EPJ Data Sci. (2017) 6: 16
https://doi.org/10.1140/epjds/s13688-017-0113-9

Regular article

Classification of Westminster Parliamentary constituencies using e-petition data

¹ Consumer Data Research Centre, Leeds Institute for Data Analytics and School of Geography, University of Leeds, Leeds, LS12 9JT, UK
² School of Geography and Leeds Institute for Data Analytics, University of Leeds, Leeds, LS12 9JT, UK
³ Leeds Institute for Data Analytics and School of Medicine, University of Leeds, Leeds, LS12 9JT, UK

^* e-mail: tra6sdc@leeds.ac.uk

Received: 5 April 2017
Accepted: 20 July 2017
Published online: 8 August 2017

Abstract

In a representative democracy it is important that politicians have knowledge of the desires, aspirations and concerns of their constituents. Opportunities to gauge these opinions are however limited and, in the era of novel data, thoughts turn to what alternative, secondary, data sources may be available to keep politicians informed about local concerns. One such source of data are signatories to electronic petitions (e-petitions). Such e-petitions have risen greatly in popularity over the past decade and allow members of the public to initiate and sign an e-petition online, with popular e-petitions resulting in media attention, a response from the government or ultimately a debate in parliament. These data are thus novel in their availability and have not yet been widely used for research purposes. In this article we will use the e-petition data to show how semantic classes of Westminster Parliamentary constituencies, fitted as Gaussian finite mixture models via EM algorithm, can be used to typify constituencies. We identify four classes: Domestic Liberals; International Liberals; Nostalgic Brits and Rural Concerns, and illustrate how they map onto electoral results. The findings and the utility of this approach to incorporate new e-petitions and adapt to changes in electoral geography are discussed.