On Wednesday 17th Feb A few of us (@andymayer @aden_76 @Jagusti @saulcozens & co) got together in the showroom at the request of @Technophobia to discuss how sheffield city council could open up it’s data streams, what the priorities might be, and what economic benefit could be found.

During the course of this discussion we identified, not unexpectedly, that most interesting insights come at the intersection of multiple data sets. One idea that came to mind in this discussion was triggered by the example “Average age by postcode”. We then went on to think how this could be shown – one example was a temperature map – hotter = higher concentrations of the “Target age group”. I’d been re-reading stafford beer that day (Think before you think) and was reminded of the phrase “Only variety can absorb variety”. So, one interesting visualisation we came up with was trying to locate under-serviced regions for specific community groups. Specifically:

One slider increases the “Heat” level for one dimension of measurements. In this case, average age. A second slider indicates the provision of services targeted at that age group, EG “Home help provision”. As each slider moves up to its max, the “Cold” from the home help provision should “Balance out” the “Heat” from the average age index. If the map returns to a steady state, all is well, but if the map has blotchy red spots, maybe there are gaps in provision that would enable better targeting of services?

Not sure this example really stands up to harsh scrutiny, but I’d never heard of heat maps being used in quite this context before. Fun times.