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.
One of the things that seems to distinguish ‘classical’ from ‘popular’ music is the fact that the same classical composers and works can remain at the top for very long periods of time – decades, even centuries – whereas popular music songs and artists can reach the top of the charts, sell millions of records, and disappear within a matter of months. But is this difference real?
The value of statistical techniques in historical musicology depends on the quality of the available data. The extent and diversity of these sources is considerable, but it is important to remember that they can only ever illuminate a small proportion of the musical world.
A historical musical dataset can be thought of as a snapshot of part of the entirety of musical activity. Although we may be tempted to extrapolate our conclusions beyond the scope of the data, there are fundamental reasons why such extrapolations can only ever be valid within narrow limits. Continue reading →
Franz Pazdírek was a Viennese music publisher who, in the first decade of the twentieth century, compiled a ‘Universal Handbook of Music Literature’ – a composite catalogue of all sheet music then in print, worldwide. This ambitious undertaking (which, perhaps not surprisingly, was never repeated) was published over six years, and resulted in nineteen 600-page volumes listing music publications by 1,400 publishers covering every continent except Antarctica. Continue reading →