Following on from our work to define the scope, the first stage of actual hard graft has been to look back and test some of our initial hypotheses, for example, does a similar household replace older childless households when they either move into more suitable accommodation, or sadly, a lone occupant dies.
We have now put a little more detail around the outline model , in particular starting to consider the work required at each phase, including where specific experiments are required as well as starting to identify key data sources (and contacts) required for the work.
In what may turn out to be a rash and dreadfully overoptimistic move, we have decided to build a local population forecasting model for Bath and North East Somerset, here’s how we’re going about it:
What are we doing?
We want to build a model which forecasts the local (Bath and North East Somerset) population.
With an election coming up, thoughts turn to registering to vote; campaigns at all levels will start encouraging people to register and participate.
From an open data perspective, the we want to know what could be learnt about electoral registration from the data held locally or in national sources and could it be used to help increase registration levels?
A sub-set of that question was then ‘how can we understand which areas have higher or lower levels of registration in Bath and North East Somerset?’.
This post intends to show it is possible (and reasonably easy) for anyone to run a quick analysis to answer these questions, provided the local authority is willing and able to release some electoral registration statistics.
We were able to achieve this to a small geographical level and publish some findings reasonably easily and it should be easy for any local area to repeat this.
I was fortunate enough to get the opportunity recently to talk with friends at Bath:Hacked about local geographies. This was in part to advertise the upcoming Boundary Review of the area, but also a chance to reflect on the many ways we slice and dice the local authority area.
This was something of a semi-structured ramble through geographies against the ONS’ rather wonderful hierarchical representation of statistical geographies.
In practice, particularly when thinking about any locality, its geography is intertwined with its history, its natural and ecological setting, its psychogeography (if you’re into such things) and so forth. But those caveats notwithstanding, it was a fun exercise.
— Giuseppe Sollazzo (@puntofisso) March 2, 2017
was an interesting thought piece. Could we even identify them, let alone publish them? Here follows a very quick rapid reflection on it…
Previously I’ve managed to display a simple d3.js visualisation on the site through a couple of different methods. Next step is to go from proving a general concept to actually displaying some data that might be interesting.
The BBC recently published a piece on small area referendum voting data. The information was gathered, through Freedom of Information requests, from less than half the local authorities (acting as ‘designated counting areas’) who administered the referendum. It’s a rather problematic piece of work.
This analysis is riddled with errors; there could be evidence of illegal practice; there are clear opportunities to improve practice around election data and the implications of this release have relevance to other realms of government data.