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”