Nem-standard adatábrázolási módszerek a statisztikai alapképzésben = Non-Standard Data Visualization Methods in Undergraduate Statistics Education

Az információs társadalomban az adatvizualizáció egyre fontosabbá válik. A hagyományos oszlop-, kör-, és vonaldiagrammok mellett egyre nagyobb szerepet kapnak az egyéb grafikus ábrázolási módszerek. Ennek ellenére a hagyományos statisztika tantervek általában alig, vagy egyáltalán nem érintik ezt a területet. Az előadásban példákat adunk az oktatásban használható interaktív, illetve nem- standard grafikonokra, és ezek használatára a bevezető statisztikai képzés során

Empirically measuring soft knowledge in visualization

In this paper, we present an empirical study designed to evaluate the hypothesis that humans’ soft knowledge can enhance the cost-benefit ratio of a visualization process by reducing the potential distortion. In particular, we focused on the impact of three classes of soft knowledge: (i) knowledge about application contexts, (ii) knowledge about the patterns to be observed (i.e., in relation to visualization task), and (iii) knowledge about statistical measures. We mapped these classes into three control variables, and used real-world time series data to construct stimuli. The results of the study confirmed the positive contribution of each class of knowledge towards the reduction of the potential distortion, while the knowledge about the patterns prevents distortion more effectively than the other two classes

The Design of Interactive Visualizations and Analytics for Public Health Data

Public health data plays a critical role in ensuring the health of the populace. Professionals use data as they engage in efforts to improve and protect the health of communities. For the public, data influences their ability to make health-related decisions. Health literacy, which is the ability of an individual to access, understand, and apply health data, is a key determinant of health. At present, people seeking to use public health data are confronted with a myriad of challenges some of which relate to the nature and structure of the data. Interactive visualizations are a category of computational tools that can support individuals as they seek to use public health data. With interactive visualizations, individuals can access underlying data, change how data is represented, manipulate various visual elements, and in certain tools control and perform analytic tasks. That being said, currently, in public health, simple visualizations, which fail to effectively support the exploration of large sets of data, are predominantly used. The goal of this dissertation is to demonstrate the benefit of sophisticated interactive visualizations and analytics. As improperly designed visualizations can negatively impact users’ discourse with data, there is a need for frameworks to help designers think systematically about design issues. Furthermore, there is a need to demonstrate how such frameworks can be utilized. This dissertation includes a process by which designers can create health visualizations. Using this process, five novel visualizations were designed to facilitate making sense of public health data. Three studies were conducted with the visualizations. The first study explores how computational models can be used to make sense of the discourse of health on a social media platform. The second study investigates the use of instructional materials to improve visualization literacy. Visualization literacy is important because even when visualizations are designed properly, there still exists a gap between how a tool works and users’ perceptions of how the tool should work. The last study examines the efficacy of visualizations to improve health literacy. Overall then, this dissertation provides designers with a deeper understanding of how to systematically design health visualizations