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Critique examples
Good critiques contain the following elements:
- Overview -- What the visualization is about: a brief overview of the data, objective, and techniques of the visualization
- Explanation of data: What kinds of datasets are used in the visualization? Is it a time series, categorical, geospatial, network, or something else? How many dimensions does the visualization use? How about the credibility of the dataset?
- Explanation of visualization techniques: what kinds of visualization methods are used? Does it use histogram? or scatterplot?
- Effectiveness of the visualization: does the visualization achieve its objective well? Which methods do or do not work? Why? Are there better ways to visualize the same information?
- Integrity of the visualization: does it distort data or make use of perceptual biases to give wrong impression? are there any biases that can be corrected by employing other ways to visualize the data?
- Design: how well/badly is it designed? Why? Is it engaging? Can there be improvements?
This visualization closely examines how each industry is recovering (or failing) from the recent great recession, underlining the huge variation across industries. It breaks down the data (the number of jobs across industries) into industries and shows that the recovery is not equally distributed across industries, but there has been “a mixed recovery” (the title of the first visualization), where some industries have been growing a lot while others were suffering.
The dataset used in this visualization documents the number of jobs in each industry. It is assembled by Bureau of Labor Statistics and thus it is the official dataset about labor statistics. It consists of the time series of the number of jobs as well as the percentage changes, and average salary of a couple of hundred industries (according to the official category used in the government). The data spans from 2004 to mid-2014 (when the visualization was created). The creators categorized each time series into one of the several categories such as: “Recovered and grown”, “Recovered”, Has not recovered”, and so on.
The whole point of the visualization is breaking down one time series — the number of jobs (job growth) — into industry-wise time series.
Each time series is depicted using a line chart and “small multiples” technique is used to compare many small line charts. The chart is interactive, showing the actual number of jobs and salary at a given month. It shows the recession as a pink shade and color-codes the categories (e.g. recovered, has not recovered, etc.) Here, the choice of color coding (Red-Green) is unfortunate because R-G blindness is the most common type of color vision problems. Colorblind people will not be able to clearly discern the differences.
The coolest and most ingenious part of the visualization is the brilliant combination of scatterplot and line chart. They first put each industry into a point in the space of wages (low - high wages) and jobs (decreased - increased since recession), and then put a line chart in each place, instead of putting a dot for each industry. In doing so, the creators do a very good job at illustrating the “mixed recovery”. This chart displays a lot of information (several hundred time series + mean wage + job growth), but still it is possible to gain insights into the overall trends and outliers (since they stand out in their choice of two axes). They even added the interactive feature — if you mouse-over on one of the squiggly line, it shows the actual time series.
After showing the first “wow” plot with animation, it uses scroll-based visual storytelling technique (connecting the visualization to the position in the page. One can simple scroll through such visualizations) to explain and highlight certain thematic part of the space, such as “housing bust” and “digital revolution”.