A “Merry Christmas” makeover to end the year with*, looking at a Statista graph of Christmas tree sales in the US from 2004 to 2016. The original is a nice simple bar chart, which clearly shows the breakdown between sales of real and fake Christmas trees in America. It’s a little hard to see the actual proportions and that’s one thing I wanted to hit in my makeover.

A number of the community’s submissions took the angle that fake trees are re-usable, and so whilst less are sold than real trees, the actual number out there in people’s homes and business might be higher. We have a real tree that is still re-usable in our house. I bought it nearly ten years ago as a small potted fir. It lives in the garden most of the time and then comes in every other Christmas or so. Why not every Christmas? Well I absolutely love the tree, it’s convenience and the re-usability. But my partner occasionally wins the debate and we get a larger cut tree. I guess there is a place for, and joy to be shared from, all sorts of tree’s at Christmas … not so different from other aspects of life really.

Anyway below is my makeover. As ever it is available on Tableau Public too here.

* Post script: why did I asterisk ending the year with this viz? Well I set myself a goal to complete all 52 this year but am 1 short on 51. Darn it, so close! Hopefully I’ll squeeze one more makeover in and hit my goal. Well done to those that have done all 52 – a massive effort. And of course a huge thanks and shout out to the organisers – Andy and Eva. 2018 Makeover Monday looks to be even more exciting. If you’re considering joining, or maybe wavering, then my simple advice is to get amongst it! The community is really supportive and if you heed the advice and pointers that you’ll hear, then you’ll likely see some real growth and learn heaps. On top of that if you participate most weeks then you’ll build up a great portfolio of visualisations.

Happy new year everyone!

Cruising towards the end of a year of weekly makeovers with a look at over 176 million daily maximum and minimum temperature readings from around the world, over three centuries. As noted by many others, this weeks original visualisation is a tough act to follow – why try to make it better? Well I didn’t! I spent all of my time digging around what was a fascinating data set. In the end my “makeover” is simply a look at how anomalies can just be down to the fact that locations for temperature readings / estimates are introduced over time. The seeming false start for Senegal being a good case perhaps! Equally when temperatures from Antarctica were introduced is it surprising that we see the minimum temperature for the year drop dramatically? What about the impact of elevation of weather stations – over time readings are being taken from more extreme locations and I didn’t even get into looking at that!

The interactive version is available here.

This week we take a look at barrier free buildings in Singapore. The original visualisation is part of a site by the Building and Construction Authority in Singapore (BCA). Although the site requires Flash to be available and enabled in your browser, there is a great range of information available if you do have Flash. From the map of an area you can drill into information about individual buildings. We didn’t have quite the data to do that (not having the building ID or the depth of information about each building). Nevertheless is was interesting to attempt to makeover the map to: cover more areas and be a little easier on the eye. Most of the info is available via hover over, but I’ve also added an inset bar chart to show how the selected area compares to the “best” and “worst”.

The interactive version is available here.

Another double header post! Brief notes on this weeks makeover and last weeks.

Week 48

Last weeks data asked what if the world was made up of just 100 people. The original is visually interesting but hard to read as all categories are combined into one overall circle. A problem with percentages is a lack of perspective of just how many people are affected by something like starvation or malnutrition. There were some great examples from the community showing the actual number of people involved in the real world. I wanted to take this a step further and provide access to a story about just one person. The power of a story over a statistic is really interesting me at the moment as a result of a human centred design project I’m involved in.

The viz is also available on Tableau Public here.

Week 49

We’re also celebrating week 100 overall for #MakeoverMonday. Wow! Well done to the organisers. For week 49 the data looked at price variation across JD Wetherspoon pubs in the UK. For my redo I wanted to look at price distributions. The data set wasn’t quite setup  for that so I’ve pulled in an additional scaffolding data set which gave me the full range of one pound price brackets. I made the axis ranges continuous rather than discrete so that the gaps are filled in. This allow us to compare the different components of the meal, but it annoys me that the grid lines are down the middle of the bars. Are there better ways to do this? Please do post back against the tweet if you think so!

This viz is also available on Tableau Public here.