty. The empirical study showed that decision outcomes slightly varied between the two visualization methods. Additionally, the decision-making process seemed to be more complicated when uncertainty was communicated through verbal expressions, as shown by the significant difference in response time. Lastly, it was found that decisions were strongly guided by heuristics related to the uncertainty information as well as the distance parameter. Furthermore, a boundary effect, already observed in other uncertainty visualization studies, occurred. Most importantly however, the results indicate that the non-expert audience had trouble correctly interpreting the uncertainty information. Consequently, it is argued that map design choices might be of secondary importance as long as profound understanding of the concept of uncertainty is lacking among map readers. The study thus calls for more profound training of the public on the concept of uncertainty, its visualization in maps and ways to incorporate it into spatial decision-making

Understanding and Supporting Trade-offs in the Design of Visualizations for Communication.

A shift in the availability of usable tools and public data has prompted mass manufacturing of information visualizations to communicate data insights to broad audiences. Despite available software, professional and novice creators of visualizations that are intended to communicate data insights to broad audiences may struggle to balance conflicting considerations in design. Studying professional practice suggests that expert visualization designers and analysts negotiate difficult design trade-offs in creating customized visualizations, many of which involve deciding how and how much data to present given a priori design goals. This dissertation presents three studies that demonstrate how studying expert visual design and data modeling practice can advance visualization design tools. Insights from these formative studies inform the development of specific frameworks and algorithms. The first study addresses the often ignored, persuasive dimension of narrative visualizations. The framework I propose characterizes the persuasive dimension of visualization design by providing empirical evidence of several classes of rhetorical design strategies that trade-off comprehensive, unbiased data presentation goals with intentions to persuade users toward intended interpretations. The rhetorical visualization framework highlights a second trade-off: the act of dividing and sequencing information from a multivariate data set in separate visualizations for ordered presentation. I contribute initial evidence of ordering principles that designers apply to ease comprehension and support storytelling goals with a visualization presentation. The principles are used in developing a novel algorithmic approach to supporting designers of visualizations in making decisions related to visualization presentation order and structuring, highlighting the importance of optimizing for both local or “single visualization” design in tandem with global “sequence” design. The final design trade-off concerns how to convey uncertainty to end-users in order to support accurate conclusions despite diverse educational backgrounds. I demonstrate how non-statistician end-users can produce more cautious and at times more accurate estimates of the reliability of data patterns through the use of a comparative sample plots method motivated by statistical resampling approaches to modeling uncertainty. Taken together, my results deepen understanding of the act of designing visualizations for potentially diverse online audiences, and provide tools to support more effective design.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107170/1/jhullman_1.pd