Best dataviz of 2016

I’m going to return to 2014’s approach of dividing best visualisation of data (dataviz!) from visualisation of methods (methodviz!).

In the first category, as soon as I saw Jill Pelto’s watercolour data paintings I was bowled over. Time series of environmental data are superimposed and form familiar but disturbing landscapes. I’m delighted to have a print of Landscape of Change hanging in the living room at Chateau Grant. Pelto studies glaciers and spends a lot of time on intrepid-sounding field trips, so she sees the effects of climate change first hand in a way that the rest of us don’t. There’s a NatGeo article on her work here.

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In the methodviz category, Fernanda Viegas, Martin Wattenberg, Shan Carter and Daniel Smilkov made a truly ground-breaking website for Google’s TensorFlow project (open source deep learning software). This shows you how artificial neural networks of the simple feedforward variety work, and allows you to mess about with their design to a certain extent. I was really impressed with how the hardest aspect to communicate — the emergence of non-linear functions of the inputs — is just simple, intuitive and obvious for users. I’m sure it will continue to help people learn about this super-trendy but apparently obscure method for years to come, and it would be great to have more pages like this for algorithmic analytical methods. You can watch them present it here.

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