See UK uses data that has been sourced from data.gov.uk and processed
into Linked Data where necessary, but is also designed to be able to use
other sources where available. All the datasets are then enriched, by
calculating area totals from point data and inferring aggregate values
for regions that do not have explicit data values, and further
enriched by establishing linkage between the datasets.
These enriched datasets are available directly from the
and can be accessed using the links below.
The visualisation provides a view centred on a chosen region of
specified size, and most noticeably gives a “pie-chart” that shows the
viewer how that region compares with similar regions around it. It
is thus designed to focus on the information most relevant to the
user. Colour indicates the “worst” (red) and “best” (green) areas
from those shown. This pie-chart is shown in preference to simply
colouring the map itself, as a coloured map confuses the map
features with the data being visualised.
It also gives some context of the real geography
involved, so that a full picture is seen. The user can navigate by
looking and clicking on the pie-chart, or the map, and can thus move
around using whatever view they are taking of the data presentation. A
search by postcode functionality is also supported, aiding the user in
finding specific locations.
An important aspect of the visualisation is that cross-dataset
correlation can be achieved and presented in a natural fashion, as
the data can be viewed as normalised by population or area, in
addition to the raw values. The user can therefore see how regions
compare in terms of, for example, crime density by population or
area, rather than just knowing that their county has little crime,
and guessing this is because the county has a small population or
See UK has been produced as a collaborative activity between Seme4 Ltd.
and members of the EnAKTing project at the University of Southampton.
For further details please contact Hugh Glaser
or Ian Millard; feedback on this application is very