Seeing the wood for the trees

Visualising “bigger data”

In the blog post Criminal goings-on in a random forest, we used supervised machine learning to see how well we could predict crime in London. We began by rendering and exploring some of the many facets of the recorded crime summary data at London borough-level .

There comes a point though where the many faces of the data require something more than a static visualisation. And there are alternative options. We can make “bigger data” visualisations more consumable and engaging. In this post we’ll go deeper into the original data with a more interactive and flexible approach. Continue reading “Seeing the wood for the trees”

The plots thicken

Every story needs a good plot

One could think of data science as “art, grounded in facts”. It tells a story through visualisation. Both story and visualisation rely on a good plot. And an abundance of those has evolved over time. Many have their own dedicated Wikipedia page!

Which generate the most interest? How is the interest in each trending over time? Try this app to find out. Continue reading “The plots thicken”