This dissertation focuses on the development of visualization methods that enable us to examine longitudinal data in a unique way. We take inspiration from statistical and fluid mechanics to represent our data as a "flow" through time. Our visualizations represent vector fields (or flow plots), streamlines, and trajectories, and they are constructed in a similar manner to how one might analyze the aggregate motion of particles in a fluid. However, the subject of our research extends beyond ordinary fluid mechanics. We will use our visualizations to examine statewide standardized test scores in Texas from 2003 to 2011. The nature of the data makes it a perfect match for our methodology, since students' test scores tend to change over time in a semi-deterministic but nonlinear manner. Furthermore, our methods represent a departure from the standard ways of analyzing educational data. By visualizing the changes in students' test scores over a nine-year period, we discovered that our flow plots were changing with the eventual graduating class of 2012. The change in our visualizations was caused by an educational policy known as the Student Success Initiative, or SSI. The policy forced students to pass their standardized tests in 5th and 8th grade, or risk being held back a grade. To help with this process, students who initially failed were given extra instruction and additional opportunities to take the test. SSI was implemented in such a way that it would affect the class of 2012 and beyond, although we did not know of the program's existence until our plots had been developed. SSI had a successful impact on the educational career of Texas students; a far greater percentage of students were able to pass the 5th and 8th grade standardized tests after SSI was implemented. The striking feature of SSI, however, is that it also significantly improved test scores in 6th, 7th, 9th, and 10th grade. Despite its success at improving test scores over many years and grades, the program was eventually defunded. This was partially due to an inability to construct a lengthy longitudinal analysis of the program's influence. Our methodology would have conclusively shown the effectiveness of the SSI policy. Despite the defunding of the SSI, I am confident our methodology can be extended to illustrate changes in other data systems. These systems may or may not be related to education; our code and techniques are designed to be as universal as possible. We will explore several extensions to other data sets at the end of this dissertation. / text

This thesis presents a system to display Structural Health Monitoring (SHM) data interactively at multiple scales that range from milliseconds to years. Typically, visualizing large SHM datasets produce static plots that take significant time to render. Our system improves upon standard tools by providing an interactive interface and a speed-optimized binning algorithm. Using the interface, a user is able to view data collected from a bridge's sensors at multiple scales in a web browser. This allows a user to visually inspect the entire range of their data to see both short and long-term trends. To render the data, the system uses a binning algorithm to calculate a five-number summary of a range of data. Those bins are combined to generate increasingly high levels of bins, which are then rendered as a binned line chart. The chart is rendered using a standard web browser on both desktop and mobile devices.

With the complexity increase of enterprise solutions, the need to monitor and maintain them increases with it. SAP Concur offers various services and applications across different environments and data centers. For all these applications and the services underneath, there are different Application Performance Management (APM) tools in place for monitoring them. However, from an incident management point of view, in case of a problem it is time consuming and non-efficient to go through different tools in order to identify the issue. This thesis proposes a solution for a custom and centralized APM which gathers metrics and raw data from multiple sources and visualizes them in real-time in a unified health dashboard called Pulse. In order to fit this solution to the needs of service managers and product owners, Pulse will go through different phases of usability tests and after each phase, new requirements will be implemented and tested again until there is a final design that fits the needs of target users.

The eerie feeling attributed to human-looking robots and animated characters may be a key factor in our perceptual and cognitive discrimination between the human and the merely humanlike. This study applies factor analysis, correlation, the generalized linear model (GLM), multidimensional scaling (MDS), and kernel isometric mapping (ISOMAP) to analyze ratings of 27 emotions of 16 moving figures whose appearance varies along a human likeness continuum. The results indicate (1) Attributions of eerie and creepy better capture human visceral reaction to an uncanny robot than strange. (2) Eeriness and creepiness are mainly associated with fear but also shocked, disgusted, and nervous. Strange and humanlike are less strongly associated with emotion. (3) Thus, strange and humanlike may be more cognitive, while eerie and creepy are more perceptual and emotional. (4) Human and facial features increase ratings of human likeness. (5) Women are slightly more sensitive to eerie and creepy than men; and older people may be more willing to attribute human likeness to a robot despite its eeriness.

As the world becomes more urbanized and heavy precipitation events increase in frequency and intensity, urban flooding is an emerging concern. Urban flooding is caused when heavy rainfall collects on the landscape, exceeding the capacity of drainage systems to effectively convey runoff. Unlike riverine and coastal flooding, urban flooding occurs frequently, and its risks and impacts are not restricted to areas within floodplains or near bodies of water. The objective of this dissertation is to improve our understanding of urban flooding and our capability to predict it through the development of tools and knowledge to assist with its analysis, modeling, and forecasting. To do this, three research objectives were fulfilled. First, the Stream Hydrology And Rainfall Knowledge System (SHARKS) app was developed to improve upon existing real-time hydrologic and meteorological data retrieval/visualization platforms through the integration of analysis tools to study the hydrologic processes influencing urban flooding. Next, the ability to simulate the hydrologic response of urban watersheds with large storm sewer networks was compared between the fully distributed Gridded Surface/Subsurface Hydrologic Analysis (GSSHA) model and the semi-distributed Storm Water Management Model (SWMM). Finally, the Probabilistic Urban Flash Flood Information Nexus (PUFFIN) application was created to help users evaluate the probability of urban flash flooding and to identify specific infrastructure components at risk through the integration of high-resolution quantitative precipitation forecasting, ensemble forecasting, and hydrologic and hydraulic modeling. The outcomes of this dissertation provide municipalities with tools and knowledge to assist them throughout the process of developing solutions to their site-specific urban flooding issues. Specifically, tools are provided to rapidly analyze and respond to rainfall and streamflow/depth information during intense rain events and to perform retrospective analysis of long-term hydrological processes. Evaluations are included to help guide the selection of hydrologic and hydraulic models for modeling urban flooding, and a new proactive paradigm of probabilistic flash flood guidance for urban areas is introduced. Finally, several potential directions for future work are recommended. / Doctor of Philosophy / As the world becomes more urbanized and heavy precipitation events increase in frequency and intensity, urban flooding is an emerging concern. Urban flooding is caused when heavy rainfall collects on the landscape, exceeding the capacity of drainage systems to effectively convey runoff. Unlike riverine and coastal flooding, urban flooding occurs frequently, and its risks and impacts are not restricted to areas within floodplains or near bodies of water. The objective of this dissertation is to improve our understanding of urban flooding and our capability to predict it through the development of tools and knowledge to assist with its analysis, modeling, and forecasting. To do this, three research objectives were fulfilled. First, the Stream Hydrology And Rainfall Knowledge System (SHARKS) app was developed to improve upon existing real-time hydrologic and meteorological data retrieval/visualization platforms through the integration of analysis tools to study the hydrologic processes influencing urban flooding. Next, the ability to simulate the hydrologic response of urban watersheds with large storm sewer networks was compared between the fully distributed Gridded Surface/Subsurface Hydrologic Analysis (GSSHA) model and the semi-distributed Storm Water Management Model (SWMM). Finally, the Probabilistic Urban Flash Flood Information Nexus (PUFFIN) application was created to help users evaluate the probability of urban flash flooding and to identify specific infrastructure components at risk through the integration of high-resolution quantitative precipitation forecasting, ensemble forecasting, and hydrologic and hydraulic modeling. The outcomes of this dissertation provide municipalities with tools and knowledge to assist them throughout the process of developing solutions to their site-specific urban flooding issues. Specifically, tools are provided to rapidly analyze and respond to rainfall and streamflow/depth information during intense rain events and to perform retrospective analysis of long-term hydrological processes. Evaluations are included to help guide the selection of hydrologic and hydraulic models for modeling urban flooding, and a new proactive paradigm of probabilistic flash flood guidance for urban areas is introduced. Finally, several potential directions for future work are recommended.

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With the rapid development of power systems, more and more smart devices are installed in power industries, and each of them is gathering tons of information every day.Due to the data explosion and the difficulty of processing these data, data visualization, a big data technology, has become a trend. With the help of information technology, the visualization of real-time data has been achieved in power industries and there are multiple successful examples such as one-line diagram, load flow dashboard and equipment dashboard.In fault analysis group, digital fault recorders are essential to record and report an event.They are triggered when a fault occurs and corresponding report is generated instantly.However, people seldom utilize the historical data from DFRs to analyze the power quality issues.Therefore, this thesis presents the development of a power quality dashboard by using the collected data from DFRs.Three related power quality analyses have been accomplished in this paper: voltage and current variation, harmonics and unbalance components.Recursive algorithm is applied to compute the phasors and errors; Discrete Fourier Transform is utilized to extract harmonics from the samples; and the symmetric components are calculated by "A"-matrix transformation.The start page for the dashboard is a google map with all the DFR markers, and after double-clicking the marker, the report page will be opened.With the reports, engineers can not only monitor the event but also analyze out the possible causes and characteristics for a fault.For those renewable energy substation, the harmonic contents can be supervised so that the damages and losses can be significantly reduced by identifying the high harmonics.Ultimately, the goal of the dashboard is to achieve warning status and harmonic gradient mapping in the future. / Master of Science

While more efficient use of energy is increasingly vital to the development of the modern industrialized world, emerging visualization tools and approaches of telling data stories provide an opportunity for the exploration of a wide range of topics related to energy consumption and conservation (Olsen, 2017). Telling energy stories using data visualization has generated great interest among journalists, designers and scientific researchers; over time it has been proven to be effective to provide knowledge and insights (Holmes, 2007). This thesis proposes a new angle of tackling the challenge of designing visualization experience for building energy data, which aims to invite the users to think besides the established data narratives, augment the knowledge and insight of energy-related issues, and potentially trigger ecological responsible behaviors, by investigating and evaluating the efficacy of the existing interactive energy data visualization projects, and experimenting with user-centric interactive interface and unusual visual expressions though the development of a data visualization prototype.

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ITC/USA 2015 Conference Proceedings / The Fifty-First Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2015 / Bally's Hotel & Convention Center, Las Vegas, NV / Telemetry data from flight tests are normally plotted using MatLab™ or other third party software. For most of the trajectory and flight parameters, a static 2D or 3D line graph does not provide the proper data visualization that can be accomplished with 3D software. Commercial 3D software can be expensive and difficult to customize, and writing custom software using Direct3D or OpenGL can be complex and time consuming. These problems were overcome using C# and Windows Presentation Foundation (WPF) to quickly and easily create a 3D Telemetry Theater and Trajectory Visualization Tool to dynamically display actual and simulated flight tests.