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- Member of: Theses and Dissertations
Description
Decision makers contend with uncertainty when working through complex decision problems. Yet uncertainty visualization, and tools for working with uncertainty in GIS, are not widely used or requested in decision support. This dissertation suggests a disjoint exists between practice and research that stems from differences in how visualization researchers conceptualize uncertainty and how decision makers frame uncertainty. To bridge this gap between practice and research, this dissertation explores uncertainty visualization as a means for reframing uncertainty in geographic information systems for use in policy decision support through three connected topics. Initially, this research explores visualizing the relationship between uncertainty and policy outcomes as a means for incorporating policymakers' decision frames when visualizing uncertainty. Outcome spaces are presented as a method to represent the effect of uncertainty on policy outcomes. This method of uncertainty visualization acts as an uncertainty map, representing all possible outcomes for specific policy decisions. This conceptual model incorporates two variables, but implicit uncertainty can be extended to multivariate representations. Subsequently, this work presented a new conceptualization of uncertainty, termed explicit and implicit, that integrates decision makers' framing of uncertainty into uncertainty visualization. Explicit uncertainty is seen as being separate from the policy outcomes, being described or displayed separately from the underlying data. In contrast, implicit uncertainty links uncertainty to decision outcomes, and while understood, it is not displayed separately from the data. The distinction between explicit and implicit is illustrated through several examples of uncertainty visualization founded in decision science theory. Lastly, the final topic assesses outcome spaces for communicating uncertainty though a human subject study. This study evaluates the effectiveness of the implicit uncertainty visualization method for communicating uncertainty for policy decision support. The results suggest that implicit uncertainty visualization successfully communicates uncertainty in results, even though uncertainty is not explicitly shown. Participants also found the implicit visualization effective for evaluating policy outcomes. Interestingly, participants also found the explicit uncertainty visualization to be effective for evaluating the policy outcomes, results that conflict with prior research.
ContributorsDeitrick, Stephanie (Author) / Wentz, Elizabeth (Thesis advisor) / Goodchild, Michael (Committee member) / Edsall, Robert (Committee member) / Gober, Patricia (Committee member) / Arizona State University (Publisher)
Created2013
Description
In this thesis, the application of pixel-based vertical axes used within parallel coordinate plots is explored in an attempt to improve how existing tools can explain complex multivariate interactions across temporal data. Several promising visualization techniques are combined, such as: visual boosting to allow for quicker consumption of large data sets, the bond energy algorithm to find finer patterns and anomalies through contrast, multi-dimensional scaling, flow lines, user guided clustering, and row-column ordering. User input is applied on precomputed data sets to provide for real time interaction. General applicability of the techniques are tested against industrial trade, social networking, financial, and sparse data sets of varying dimensionality.
ContributorsHayden, Thomas (Author) / Maciejewski, Ross (Thesis advisor) / Wang, Yalin (Committee member) / Runger, George C. (Committee member) / Mack, Elizabeth (Committee member) / Arizona State University (Publisher)
Created2014
Description
In visualizing information hierarchies, icicle plots are efficient diagrams in that they provide the user a straightforward layout for different levels of data in a hierarchy and enable the user to compare items based on the item width. However, as the size of the hierarchy grows large, the items in an icicle plot end up being small and indistinguishable. In this thesis, by maintaining the positive characteristics of traditional
icicle plots and incorporating new features such as dynamic diagram and active layer, we developed an interactive visualization that allows the user to selectively drill down or roll up to review different levels of data in a large hierarchy, to change the hierarchical
structure to detect potential patterns, and to maintain an overall understanding of the
current hierarchical structure.
icicle plots and incorporating new features such as dynamic diagram and active layer, we developed an interactive visualization that allows the user to selectively drill down or roll up to review different levels of data in a large hierarchy, to change the hierarchical
structure to detect potential patterns, and to maintain an overall understanding of the
current hierarchical structure.
ContributorsWu, Bi (Author) / Maciejewski, Ross (Thesis advisor) / Runger, George C. (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2014
Description
Node-link diagrams are widely used to visualize the relational structure of real world datasets. As identical data can be visualized in infinite ways by simply changing the spatial arrangement of the nodes, one of the important research topics of the graph drawing community is to visualize the data in the way that can facilitate people's comprehension. The last three decades have witnessed the growth of algorithms for automatic visualization. However, despite the popularity of node-link diagrams and the enthusiasm in improving computational efficiency, little is known about how people read these graphs and what factors (layout, size, density, etc.) have impact on their effectiveness (the usability aspect of the graph, e.g., are they easy to understand?). This thesis is comprehensive research to investigate the factors that affect people's understanding of node-link diagrams using eye-tracking methods. Three experiments were conducted, including 1) a pilot study with 22 participants to explore the layout and size effect; 2) an eye tracking experiment with 43 participants to investigate the layout, size and density effect on people's graph comprehension using abstract node-link diagram and generic tasks; and 3) an eye tracking experiment with the same participants to investigate the same effects using a real visualization analytic application. Results showed that participants' spatial reasoning ability had significant impact on people's graph reading performance. Layout, size, and density were all found to be significant effects under different task circumstances. The applicability of the eye tracking methods on visualization evaluation has been confirmed by providing detailed evidence that demonstrates the cognitive process of participants' graph reading behavior.
ContributorsLiu, Qing (Author) / McKenna, Anna (Thesis advisor) / Jennifer, Jennifer (Committee member) / Cooke, Nancy J. (Committee member) / Arizona State University (Publisher)
Created2015
Description
This paper presents the results of an empirical analysis of deceptive data visualizations paired with explanatory text. Data visualizations are used to communicate information about important social issues to large audiences and are found in the news, social media, and the Internet (Kirk, 2012). Modern technology and software allow people and organizations to easily produce and publish data visualizations, contributing to data visualizations becoming more prevalent as a means of communicating important information (Sue & Griffin, 2016). Ethical transgressions in data visualizations are the intentional or unintentional use of deceptive techniques with the potential of altering the audience’s understanding of the information being presented (Pandey et al., 2015). While many have discussed the importance of ethics in data visualization, scientists have only recently started to look at how deceptive data visualizations affect the reader. This study was administered as an on-line user survey and was designed to test the deceptive potential of data visualizations when they are accompanied by a paragraph of text. The study consisted of a demographic questionnaire, chart familiarity assessment, and data visualization survey. A total of 256 participants completed the survey and were evenly distributed between a control (non-deceptive) survey or a test (deceptive) survey in which participant were asked to observe a paragraph of text and data visualization paired together. Participants then answered a question relevant to the observed information to measure how they perceived the information to be. The individual differences between demographic groups and their responses were analyzed to understand how these groups reacted to deceptive data visualizations compared to the control group. The results of the study confirmed that deceptive techniques in data visualizations caused participants to misinterpret the information in the deceptive data visualizations even when they were accompanied by a paragraph of explanatory text. Furthermore, certain demographics and comfort levels with chart types were more susceptible to certain types of deceptive techniques. These results highlight the importance of education and practice in the area of data visualizations to ensure deceptive practices are not utilized and to avoid potential misinformation, especially when information can be called into question.
ContributorsO'Brien, Shaun (Author) / Laure, Claire (Thesis advisor) / Brumberger, Eva (Committee member) / D'Angelo, Barbara J. (Committee member) / Arizona State University (Publisher)
Created2017
Description
With the advent of social media and micro-blogging sites, people have become active in sharing their thoughts, opinions, ideologies and furthermore enforcing them on others. Users have become the source for the production and dissemination of real time information. The content posted by the users can be used to understand them and track their behavior. Using this content of the user, data analysis can be performed to understand their social ideology and affinity towards Radical and Counter-Radical Movements. During the process of expressing their opinions people use hashtags in their messages in Twitter. These hashtags are a rich source of information in understanding the content based relationship between the online users apart from the existing context based follower and friend relationship.
An intelligent visual dash-board system is necessary which can track the activities of the users and diffusion of the online social movements, identify the hot-spots in the users' network, show the geographic foot print of the users and to understand the socio-cultural, economic and political drivers for the relationship among different groups of the users.
An intelligent visual dash-board system is necessary which can track the activities of the users and diffusion of the online social movements, identify the hot-spots in the users' network, show the geographic foot print of the users and to understand the socio-cultural, economic and political drivers for the relationship among different groups of the users.
ContributorsGaripalli, Sravan Kumar (Author) / Davulcu, Hasan (Thesis advisor) / Shakarian, Paulo (Committee member) / Hsiao, Ihan (Committee member) / Arizona State University (Publisher)
Created2015
Description
The Global Change Assessment Model (GCAM) is an integrated assessment tool for exploring consequences and responses to global change. However, the current iteration of GCAM relies on NetCDF file outputs which need to be exported for visualization and analysis purposes. Such a requirement limits the uptake of this modeling platform for analysts that may wish to explore future scenarios. This work has focused on a web-based geovisual analytics interface for GCAM. Challenges of this work include enabling both domain expert and model experts to be able to functionally explore the model. Furthermore, scenario analysis has been widely applied in climate science to understand the impact of climate change on the future human environment. The inter-comparison of scenario analysis remains a big challenge in both the climate science and visualization communities. In a close collaboration with the Global Change Assessment Model team, I developed the first visual analytics interface for GCAM with a series of interactive functions to help users understand the simulated impact of climate change on sectors of the global economy, and at the same time allow them to explore inter comparison of scenario analysis with GCAM models. This tool implements a hierarchical clustering approach to allow inter-comparison and similarity analysis among multiple scenarios over space, time, and multiple attributes through a set of coordinated multiple views. After working with this tool, the scientists from the GCAM team agree that the geovisual analytics tool can facilitate scenario exploration and enable scientific insight gaining process into scenario comparison. To demonstrate my work, I present two case studies, one of them explores the potential impact that the China south-north water transportation project in the Yangtze River basin will have on projected water demands. The other case study using GCAM models demonstrates how the impact of spatial variations and scales on similarity analysis of climate scenarios varies at world, continental, and country scales.
ContributorsChang, Zheng (Author) / Maciejewski, Ross (Thesis advisor) / Sarjoughian, Hessam S. (Committee member) / White, Dave (Committee member) / Luo, Wei (Committee member) / Arizona State University (Publisher)
Created2015
Description
Software engineering education today is a technologically advanced and rapidly evolving discipline. Being a discipline where students not only design but also build new technology, it is important that they receive a hands on learning experience in the form of project based courses. To maximize the learning benefit, students must conduct project-based learning activities in a consistent rhythm, or cadence. Project-based courses that are augmented with a system of frequent, formative feedback helps students constantly evaluate their progress and leads them away from a deadline driven approach to learning.
One aspect of this research is focused on evaluating the use of a tool that tracks student activity as a means of providing frequent, formative feedback. This thesis measures the impact of the tool on student compliance to the learning process. A personalized dashboard with quasi real time visual reports and notifications are provided to undergraduate and graduate software engineering students. The impact of these visual reports on compliance is measured using the log traces of dashboard activity and a survey instrument given multiple times during the course.
A second aspect of this research is the application of learning analytics to understand patterns of student compliance. This research employs unsupervised machine learning algorithms to identify unique patterns of student behavior observed in the context of a project-based course. Analyzing and labeling these unique patterns of behavior can help instructors understand typical student characteristics. Further, understanding these behavioral patterns can assist an instructor in making timely, targeted interventions. In this research, datasets comprising of student’s daily activity and graded scores from an under graduate software engineering course is utilized for the purpose of identifying unique patterns of student behavior.
One aspect of this research is focused on evaluating the use of a tool that tracks student activity as a means of providing frequent, formative feedback. This thesis measures the impact of the tool on student compliance to the learning process. A personalized dashboard with quasi real time visual reports and notifications are provided to undergraduate and graduate software engineering students. The impact of these visual reports on compliance is measured using the log traces of dashboard activity and a survey instrument given multiple times during the course.
A second aspect of this research is the application of learning analytics to understand patterns of student compliance. This research employs unsupervised machine learning algorithms to identify unique patterns of student behavior observed in the context of a project-based course. Analyzing and labeling these unique patterns of behavior can help instructors understand typical student characteristics. Further, understanding these behavioral patterns can assist an instructor in making timely, targeted interventions. In this research, datasets comprising of student’s daily activity and graded scores from an under graduate software engineering course is utilized for the purpose of identifying unique patterns of student behavior.
ContributorsXavier, Suhas (Author) / Gary, Kevin A (Thesis advisor) / Bansal, Srividya K (Committee member) / Sohoni, Sohum (Committee member) / Arizona State University (Publisher)
Created2016
Description
The proper quantification and visualization of uncertainty requires a high level of domain knowledge. Despite this, few studies have collected and compared the roles, experiences and opinions of scientists in different types of uncertainty analysis. I address this gap by conducting two types of studies: 1) a domain characterization study with general questions for experts from various fields based on a recent literature review in ensemble analysis and visualization, and; 2) a long-term interview with domain experts focusing on specific problems and challenges in uncertainty analysis. From the domain characterization, I identified the most common metrics applied for uncertainty quantification and discussed the current visualization applications of these methods. Based on the interviews with domain experts, I characterized the background and intents of the experts when performing uncertainty analysis. This enables me to characterize domain needs that are currently underrepresented or unsupported in the literature. Finally, I developed a new framework for visualizing uncertainty in climate ensembles.
ContributorsLiang, Xing (Author) / Maciejewski, Ross (Thesis advisor) / Mascaro, Giuseppe (Committee member) / Sarjoughian, Hessam S. (Committee member) / Arizona State University (Publisher)
Created2016
Description
Mobile data collection (MDC) applications have been growing in the last decade
especially in the field of education and research. Although many MDC applications are
available, almost all of them are tailor-made for a very specific task in a very specific
field (i.e. health, traffic, weather forecasts, …etc.). Since the main users of these apps are
researchers, physicians or generally data collectors, it can be extremely challenging for
them to make adjustments or modifications to these applications given that they have
limited or no technical background in coding. Another common issue with MDC
applications is that its functionalities are limited only to data collection and storing. Other
functionalities such as data visualizations, data sharing, data synchronization and/or data updating are rarely found in MDC apps.
This thesis tries to solve the problems mentioned above by adding the following
two enhancements: (a) the ability for data collectors to customize their own applications
based on the project they’re working on, (b) and introducing new tools that would help
manage the collected data. This will be achieved by creating a Java standalone
application where data collectors can use to design their own mobile apps in a userfriendly Graphical User Interface (GUI). Once the app has been completely designed
using the Java tool, a new iOS mobile application would be automatically generated
based on the user’s input. By using this tool, researchers now are able to create mobile
applications that are completely tailored to their needs, in addition to enjoying new
features such as visualize and analyze data, synchronize data to the remote database,
share data with other data collectors and update existing data.
especially in the field of education and research. Although many MDC applications are
available, almost all of them are tailor-made for a very specific task in a very specific
field (i.e. health, traffic, weather forecasts, …etc.). Since the main users of these apps are
researchers, physicians or generally data collectors, it can be extremely challenging for
them to make adjustments or modifications to these applications given that they have
limited or no technical background in coding. Another common issue with MDC
applications is that its functionalities are limited only to data collection and storing. Other
functionalities such as data visualizations, data sharing, data synchronization and/or data updating are rarely found in MDC apps.
This thesis tries to solve the problems mentioned above by adding the following
two enhancements: (a) the ability for data collectors to customize their own applications
based on the project they’re working on, (b) and introducing new tools that would help
manage the collected data. This will be achieved by creating a Java standalone
application where data collectors can use to design their own mobile apps in a userfriendly Graphical User Interface (GUI). Once the app has been completely designed
using the Java tool, a new iOS mobile application would be automatically generated
based on the user’s input. By using this tool, researchers now are able to create mobile
applications that are completely tailored to their needs, in addition to enjoying new
features such as visualize and analyze data, synchronize data to the remote database,
share data with other data collectors and update existing data.
ContributorsAl-Kaf, Zahra M (Author) / Lindquist, Timothy E (Thesis advisor) / Bansal, Srividya (Committee member) / Bansal, Ajay (Committee member) / Arizona State University (Publisher)
Created2016