This is the fourth in a series detailing the projects I have worked on at UCL in the last academic year.
The HE Profiler is the last of the three “core” school-profiling map mashups that I have developed over the last year – this has been developed over the last few weeks and indeed was finished only today, my final project of the year.
It is designed to be used by university widening-participation administrators, as a graphical tool to discover and evaluate the schools to target for campaigns to encourage university application. To do this, it makes use of two metrics – the OAC demographics of pupils attending each school, and the POLAR score of their postcode – in simple terms a National Statistics demographic describing the likelihood that people from this postcode go to university.
Again it is powered by OpenLayers, displaying point-based vector information on top of Google Maps image tiles, using NPE data for geocoding postcodes. The most interesting thing about this application is I’ve started to explore the very powerful rule/attribute based symbolisation for points available in OpenLayers. This sort of symbolisation will be, I expect, very useful in my next year’s project. I am very impressed with what can be done – some quite GIS-like properties present in a popular and freely available web application.
The graphic above shows target schools for a central-London university, based on the proportion of POLAR1/2 pupils (least likely to go to universities) compared with the rest. Schools with a majority of pupils in this category are coloured red. The area of each circle represents the number of such pupils present. The poor representation at university of the Thames Gateway region can be clearly seen. As an aside, the OAC demographic, not shown here, does not work well for London due to its size – the OAC is calibrated across the whole UK, and it is likely a more specific demographic analysis for London (e.g. LOAC) for schools there, would be more useful.