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”

But can ravens forecast?

G-Cloud forecasting

Why forecast sales?

Humans have the magical ability to plan for future events, for future gain. It’s not quite a uniquely human trait. Because apparently ravens can match a 4-year-old.

An abundance of data, and some very nice R packages, make our ability to plan all the more powerful.

A couple of months ago we looked at sales from an historical perspective in Digital Marketplace. Six months later. In this post, we’ll use the sales data to March 31st to model a time-series forecast for the next two years. The techniques apply to any time series with characteristics of trend, seasonality or longer-term cycles. Continue reading “But can ravens forecast?”

An East-West less divided?

With tensions heightened recently at the United Nations, one might wonder whether we’ve drawn closer, or farther apart, over the decades since the UN was established in 1945.

We’ll see if we can garner a clue by performing cluster analysis on the General Assembly voting of five of the founding members. We’ll focus on the five permanent members of the Security Council. Then later on we can look at whether Security Council vetoes corroborate our findings. Continue reading “An East-West less divided?”

The “cluster of six”

Unsupervised machine learning

Hansard reports what’s said in the UK Parliament, sets out details of divisions, and records decisions taken during a sitting. The hansard R package provides functions to import its data.

Using the Hansard API (Application Programming Interface), we’ll apply unsupervised machine learning to analyze the voting patterns of 219 Labour Members of Parliament (MPs). We’ll consider all divisions (results of the votes) in the UK House of Commons since the 2017 general election. Continue reading “The “cluster of six””

SW10 digs deep

Responding to a weak property market

In December I looked at how recent events have shaped the property market in London SW10. If short-distance moves are off the table in the current climate, how are property owners responding? When sales are weak, are planning applications in the ascendency? I applied data science techniques to Royal Borough of Kensington and Chelsea (RBKC) planning data to find out.

Continue reading “SW10 digs deep”