Visualising the Location Graph – example with Gephi and Ordnance Survey linked data

This is arguably a simpler follow up to my previous blog post, and here I want to look at visualising Ordnance Survey linked data in Gephi. Now Gephi isn’t really a GIS, but it can be used to visualise the adjacency graph where regions are represented as nodes in a graph, and links represent adjacency relationships.

The approach here will be very similar to the approach in my previous blog. The main difference is that you will need to use the Ordnance Survey SPARQL endpoint and not the DBpedia one. So this time in the Gephi semantic web importer enter the following endpoint URL:

http://data.ordnancesurvey.co.uk/datasets/os-linked-data/apis/sparql

The Ordnance Survey endpoint returns turtle by default, and Gephi does not seem to like this. I wanted to force the output as XML. I figured this could be done in the using a ‘REST parameter name’ (output) with value equal to xml. This did not seem to work, so instead I had to do a bit of a hack. In the ‘query tag…’ box you will need to change the value from ‘query’ to ‘output=xml&query’. You should see something like this in the Semantic Web Importer now:

Now click on the query tab. If we want to, for example, view the adjacent graph for consistuencies we can enter the following query:

prefix gephi:<http://gephi.org/>
construct {
?s gephi:label ?label .
?s gephi:lat ?lat .
?s gephi:long ?long .
?s <http://data.ordnancesurvey.co.uk/ontology/spatialrelations/touches> ?o .}
where
{
?s a <http://data.ordnancesurvey.co.uk/ontology/admingeo/WestminsterConstituency> .
?o a <http://data.ordnancesurvey.co.uk/ontology/admingeo/WestminsterConstituency> .
?s <http://data.ordnancesurvey.co.uk/ontology/spatialrelations/touches> ?o .
?s <http://www.w3.org/2000/01/rdf-schema#label> ?label .
?s <http://www.w3.org/2003/01/geo/wgs84_pos#lat> ?lat .
?s <http://www.w3.org/2003/01/geo/wgs84_pos#long> ?long .
}

and click ‘run’. To visualise the output you will need to follow the exact same steps mentioned here (remember to recast the lat and long variables to decimal).

If we want to view adjacency of London Boroughs then we can do this with a similar query:

prefix gephi:<http://gephi.org/>
construct {
?s gephi:label ?label .
?s gephi:lat ?lat .
?s gephi:long ?long .
?s <http://data.ordnancesurvey.co.uk/ontology/spatialrelations/touches> ?o .}
where
{
?s a <http://data.ordnancesurvey.co.uk/ontology/admingeo/LondonBorough> .
?o a <http://data.ordnancesurvey.co.uk/ontology/admingeo/LondonBorough> .
?s <http://data.ordnancesurvey.co.uk/ontology/spatialrelations/touches> ?o .
?s <http://www.w3.org/2000/01/rdf-schema#label> ?label .
?s <http://www.w3.org/2003/01/geo/wgs84_pos#lat> ?lat .
?s <http://www.w3.org/2003/01/geo/wgs84_pos#long> ?long .
}

When visualising you might want to change the scale parameter to 10000.0. You should see something like this:

So far so good. Now imagine we want to bring in some other data – recall my previous blog post here. We can use SPARQL federation to bring in data from other endpoints. Suppose we would like to make the size of the node represent the ‘IMD rank‘ of each London Borough…we can do with by bringing in data from the Open Data Communities site:

prefix gephi:<http://gephi.org/>
construct {
?s gephi:label ?label .
?s gephi:lat ?lat .
?s gephi:long ?long .
?s gephi:imd-rank ?imdrank .
?s <http://data.ordnancesurvey.co.uk/ontology/spatialrelations/touches> ?o .}
where
{
?s a <http://data.ordnancesurvey.co.uk/ontology/admingeo/LondonBorough> .
?o a <http://data.ordnancesurvey.co.uk/ontology/admingeo/LondonBorough> .
?s <http://data.ordnancesurvey.co.uk/ontology/spatialrelations/touches> ?o .
?s <http://www.w3.org/2000/01/rdf-schema#label> ?label .
?s <http://www.w3.org/2003/01/geo/wgs84_pos#lat> ?lat .
?s <http://www.w3.org/2003/01/geo/wgs84_pos#long> ?long .
SERVICE <http://opendatacommunities.org/sparql> {
?x <http://purl.org/linked-data/sdmx/2009/dimension#refArea> ?s .
?x <http://opendatacommunities.org/def/IMD#IMD-score> ?imdrank . }
}

You will need to recast the imdrank as an integer for what follows (do this using the same approach used to recast the lat/long variables). You can now use Gephi to resize the nodes according to IMD rank. We do this using the ranking tab:

You should now see you London Boroughs re-sized according to their IMD rank:

turning the lights off and adding some labels we get:

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