Data Journalism Readings Week 7:

Time Series/Oversampling

The author makes the case for the use of ‘oversampling’ to standardize a subgroup of a particular set of data. This is used to correct for a bias or a higher variability in one group. You simply increase a number of instances for a variable and then weigh it down in proportion to what it is supposed to be with the overall group. This is a helpful too to use when representing data for analysis. While there are arguments to be made about how artificially setting irregular intervals to standardize a graph or representation can be misleading, if used correctly, it helps give us a more accurate representation of our data.

In the map example, the author gives different examples of how manipulating the classes can help illustrate different parts about the data. For e.g, in the population map of the US, he standardizes a large middle class for most of the numbers to fall in so that the map does a better job of highlighting extremes on the spectrum.

Wall Street Journal Style Guide

The first two chapters from the Wall Street Journal style guide to charts illustrate the dos and donts of data visualization.  It is a comprehensive guide telling us what we should keep in mind regarding each kind of chart, whether its a bar chart, a pie chart or a graph.

The guide walks us through four steps of creating a visualization, which is Research, Edit, Plot and Review. This is important to remember instead of plotting first and then having to go back and edit the numbers or the research. It also makes an important point that simply acquiring data is not enough. In itself we cannot make a large quantity of data work for us, it has to be made relevant, either by changing it’s value representations (e.g. changing numerical increase to percentage increase) or taking only a certain section of the data so that it is easier to digest.

The rest of the reading discusses things we should keep in mind, such as using the right units, starting base and choosing the right kind of chart for the corresponding data.

We are also given a guide to color schemes, what works and what stands out as garish or with too much contrast.


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