Makeover Monday: Scottish Index of Multiple Deprivation

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:

Scottish Index of Multiple Deprivation by 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.


Makeover Monday 2016 Week 44

Click to view interactive viz

Makeover Monday: U.S. National Debt

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:

Canadian Federal Election Results

It’s been one year since Justin Trudeau and the Liberal Party of Canada swept into power. Here’s the viz I created last year to explore the results of the most recent Canadian federal elections:

Makeover Monday: Clinton vs. Trump U.S. Election Viz

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.

Daily Kos polling results tracker

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:

The History of the NHL

With the start of a new NHL season upon us, I thought I would visualize the historical performance of each NHL team. I used a format similar to my NHL Barcode Viz, except this time it’s not binary; rather, it charts Points Percentage above and below the .500 mark.

I took my inspiration from similar vizzes created by Chris Jones (MLB Franchise Performance) and Matt Chambers (The History of the NFL). I also took some pointers from Andy Kriebel in this blog post.

I have focused on seasons from 1967 to the present, i.e. the NHL’s “Expansion Era”, as only the Original Six teams were in existence prior to ’67. The franchises are ordered alphabetically within their current divisions, and with their current team names. Winning seasons are shown in the team’s colour, while losing seasons are in grey.