The Visual Display of Quantitative Information

Category: Mathematics
Author: Edward R. Tufte
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Comments

by hsitz   2017-09-26
I think any of the Edward Tufte books qualify, starting with _The Visual Display of Quantitative Information_:

https://www.amazon.com/Visual-Display-Quantitative-Informati...

by radicalbyte   2017-08-19
No, it's not common to see this kind of visualisation. Bad visualisations? Yeah, they're pretty common. The usual mistake is overuse of Pie Charts.

I'd recommend the OP (and anyone else who has an interest in communication) to read:

The Visual Display of Quantitative Information http://www.amazon.co.uk/The-Visual-Display-Quantitative-Info...

Information Dashboard Design http://www.amazon.co.uk/Information-Dashboard-Design-Effecti...

Now You See http://www.amazon.co.uk/Now-You-See-Stephen-Few/dp/097060198...

by kallus   2017-08-19
Edward Tufte was already mentioned and his books contains some really good examples

http://www.amazon.co.uk/Visual-Display-Quantitative-Informat...

I'd say, yes numerical is often just as good, but not always. Also, in my opinion, whether information is displayed numerically or visually, the key is to be as minimalistic as possible.

by as   2017-08-19
It all begins with Tufte.

http://www.amazon.com/Envisioning-Information-Edward-R-Tufte...

http://www.amazon.com/Visual-Display-Quantitative-Informatio...

by PaulHoule   2017-08-19
If you look at

https://www.amazon.com/Visual-Display-Quantitative-Informati...

you see that world class infographics require a "data artist", that is a lot of curation. You have to set the parameters of the chart quite carefully to produce a graphic which appears insightful. It is very easy to change those parameters a little and wind up in a bad place, where you might overload the tools with too much data, etc.

Data viz tools that are easy for a non "data artist" to use are an active area for CS research, new software products, etc.

Another problem is that data is less interesting than it seems to be at first. What action are you going to take on it? Data analysis delivers value when it informs actions.

The history of international development efforts is that aid agencies often have little understanding about conditions on the ground. For instance, they would send big tractors to Chile and truck them at great trouble and expense to get them into farming villages that could not use them, fuel them, fix them, etc.

Thus reducing a country down to a few numbers could cause the illusion that you know something when you really don't.

The numbers themselves are suspect. In a place like Rwanda, few people file tax returns, much of the economy is farmers selling corn to their neighbors, so national income numbers are often a wild-assed guess.

Also the life expectancy numbers are not based on a rigorous probability analysis, but rather a simple model that takes the death rates of 15 year old people in 2015 and 45 year old people in 2015 and treats that like a Markov chain where you are pretending that the 15 year old in 2015 is going to die at age 45 in 2045 at the same rate that that 45 year old people die in 2015, which is just not true -- particularly if you consider exceptional events such as war, famines, etc.

Thus those graphics are great for a talk, but they don't have the real depth of knowledge you'd need if you want to sell something to those people, plan a business, run an effective aid program, etc.