A discussion on The Visual Display of Quantitative Information

When quantifying data (with the intent of displaying it) there are many things that one should consider. First and foremost It is important to know the data set you are working with as well as how that data can be properly and efficiently visualized for the sake of gaining information. The Visual Display of Quantative Information by Edward R. Tufte details some of these topics in his discussion of the matter.

Initially he starts off by stating some general rules about Data Visualization (DV). It is simply not enough to show the data, showing the data meets the bare minimum of what is required for the effective use of displaying the information. Yes you need to show the data but it is more the question of how you go about doing so that is significant. First of all, only the relevant data should be consider and any outlying or extraneous variables should be discarded lest the visualization becomes skewed. Secondly you should display the data in many layers and from many angles. This ensures that you're viewer may come to understand the data fully and completely. The dataset should be well described as well to eliminate any questions or confusion.

Tufte then goes on to talk about the graphical essence of how the data might be displayed. When considering something like DV there are numerous methods to achieve a relatable aesthetic. Visualization such as graphs and charts are sometimes too traditional. Tufte suggests something called data maps and he believes that quantifying datasets (especially large ones that may otherwise seem confusing) can be elegantly represented. He exemplifies this through the map of the United States' counties which details the mortality rate due to cancer/other diseases.

Another method for representing rather complex datasets is (what he calls) through a time series. This enables the viewer to recognize patters or trends within the dataset. Similarly time series' are also very effective in establishing change. This is perhaps most notable in the graph depicting the weather in New York in 1980. another example would be the train schedule in Paris in 1880 and changes that occurred to the map nearly 100 years later with the addition of a new train line. When put into context with a visual image this becomes even more effective. This is expounded upon through the many examples of how animals and humans move kinetically.

Lastly, some of the most effective graphs are what through the narrative graphics of space and time. These combine some of the most important aspects of DV. Utilizing space, time as well as visual representing, and linking them all together in a consists data form allows for a simple intake of the information presented. This creates little confusion, is somewhat simplistic but at the same time it does not hinder the graphs relevance. An example of this in his chapter is in the Man and Insects example.

It is clear that there are many ways of choosing how to represent a dataset. In part, your dataset may determine what sort of DV you use, however as much as the display of information is necessary it is important that you not loose sight of the message that is trying to be conveyed.