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. A prior article, entitled the “cluster of six“, employed unsupervised machine learning to discover the underlying structure of voting data. We’ll use related techniques here to explore the voting history of the General Assembly, the only organ of the United Nations in which all 193 member states have equal representation. By dividing the voting history into two equal parts, which we’ll label as the “early years”…
Original Post: 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. Supervised machine learning makes predictions from labeled training data. The unsupervised flavour looks for hidden structure in “unlabeled” data, i.e. a classification or categorisation not included in the observations. Hierarchical clustering will identify a cluster of six MPs as the most “distant” from the wider party. The full methodology, including the code, is published here. This extended narrative confirms the suitability of the data for clustering; reviews…
Original Post: 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. Property transactions evaporated with the Financial Crisis. The Government “stamped” on the green shoots of recovery with penalising duties on moves. And the uncertainty surrounding Brexit hasn’t helped. Property development offers an alternative way to add space. Owners unable to sell would want to consider their options, engage consultants, and secure planning permission. So one could reasonably expect the data to reflect a delayed response. And that’s what we see when plotting sales versus planning…
Original Post: SW10 digs deep

Surprising stories hide in seemingly mundane data

Geospatial experimentation Recently I experimented with geospatial mapping techniques in R.  I looked at both static and interactive maps. Embedding the media into a WordPress blog would be simple enough with a static map. The latter would require (for me) a new technique to retain the interactivity inside a blog post. My web-site visitor log, combined with longitude and latitude data from MaxMind’s GeoLite2, offered a basis for analysis. Although less precise than the GeoIP2 database, this would be more than adequate for my purpose of getting to country and city level.  I settled on the Leaflet package for visualisation given the interactivity and pleasing choice of aesthetics. The results however were a little puzzling. Whiling away the hours in Kansas The concentration of page views in central London was of no immediate surprise as this was likely to be…
Original Post: Surprising stories hide in seemingly mundane data