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.
Highlights
Knowledge of an area’s characteristics is important in gaining an understanding of the needs of those who live in, work in or service the area
4 Results The implementation of Gaussian finite mixture models used here estimates a range of possible models using a combination of class configurations (See Figure and Table of Scrucca et al [ ]) and numbers of components/classes and selecting the combination that gives the highest Bayesian Information Criteria (BIC) goodness of fit
This shows that a very high proportion of the assignments are almost % certain and in Figure this certainty is mapped by Westminster Parliamentary constituencies (WPC), illustrating that there is little or no spatial clustering in this measure. The centres of these four classes are established and an index calculated that measures the level of support expressed by these centres relative to the mean level of support for that e-petition. This measures how important the e-petition is to the signatories who live in WPCs belonging to the class, an index of . indicates that this epetition is twice as likely to be signed by people in these WPCs than people in all WPCs
Summary
Knowledge of an area’s characteristics is important in gaining an understanding of the needs of those who live in, work in or service the area. The classification or geodemographic segmentation of areas allows for those areas that are similar in nature to be grouped together as identifiable classes. These classes are usually established by using multi-variate data to characterise an area and grouping together areas whose characteristics are broadly similar (Everitt et al [ ]). Classification can be applied at any level of geographic scale, from small neighbourhoods (Office for National Statistics [ ]; Gale et al [ ]) through to municipalities (Office for National Statistics [ ]) They can be designed for general use or bespoke for a particular
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