The first article in this series looked at how to get the data on Eighteenth Century London Concerts into a more usable form, and the second geocoded the locations and classified the venues by type. In this third article, we will start the analysis of the data by looking in more detail at the distribution of concert venues.
In the previous article looking at Simon McVeigh’s “Calendar of London Concerts 1750-1800“, I considered how to organise the data to make it suitable for statistical analysis. In this second article, I will add to the dataset by identifying the exact locations of the concert venues.
This is the first in a series of articles looking at Simon McVeigh’s fascinating dataset “Calendar of London Concerts 1750-1800“. In this article I will describe the data and consider how it can be put into a form suitable for statistical analysis. A second article will look at finding the locations of the concert venues, and I will then move on to some analysis of the dataset.
Many British buildings are adorned with plaques, marking the birthplace or residence of a famous person, or the site of a significant event. Details of these plaques are available in an online database, and I thought it would be interesting to see how many of them have a musical connection.
I recently stumbled across this page on Wikipedia, listing music students and their teachers. This is an ideal dataset to explore as a network diagram, or “graph”, in which a set of points (or “nodes”) are connected by lines (or “edges”). Here, the nodes are individuals, and there is an edge between them if one taught the other.
“Desert Island Discs” is the UK’s longest running radio show, still going strong on BBC Radio 4 over 78 years after the first episode on the BBC Forces Programme in January 1942. Each episode, an interviewed “castaway” chooses the eight pieces of music they would take with them to a desert island. They also select their favourite of these tracks, plus a book and a luxury item.
In this previous post in the series, we used capitalisation to identify proper nouns (names, places, etc) in our dataset of song lyrics. Other parts of speech – verbs, adjectives, etc – are not so easy to identify, although software exists to do just that.