This week’s #MakeoverMonday dataset presented a lot of options for visualization. The data is from the Scottish Index of Multiple Deprivation (SIMD), a national report that scores and ranks the relative “deprivation”, or poverty level, across Scotland. The population is divided into “datazones”, which are each associated with a local authority. Each datazone is evaluated on seven different aspects: employment, income, health, education, crime, housing, and geographic access to services. These scores are then combined into an overall deprivation index. The objective of the SIMD is to identify the areas in Scotland suffering from deprivation in multiple aspects.
In the 2012 SIMD report, the following “barcode chart” is used to present the level of deprivation in each local authority:
What works well?
- Using the bars allows a lot of information to be encoded into a compact graphic
- Dense clusters of bars make it easy to spot regions of concentrated deprivation
What could be improved?
- The local authorities are sorted alphabetically. It may be better to sort by level of deprivation.
- The datazones are plotted by ranking (1 to 6,505), but this does not allow for comparison based on deprivation score. Most likely, the level of deprivation is not linear along the ranking.
Here is my version, improving upon the original barcode chart. After playing around with circle marks, boxplots, and other forms of viz, I decided to keep it simple and make incremental improvements:
- Local authorities are sorted by Local Share of Deprivation. Those at the top have a higher percentage of their datazones among the 15% most deprived in Scotland.
- Bars are plotted according to the overall SIMD score, not the ranking. This makes the relative levels of deprivation more apparent.
Click to view interactive viz
This week’s #MakeoverMonday features a really simple dataset: the size of the U.S. national debt ($19.5 trillion) compared to that of the rest of the world combined ($39 trillion).
I aimed to maintain simplicity in my visualization, representing the debt in blocks of $500 billion:
This week’s #MakeoverMonday viz takes a look at polling results in each state leading up to the U.S. Presidential election. The original viz is this interactive “Votamatic” tool at Daily Kos.
This is honestly an awesome visualization. The filter allows you to view the results for each state individually. And the interactive chart lets you hover and view the polling numbers at any point during the race.
So, I decided to look at the data in a slightly different way.
Here’s my Tableau #ElectionViz:
Here’s my first attempt at a Makeover Monday viz.
This week’s featured graphic is from the Financial Times. It displays the results of a public transit satisfaction survey conducted across Europe. Respondents were asked to rate their satisfaction with public transit in their city.
So how can we improve this viz? Let’s find out.
Here’s my version:
What improvements did I make?
- Taking a hint from Zen Master Andy Kriebel, I centred the bars around a central axis, showing positive sentiment to the right and negative to the left. This makes is easier to judge the overall response.
- I used an orange-green colour palette, with positive responses in green and negative responses in orange.
- Instead of a traditional colour legend, I used colour-coded text labels along the top of the chart. I also aligned the city labels immediately beside the data bars. And I added data labels on the bars, instead of using a horizonal axis. The intent of these changes is to make the chart easier to read by more directly labeling the data.