Best of the viz

This time I thought I’d put together a collection of interesting analytic visualisations created by others, with a broad range of applications from dating to transport! Hopefully you’ll find something to interest you too 🙂

My current Top 5 :

  1. Transport + house prices: it’s long been obvious that real estate prices are hugely linked to transport networks; however what surprised me about this heatmap was just how close to the station you need to be in order to really see the value uplift (tiny radius of bubbles at stations further out!). Being more independent of transport routes (cycling?) seems to be a great way to benefit from Londoners’ laziness!


Extract from Chris Li

Also related to this theme, has developed a price chart per tube stop on each of the lines (this one is for the Central line), alongside functionality that allows you to find the best neighbourhood from which to commute to your workplace, taking into account the time and cost of your commute as well as rents. I’ve been wishing for a while for this kind of analysis to be incorporated in property searches!

Central line

Image reproduced from Find Properly’s “Cool Things”

2. d3 to visually explain tricky concepts: d3 is an awesome interactive visualisation tool. This article shows off many of its animated features, to aid understanding of the topic of Machine Learning (interesting in itself too!). I have included a sadly-static excerpt, the animated version should be viewed on their website to do it justice!


Extract from Stephanie & Tony’s post on

Other topics are explored in “Explained Visually”: an experiment in making hard ideas more intuitive, again using d3. Ever wondered what conditional probability “looks” like in action? Victor Powell & Lewis Lehe bring this & other mathematical topics to life:

  1. Dating: Christian Rudder, the author of “Dataclysm (Who We Are…when we think no-one’s looking)”, was a co-founder of OkCupid. He graduated from Harvard with a degree in maths and applied this to the data that rapidly accumulated through his dating website. The book is full of his observations of online dating; the most memorable insight for me was as follows. Despite men claiming on their profiles to be looking for a woman of similar age to them, they in fact click most frequently on the profiles of women aged 20-24, irrespective of the age of the man! The impact of this, as Rudder goes on to demonstrate, is that women’s dating chances drastically drop off as they age, compared to men’s.


Also on this topic, I like mathematician Hannah Fry’s work on dating strategy, with one conclusion of hers being that once you have passed the 37% point on your dating timeline, marry the first person you meet who is been better than all those that you’ve met previously! For more information: or

Hannah Fry

  1. Student migration interactive chart: in my role at Times Higher Education: World University Rankings, I came across this interactive chart that you can play with. The scaled arrows demonstrate which parts of the world students originate from or move to for university. I was mostly surprised by how few students go abroad to study from continental Europe – presumably because university education at home is that much cheaper, so the offering abroad has to be that much more appealing to make it worth their while:

Migration students

Extract from UNESCSO Institute for Statistics (link above)

  1. Social Cartograms: Dr Ben Hennig is a senior research fellow at University of Oxford, working on visualising the social dimensions of our planet. The following cartogram from his website shows the current situation in Europe using Eurostat’s latest statistics about the number of asylum applicants in each country, with each country resized according to the absolute number of asylum applications in that country from January to June 2015:


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