The visualization method is based on a interactive bubble chart. It is possible to arrange the bubbles as you wish. One goal was to emphasize the organic aspects in a conversation. There are hashtags with a short life span and in contrast highly used hashtags with a life span over the whole conversation about Big Data. In this way the appearence and disappearence of bubbles was animated smoothly and shows the active conversation over time.

See the visualization >

The challenge is that every cancer occurence must be analysed under different perspectives. Every occurence contains information about the age, gender, localization of the cancer (e.g. lung) and implication. Through this multidimensional view the visualization of cancer statistics  must cover these properties simultaneously. The choice was to visualize the cancer occurence along these properties to allow the user a discovery process. If you have a massive data set it es necessary to recognize patterns or intersting relationships. The flow visualization helps you to identify cancer occurence of specific groups and to see the value distribution along the other properties. Now it is easer to ask questions of relevance:

• How is the age structure by men with prostate cancer?
• What is the type of cancer with the highest number of incidences?
• Does exists gender specific types of cancer?

These questions can be answered with the provided structure of the visualization. Each vertical line represents one dimension (e.g. gender) and the total amount of cancer occurence by every property. Each flow shows the total amount along the displayed dimensions. If you click on one property it is possible to see detailed flowes with specific values. After the click the specific flows are highlighted to discover the needed relationships. This is done by the use of transparency and color.

At the end the provided visualization helps to recognize relationships through a multidimensional view. In the development process the use of color, transparency and form is determining the perception of the user.

The visualization was developed with D3.js based on the data by Eurostat, Gesundheitsberichterstattung des Bundes, WHO provided from visualizing.org.

The challenge was to show the range of references between the paragraphs and to figure out the overall complexity. In the development of the arc visualization the paragraphs follow a logical order. A total number of 2385 paragraphs should be visualized through columns and every arc represents a reference to another paragraph.  As a result the visualization shows that references are not only limited to the books instead the paragraphs are connected over the whole Civil Code. The most paragraphs which are referenced are §§206 and 207 with 16 counts. These two paragraphs are playing a fundamental role in the Civil Code from an abstract perspective. On the other side the most referencing paragraph is §1266 with 54 counts which defines the usage of §§ 1205 to 1257. From a structural overview the visualization shows in an asthetic way the beauty of the law with simple forms.

The internet radio Last.fm is a well-known platform to enjoy his favored music taste. Every user has the possibility to enrich music artists, albums and tracks with specified tags to create a folksonomy. These data provide an ideal challenge to visualize the music artists with their related tags. The dataset were used from a public source musicmachinery.com. The goal was to visualize the similarities and differences between two music artists based on their number of tags.

As a result you can see the mainstream and individual characteristics of the two artists. Both artists Britney Spears and Nelly Furtado are associated with the tag “seen live” in the same way. But some tags tend more to the right or left side and showing a stronger association to the related artist. Through the visualization it is easier to see the differences of the artists. In example the tag “dance” is more associated with Britney Spears than Nelly Furtado.