In my last blog post I gave a brief introduction to the linked data offered by Ordnance Survey. Furthermore, in that post I said ‘By linking to identifiers for places and postcodes in your data you can enrich the information you hold’. It is this point I want to expand on here.
Last year Talis, Iconomical and the data.gov.uk team got together to build a Research Funding Explorer. The idea behind this (as explained here) was to show how linked data techniques could bring real benefit when it comes to joining and analysing data from a number of different sources.
The original application allows a user to browse funded research projects by subject and organisation. Furthermore, the original work contained a geographical element which allowed you to see which projects were being funded in which region.
Following this link we can see which projects are being worked on by institutions in the South East of England, and how much funding they are receiving. However, it would arguably be useful if you could also view the geographic distribution of projects at a more fine grained level. This can be made possible by linking the data to Ordnance Survey linked data.
In the original data the postcode is recorded for each institution. The postcode is captured simply as text. However, replacing the postcode text with a postcode URI means your data suddenly has the potential to contain a lot more information. A simple step in a text editor will take you from the text “SO16 4GU” to the URI data.ordnancesurvey.co.uk/id/postcodeunit/SO164GU.
In doing so we can now ingest the linked data for that postcode.
Because the postcode linked data provides a look up between postcode and region, we can enrich our original data with knowledge about the ward, district and county (where applicable) for each institution. This means we can now analyse research funding by local authority area as well as European region.
Using the spatial relationships in the Ordnance Survey linked data means we can also start to do more complex analysis. For example, we could compare funding in one region with funding in its neighbouring region or use the containment relationships to aggregrate the information up to coarser grained geographies.
None of this could have been done with the original data, but is made possible simply by replacing some text with a URI.
Hopefully this simple example shows that using linked data techniques to bring two relatively simple datasets together can start to yield benefits. The braver readers amongst you can read about this in a bit more detail on my personal blog.
Photo by Reedster via Flickr.