LocalGov Elections

This module provides submodules, content types, views and configuration that allow the reporting of election results for the LocalGov Drupal distribution.

Features

• First past the post single seat per electoral area elections
• 'All-out' and not 'all-out' (e.g. halves or thirds) elections
• Table, graph and map based views of the results
• Extensible boundary source provider for electoral areas and geo data
• Social media posting of results (currently Twitter / X)

How to use

See the Documentation for more details

Installing

Installation

You can install this module with the following composer command.

composer require localgovdrupal/localgov_elections

Libraries

The libraries required by Charts/Highcharts are included, by default via CDN. If you wish to have these locally follow the instructions at https://www.drupal.org/docs/contributed-modules/charts/50x-getting-started#s-using-composer-and-wikimediacomposer-merge-plugin which uses the composer.json provided by the Charts module to install.

Submodules

You may also wish to use the submodules provided as part of the Localgov Election Reporting module. They are:

1. LocalGov Elections Reporting ONS Wards 2023 - Boundary source provider for Office of National Statistics 2023 Wards
2. Localgov Elections Parliamentary Constituency Provider - Boundary source provider for Office of National Statistics 2024 constituency boundaries
3. LocalGov Elections Reporting Social Post Integration - Post results to social media (Twitter/X)
4. LocalGov Elections Reporting Demo - Demo content to help with testing/evaluation
5. LocalGov Elections Reporting - UK Parties - Adds the majority of UK political parties to the party taxonomy.

Further details for these modules are in their own module READMEs and the Documentation.

Issues

If you run into issues using this module, please report them at https://github.com/localgovdrupal/localgov_elections/issues

Maintainers

This project is currently maintained by:

• Chris Wales https://github.com/chriswales95
• Duncan Davidson https://github.com/dedavidson

It is based on work originally done by Rob Carr https://github.com/rgcarr.