Analyzing student travel patterns with augmented data visualizations

Visualization and visual analytics tools can provide critical support for experts and stakeholders to understand transportation flows and related human activities. Correlating and representing quantitative data with data from human actors can provide explanations for patterns and anomalies. We conducted research to compare and contrast the capabilities of several tools available for visualization and decision support as a part of an integrated urban informatics and visualization research project that develops tools for transportation planning and decision making. For this research we used the data collected by the StudentMoveTO (Toronto) survey which was conducted in the fall of 2015 by Toronto's four universities with the goal of collecting detailed data to understand travel behaviour and its effect on the daily routines of the students. This paper discusses the usefulness of new software which can allow designers to build meaningful narratives integrating 3D representations to assist in Geo-spatial analysis of the data

Data Analytics Helps Business Decision Making

While business analytics increases its use in gaining data driven insights in supporting business decision making, there has been little research done concerning some of the mechanisms that business analytics uses in improving decision making. Drawing on contingency theory and information processing views, this paper analyzes data analytics, linking IBM Watson Analytics to organizations such as an analytics analyzing airline survey, as well as how data analytics helps in decision making. The purpose of this study is, therefore, to examine the data analytics in decision making. This study examines the history of data analytics and the significance of data analytics while reviewing the traditional business intelligence solutions. Additionally, this study provides a statement of problems demonstrating the features and capabilities of IBM Watson analytics; business components, including the benefits offered by business analytics and cost involved; technology components and IBM Watson Analytics demonstration; and results. IBM Watson results show that it intelligently connects, analyzes, and secures data, hence, improving decision making, as well as customer service. Therefore, the key results and findings show that business analytics positively impact the capability of information processing, which in turn, positively influences decision making. This study’s results supports literature from business analytics through inclusion of useful insights into applications, features, and capabilities of IBM Watson Analytics and assistance of data driven decision-making while comparing IBM Watson Analytics with other tools in decision-making

Digging Into the Enlightenment: Mapping the Republic of Letters

The Digging Into the Enlightenment: Mapping The Republic of Letters project is a collaborative effort between humanities scholars and computer scientists at Stanford University and the University of Oklahoma in the United States, and at the University of Oxford in the United Kingdom. Our research hypothesis is that we can revolutionize the practice of interpretive research in the humanities by integrating innovative visualization and annotation techniques into highly interactive tools for excavating and dissecting details about people, places, times, and relationships in large data sets. Our project focuses on the Electronic Enlightenment (EE), a University of Oxford collection currently containing more than 53,000 letters. The goal of the project is thus to develop new visualization techniques and tools that support research into the "Republic of Letters" by facilitating interpretation of the complex data sets that have been materialized from this predominantly textual archival collection

Spatial Interaction for Immersive Mixed-Reality Visualizations

Growing amounts of data, both in personal and professional settings, have caused an increased interest in data visualization and visual analytics. Especially for inherently three-dimensional data, immersive technologies such as virtual and augmented reality and advanced, natural interaction techniques have been shown to facilitate data analysis. Furthermore, in such use cases, the physical environment often plays an important role, both by directly influencing the data and by serving as context for the analysis. Therefore, there has been a trend to bring data visualization into new, immersive environments and to make use of the physical surroundings, leading to a surge in mixed-reality visualization research. One of the resulting challenges, however, is the design of user interaction for these often complex systems. In my thesis, I address this challenge by investigating interaction for immersive mixed-reality visualizations regarding three core research questions: 1) What are promising types of immersive mixed-reality visualizations, and how can advanced interaction concepts be applied to them? 2) How does spatial interaction benefit these visualizations and how should such interactions be designed? 3) How can spatial interaction in these immersive environments be analyzed and evaluated? To address the first question, I examine how various visualizations such as 3D node-link diagrams and volume visualizations can be adapted for immersive mixed-reality settings and how they stand to benefit from advanced interaction concepts. For the second question, I study how spatial interaction in particular can help to explore data in mixed reality. There, I look into spatial device interaction in comparison to touch input, the use of additional mobile devices as input controllers, and the potential of transparent interaction panels. Finally, to address the third question, I present my research on how user interaction in immersive mixed-reality environments can be analyzed directly in the original, real-world locations, and how this can provide new insights. Overall, with my research, I contribute interaction and visualization concepts, software prototypes, and findings from several user studies on how spatial interaction techniques can support the exploration of immersive mixed-reality visualizations.Zunehmende Datenmengen, sowohl im privaten als auch im beruflichen Umfeld, führen zu einem zunehmenden Interesse an Datenvisualisierung und visueller Analyse. Insbesondere bei inhärent dreidimensionalen Daten haben sich immersive Technologien wie Virtual und Augmented Reality sowie moderne, natürliche Interaktionstechniken als hilfreich für die Datenanalyse erwiesen. Darüber hinaus spielt in solchen Anwendungsfällen die physische Umgebung oft eine wichtige Rolle, da sie sowohl die Daten direkt beeinflusst als auch als Kontext für die Analyse dient. Daher gibt es einen Trend, die Datenvisualisierung in neue, immersive Umgebungen zu bringen und die physische Umgebung zu nutzen, was zu einem Anstieg der Forschung im Bereich Mixed-Reality-Visualisierung geführt hat. Eine der daraus resultierenden Herausforderungen ist jedoch die Gestaltung der Benutzerinteraktion für diese oft komplexen Systeme. In meiner Dissertation beschäftige ich mich mit dieser Herausforderung, indem ich die Interaktion für immersive Mixed-Reality-Visualisierungen im Hinblick auf drei zentrale Forschungsfragen untersuche: 1) Was sind vielversprechende Arten von immersiven Mixed-Reality-Visualisierungen, und wie können fortschrittliche Interaktionskonzepte auf sie angewendet werden? 2) Wie profitieren diese Visualisierungen von räumlicher Interaktion und wie sollten solche Interaktionen gestaltet werden? 3) Wie kann räumliche Interaktion in diesen immersiven Umgebungen analysiert und ausgewertet werden? Um die erste Frage zu beantworten, untersuche ich, wie verschiedene Visualisierungen wie 3D-Node-Link-Diagramme oder Volumenvisualisierungen für immersive Mixed-Reality-Umgebungen angepasst werden können und wie sie von fortgeschrittenen Interaktionskonzepten profitieren. Für die zweite Frage untersuche ich, wie insbesondere die räumliche Interaktion bei der Exploration von Daten in Mixed Reality helfen kann. Dabei betrachte ich die Interaktion mit räumlichen Geräten im Vergleich zur Touch-Eingabe, die Verwendung zusätzlicher mobiler Geräte als Controller und das Potenzial transparenter Interaktionspanels. Um die dritte Frage zu beantworten, stelle ich schließlich meine Forschung darüber vor, wie Benutzerinteraktion in immersiver Mixed-Reality direkt in der realen Umgebung analysiert werden kann und wie dies neue Erkenntnisse liefern kann. Insgesamt trage ich mit meiner Forschung durch Interaktions- und Visualisierungskonzepte, Software-Prototypen und Ergebnisse aus mehreren Nutzerstudien zu der Frage bei, wie räumliche Interaktionstechniken die Erkundung von immersiven Mixed-Reality-Visualisierungen unterstützen können

Data visualization of virtual reality library user data

Abstract. User research is an important part of developing software. In the gaming industry, different ways to analyse user behaviour is an increasingly important part of research. However, as game analytics are relatively new to the game industry, there is limited amount of research available. In this work, we discuss how to visualise collected data in virtual reality environments in a meaningful way to improve product quality and extract user behaviour patterns. We use clustering algorithms and analytical functions to have a more comprehensive look on test participants’ behaviour with our Data Visualization tool. This behaviour is then presented using different path maps, heat maps and data charts. Originally our aim was to conclude research on user behaviour in the Oulu Virtual Library application, but due to the COVID-19 pandemic, we had to change our focus from user research to designing and implementing a tool for researchers to analyse similar data sets as our example data. Even though we had no concrete user data, researchers can use the tool we developed with relative small modifications, when dealing with similar data cases in the future. Usability improvements and real-word experiences are still needed to make the tool more robust.Tiivistelmä. Käyttäjätutkimus on tärkeä osa ohjelmistokehitystä. Koska pelianalytiikka on suhteellisen uutta peliteollisuudessa ja saatavilla oleva tutkimus vähäistä, loppukäyttäjien toiminnan analysointi on yhä tärkeämpi osa peliteollisuuden kehitystä. Tässä tutkielmassa pohditaan, kuinka virtuaaliympäristöistä kerättyä dataa voidaan esittää, merkityksellisellä tavalla, tuotteiden kehittämiseksi ja käyttäjien erilaisten käyttäytymismallien tunnistamiseksi. Käytämme ryhmittelyalgoritmeja ja analyyttisia funktioita, jotta saamme esitettyä käyttäjien toimintaa datavisualisointityökaluamme hyödyntämällä. Käyttäjien toiminta esitetään erilaisten polku- ja lämpökarttojen sekä datakaavioiden avulla. Alkuperäisenä tarkoituksenamme oli tutkia käyttäjien toimintaa Oulun Virtuaalikirjasto-sovelluksessa, mutta COVID-19-pandemian takia jouduimme siirtämään painopisteen käyttäjätutkimuksesta tutkijoille suunnatun datavisualisointityökalun suunnitteluun ja kehitykseen. Vaikka emme saaneet konkreettista aineistoa, tutkijat voivat käyttää työkalua, suhteellisen pienillä muunnoksilla, esimerkkiaineistoa vastaavan aineiston käsitelyyn ja analysointiin tulevaisuudessa. Työkalu tarvitsee yhä käytettävyysparannuksia ja todellisia käyttökokemuksia työkalun käyttövarmuuden parantamiseksi