Information visualization deals with the graphical representation of abstract data without spatial structure. Abstract data visualization uses visual metaphors and interaction to extract information from the data. Typical application scenarios are analyzing financial transactions or social networks, bioinformatics, geography, text analysis, or visualization of software source code. In this lecture, various techniques are presented for visualizing different data types. In particular, the following topics will be addressed:
The implemented algorithms are given below. We strongly recommend dragging control points and interacting with the graphics to understand the algorithm's purpose.
The algorithms have been implemented in JavaScript, with some components also developed within a Jupyter Notebook. As a result, the JavaScript code can run seamlessly in any conventional web browser, making it easily accessible with modern browsers. However, please be aware that certain algorithms depend on loading data from files, which may not function correctly if you open the page directly from your file system. To ensure optimal performance, we recommend setting up a local web server. I personally use the "Live Server" extension in Visual Studio Code, but you can also take advantage of Python’s built-in HTTP server for this purpose. Happy exploring!