2026 England Local Elections: Gender Analysis

I was really impressed by how easy this site is to use for digging into the 2026 election data. The colour coding makes it simple to spot gender balance at a glance, with blue for male and pink for female. The saturation level really helps show where the predictions are less certain, which is a smart touch. I found the export options very handy for compiling reports. It is great that the source code is on GitHub and the methodology is clearly explained. This feels like a genuinely useful tool for anyone tracking local government diversity this year.

While the data presentation is clean, the reliance on predicted gender for most candidates is a significant caveat. The site notes that most candidates had no gender recorded, so the percentages are calculated only among those with known or predicted genders. This means the gender balance figures in low-confidence areas are less reliable. Users should check the methodology page to understand how names were classified before drawing conclusions about representation.

The ability to drill down by party or region is very convenient. I liked that I could export the data in both Excel and PNG formats. The council drilldown feature allows for quick ward-by-ward checks without navigating away. It saves time when comparing incumbent retention rates across different areas.

The reliance on predicted gender data raises questions about accuracy, yet the methodology details remain insufficient for full verification.

The gender analysis for the 2026 England Local Elections is impressively detailed and transparent. I appreciate how the methodology clearly explains the use of the gender_guesser library alongside the ONS historical baby names dataset to handle names that have shifted in gender balance over time. The fact that most candidates had no recorded gender and that an AI model was used for unresolved names adds a layer of honesty to the data. The visual approach using hue for balance and saturation for confidence is intuitive. It is refreshing to see a volunteer-run project that provides such robust, exportable data for public scrutiny.