Professor Pollkatz Speaks

Here's some more insight and clarity from Professor Pollkatz who we mentioned in an earlier post.

Check out page 6 of his "Footnotes" PDF where he defends his "fair use" right to use Gallup polling data in creating some uniquely powerful graphics.

http://www.pollkatz.homestead.com/pollkatzfootnotes02july.pdf

I particularly resonated on the following (emphasis added):

My graphics are informative in a way that mere data is not. The information the graphics provide is priceless, illustrating truths about vital public issues that otherwise might go unobserved and displaying those truths for all to see. Any number of people whose eyes might glaze over at columns of numbers or bar charts can read my graphics and understand their significance. Among other things, this reduces the power of unscrupulous journalists and public figures to “lie with statistics.” The graphics are also put to use, by me and others, to offer commentary and criticism on issues of public importance.

...

My graphics, by setting poll results on the same axes so they can be compared and contrasted, illustrate many things that single graphs would leave obscure. Foremost, depicting all the facts together shows broad trends that the vagaries of sampling hide in a single series. Also, viewers of the graphics can follow the relative position of a single poll organization, and decide for themselves whether that pollster exhibits any consistent biases. Omitting any pollster’s data from the graphic would diminish these values, let alone omitting Gallup, the most prominent pollster of all. It would be akin to leaving some neighborhoods out of a regional telephone directory.

I have compiled the data into a database, myself, from public sources. Converting the data to graphics has involved a considerable amount of programming skill (Microsoft Excel), and the Excel macros may themselves be copyrightable. People have written to me asking for help in doing similar graphics on other topics. By any standard, this qualifies my graphics as derivative works.

COMMENTARY:

For me the text "that otherwise might go unobserved" is the fundamental reason for looking for the best possible way to represent numeric time series data in some creative visual format so that all can see.

When done well, this transformation from eye glaze over data to potent pictures definitely has the power to reduce the ability of the unscrupulous among us from spinning stories that are contrary to the underlying facts.

When done brilliantly and creatively as Professor Pollkatz has done in this one small but important domain, it opens up the possibility of communication, collaboration, and greatly enhanced understanding of the factors that most affect our lives.

While moving in this direction may seem conceptually easy, the behind the scenes mechanics of acquiring the data and making it usable represent a serious impediment to our collective ability to transform the data into socially productive forms. This is exemplified with Professor Polkatz statement that "converting the data to graphics has involved a considerable amount of programming skill".

This is a key challenge not just for Professor Pollkatz' polling data but with time series data representing the most important factors in any domain of interest. There are an astronomical number of publicly available and vital data series. Many of these can be readily downloaded on the web.

For example, there are countless possibilities available from the Department of Labor, Department of Labor Statistics at: http://www.bls.gov/data/home.htm . And this web site makes all the data available and provides some powerful built-in capabilities for looking at individual time series. However, if you want to create a composite graphic that was not already designed into their reporting and graphing engine, you are on your own and the job is likely to be complex and time-consuming.

In subsequent posts, I will be addressing the specific question of the scarcity of our personal time and the complexity of the data. The goal will be to develop and refine a methodology and approach that makes the transformation from complex data to powerful time series graphics easier and easier.

If you want to change the world, build better tools.