Here’s an excerpt:

A New York City subway train holds 1,200 people. This blog was viewed about 5,700 times in 2011. If it were a NYC subway train, it would take about 5 trips to carry that many people.

Click here to see the complete report.

The advent of new standards and initiatives for data publication in the context of the World Wide Web (in particular the move to linked data formats) has resulted in the availability of rich sources of information about the changing economic, geographic and socio-cultural landscape of the United Kingdom, and many other countries around the world. In order to exploit the latent potential of these linked data assets, we need to provide access to tools and technologies that enable data consumers to easily select, filter, manipulate, visualize, transform and communicate data in ways that are suited to specific decision-making processes.In this project, we will enable organizations to press maximum value from the UK’s growing portfolio of linked data assets. In particular, we will develop a suite of software components that enables diverse organizations to rapidly assemble ‘goal-oriented’ linked data applications and data processing pipelines in order to enhance their awareness and understanding of the UK’s geographic, economic and socio-cultural landscape.A specific goal for the project will be to support comparative and multi-perspective region-based analysis of UK linked data assets (this refers to an ability to manipulate data with respect to various geographic region overlays), and as part of this activity we will incorporate the results of recent experimental efforts which seek to extend the kind of geo-centred regional overlays that can be used for both analytic and navigational purposes. The technical outcomes of this project will lead to significant improvements in our ability to exploit large-scale linked datasets for the purposes of strategic decision-making.RAGLD is a collaboative research initiative between the Ordnance Survey, Seme4 Ltd and the University of Southampton, and is funded in part by the Technology Strategy Board‘s “Harnessing Large and Diverse Sources of Data” programme. Commencing October 2011, the project runs for 18 months.

If you’d like to input into the requirements phase of the project I’d be very grateful if you could fill in one of these questionnaires. Many thanks in advance.

By way of a quick example you’ll see that the currently Ordnance Survey Linked Data is hosted in Kasabi here. There are a number of standard APIs provided with each dataset such as a SPARQL endpoint, search API etc. Kasabi provides an easy way to map complex SPARQL queries to simple API queries. For example, this API provides an easy way to do topological queries on the Ordnance Survey Linked Data. For example:

http://api.kasabi.com/dataset/ordnance-survey-linked-data/apis/33m?id=7000000000037256&spatialrelation=touches&apikey=yourkey

will find all regions that touch The City of Southampton.

This API gives you a list of all postcodes (and their lat/long) within a particular region. For example:

http://api.kasabi.com/api/ordnance-survey-postcode-region?district=7000000000037256&apikey=yourkey

will find all postcodes inThe City of Southampton. Additionally we can apply a style sheet to get back the same information as KML:

http://api.kasabi.com/api/ordnance-survey-postcode-region?district=7000000000037256&apikey=yourkey&output=kml

Kasabi also provides a handy place to store and host data, and one Sunday afternoon I decided to see how easy it would be to create a couple of hyperlocal datasets: one for Southampton and one for Hampshire. The basic approach in creating these hyperlocal datasets was to effectively ‘cookie cut’ region specific data from a number of different linked (open) data sets and put them into one store. So for Southampton and Hampshire we have a list of: airports, bus stops, stations, schools, GPs, hospitals, renewable energy generators, heritage sites, councillors, crime statistics, administrative regions and postcodes…   Each important element, like schools or hospitals, is linked to a postcode, district, ward—and in the case of Hampshire—county. The Ordnance Survey linked data is effectively acting as the clue between disparate sources of information. The fact that each of these datasets was provided as linked data, and furthermore referenced common identifiers for administrative regions and postcodes, meant it was very easy to bring them together in one store. Some sample queries are provided here. It gets more interesting when you combine elements from different datasets to, for example, ask questions like ‘find me GPs in my ward, and all the bus stops within a 100 metre radius of those GPs’.  If one were extra paranoid it would then be possible to extend the query to only find bus stops in areas of low anti-social crime levels. These queries are all well and good, or ‘nerdy, but nice’ as Andrew Stott put it.

What I was really hoping to do was build a nice webapp on top of this integrated data. Anyone who has seen my previous mash-ups will know that web design is not amongst my key skills, so Zach Beauvais suggested I put word out to the developer community to see if anyone fancied building on this data to make something cool and interesting like (for example) the (in)famous postcode paper. Any volunteers?

It appears meanwhile that Robert has also been building his own version.

Hopefully our efforts will see the start of some linked open data from CERN – I can dream I’ll write up this work when the ontology is complete. Meanwhile comments welcome (comments aside from “you need to get out more” that is).

[1] – best viewed in Protege 4.1

The Beeb have been in the linked data business for a while and their initial efforts were around programmes and music (but you also check out the great linked data powered wildlife finder).

Recently they’ve started to experiment with tagging their programmes with relevant people, places and organisations.

I decided it might be quite nice to have a simple mashup showing TV and radio shows about different places. To this end I did a quick linked data mashup to produce some KML showing this information.

To do this I again used Python’s rdflib. Here it was a simple case of following links from a place to a TV/radio programme and loading the RDF into a graph. It was then a case of executing a simple SPARQL query over this graph to get a KML file containing programme details and a lat/long coordinate for plotting it on a map. The BBC place data did not contain lat/long for all the places, but luckily they did include a ‘sameAs’ to the place information in DBpedia. Here all we had to do was follow the ‘sameAs’ link and load in the DBpedia data.

I explained how to use rdflib to do this sort of thing in my last post, but meanwhile here is the source code and here is the KML. The KML can be used with a mapping API of your choice, but for a quick view drop the KML URL into the search box on Google maps or view in Google Earth.

At the moment this is a bit clunky, but it’s just a start…

For this mashup I used Python 2.7 and rdflib 3.0.0.

First off you need to install rdflib. Full instructions on doing this can be found here. If you use easy_install you can install rdflib by typing:

easy_install -U "rdflib>=3.0.0"

You will also need to install rdfextras (see here). This can also be done using easy_install

easy_install rdfextras

You are now good to go. The next thing I needed was the BIS funding data. This can be downloaded here. The original BIS data gives location for various organisations via a URI based on the organisation’s postcode. For example:

<http://education.data.gov.uk/id/institution/UniversityOfWalesSwansea>
<http://research.data.gov.uk/def/project/location>
<http://education.data.gov.uk/id/institution/BabrahamBioscienceTechnolgiesLtd/SA28PP> .

I edited the data to point to URIs for postcodes in the Ordnance Survey linked data (note these weren’t available when the BIS data was created). Now we have:

<http://education.data.gov.uk/id/institution/UniversityOfWalesSwansea>
<http://research.data.gov.uk/def/project/location>
<http://data.ordnancesurvey.co.uk/id/postcodeunit/SA28PP> .

This triple basically states the location of the University of Wales in terms of its postcode.

So the edited RDF data now contains location information for research institutions in terms of a postcode URI, and it also contains information about the research projects worked on by those institutions and how much funding those projects received. Using rdflib it is very straight forward to load this data into Python and use it programmatically. Here’s how:

These first few lines load the necessary libraries and plugins:

import logging
import rdflib

# Configure how we want rdflib logger to log messages
_logger = logging.getLogger("rdflib")
_logger.setLevel(logging.DEBUG)_hdlr = logging.StreamHandler()
_hdlr.setFormatter(logging.Formatter('%(name)s %(levelname)s: %(message)s'))
_logger.addHandler(_hdlr)

from rdflib import Graph
from rdflib import URIRef, Literal, BNode, Namespace, ConjunctiveGraph
from rdflib import RDF
from rdflib import RDFS
rdflib.plugin.register('sparql', rdflib.query.Processor,'rdfextras.sparql.processor', 'Processor')
rdflib.plugin.register('sparql', rdflib.query.Result, 'rdfextras.sparql.query', 'SPARQLQueryResult')

we now create a Graph in which to store the RDF:

store = Graph()

the data can be easily loaded from the web or hard drive. In this case I have the files stored locally:

store.parse("file:/C:/Projects/RDFPythonPlay/data/businessdatagovuk.nt", format="nt")
store.parse("file:/C:/Projects/RDFPythonPlay/data/educationdatagovuk.nt", format="nt")
store.parse("file:/C:/Projects/RDFPythonPlay/data/patentsdatagovuk.nt", format="nt")
store.parse("file:/C:/Projects/RDFPythonPlay/data/researchdatagovuk.nt", format="nt")

Recall from here that I am interested in seeing which parties are funding in which local authority areas. The data as it stands will not let me do this. However, the OS postcode linked data provides information about the local authority areas that a postcode is contained in. All I now have to do is ‘follow my nose’ and load in the postcode data. I can do this by going through the triples containing links between organisations and postcodes via the location property. First I set up a few namespace bindings:

# Bind a few prefix, namespace pairs.
store.bind("PROJECT", "http://research.data.gov.uk/def/project/")
store.bind("FOAF", "http://xmlns.com/foaf/0.1/")

# Create a namespace object for the project and FOAF namespaces.
PROJECT = Namespace("http://research.data.gov.uk/def/project/")
FOAF = Namespace("http://xmlns.com/foaf/0.1/")

I can now iterate over the triples in the store and find those who subject is a type of foaf:Organization, and which contain the location property. An example of such a triple would be the one we had above:

<http://education.data.gov.uk/id/institution/UniversityOfWalesSwansea>
<http://research.data.gov.uk/def/project/location>
<http://data.ordnancesurvey.co.uk/id/postcodeunit/SA28PP> .

I can then lookup the data behind the postcode URI and load this into the store. This is all done by the following code:

# For each foaf:Organization in the store get the postcode

for organization in store.subjects(RDF.type, FOAF["Organization"]):
for postcode in store.objects(organization, PROJECT["location"]):
try:
print postcode
store.parse(postcode)
except:
print '404 not found'

Now the data in the store will contain a link from organisation to postocde, and a link from postcode to local authority area. We can now traverse the graph to find the link from organisation to local authority area. We can now use a simple SPARQL query to retrieve a list of projects giving the local authority areas the participating organisations are based in. The SPARQL query to do this is:

select distinct ?label ?districtlabel
where
{
?organisation <http://research.data.gov.uk/def/project/project> ?project .
?project <http://www.w3.org/2000/01/rdf-schema#label> ?label .
?organisation <http://research.data.gov.uk/def/project/location> ?x .
?x <http://data.ordnancesurvey.co.uk/ontology/postcode/district> ?district .
?district <http://www.w3.org/2000/01/rdf-schema#label> ?districtlabel . }

We can now add that into our Python code as follows and print out the query answers:


query = """select distinct ?label ?districtlabel \
where \
{\
?organisation <http://research.data.gov.uk/def/project/project> ?project .\
?project <http://www.w3.org/2000/01/rdf-schema#label> ?label . \
?organisation <http://research.data.gov.uk/def/project/location> ?x . \
?x <http://data.ordnancesurvey.co.uk/ontology/postcode/district> ?district . \
?district <http://www.w3.org/2000/01/rdf-schema#label> ?districtlabel . }"""


answers = store.query(query).serialize('python')

for (label,districtlabel) in answers:
print "%s was funded in %s" % (label,districtlabel)


To summarise, this post shows how you just need rdflib and Python to build a simple linked data mashup – no separate triplestore is required! RDF is loaded into a Graph. Triples in this Graph reference postcode URIs. These URIs are de-referenced and the RDF behind them is loaded into the Graph. We have now enhanced the data in the Graph with local authority area information. So as well as knowing the postcode of the organisations taking part in certain projects we now also know which local authority area they are in. Job done! We can now analyse funding data at the level of postcode, local authority area and (as an exercise for the ready) European region.

[Python note - WordPress keeps messing with my indentation and I'm too tired to fix. I hope that doesn't detract from your enjoyment of this blog post ]

The Blog-Health-o-Meter™ reads Wow.

Crunchy numbers

A Boeing 747-400 passenger jet can hold 416 passengers. This blog was viewed about 4,400 times in 2010. That’s about 11 full 747s.

In 2010, there were 4 new posts, growing the total archive of this blog to 19 posts.

The busiest day of the year was October 26th with 341 views. The most popular post that day was So what can I do with the new Ordnance Survey Linked Data?.

Where did they come from?

The top referring sites in 2010 were twitter.com, johngoodwin.me.uk, blog.ordnancesurvey.co.uk, Google Reader, and blogs.talis.com.

Some visitors came searching, mostly for john goodwin blog, foaf genealogy, genealogy rdf, semantic genealogy, and rdf genealogy.

Attractions in 2010

These are the posts and pages that got the most views in 2010.

So what can I do with the new Ordnance Survey Linked Data? October 2010
4 comments

Some quick linked data hacks June 2010
19 comments

/location /location /location – exploring Ordnance Survey Linked Data October 2009
4 comments

/location /location /location – exploring Ordnance Survey Linked Data – Part 2 October 2010
3 comments

Genealogy and Linked Data (revisited) November 2009
6 comments

A while ago Talis built their BIS Explorer. The aim of this application was to allow users to “identify centres of excellence at the click of a button” and more can be read about the application here. This data mash-up took different data sources about funded research projects and joined them together using linked data. In the original application you could, for example, look at funded research projects by European Region in Great Britain. This can be seen here. At the time this demo was created Ordnance Survey was yet to publish its postcode data as linked data, but if they had it would have been very easy to get a more fine grained view of research projects down at the county and district level. Here’s how…

The basic data model of the original BIS data was fairly straight forward. Universities and businesses have a link to the projects they worked on. For each university there is also postcode information. Things get interesting if instead of/as well as linking to a string representation of a postcode you link to the URI for said postcode. This can be done by using the property:

http://data.ordnancesurvey.co.uk/ontology/postcode/postcode

So say we wanted to do this for Imperial College. All we need is this (this example is in N-Triple format) in our data:

<http://education.data.gov.uk/id/institution/ImperialCollegeOfScienceTechnologyAndMedicine>

<http://data.ordnancesurvey.co.uk/ontology/postcode/postcode>

<http://data.ordnancesurvey.co.uk/id/postcodeunit/W68RF> .

Now, by the power of linked data, connecting to a resource for the postcode means we can now enrich our university dataset with knowledge of the ward, county and district the university is in. Also, given that the university is connected to a project we have a link from project to region. Through the link from project to university to postcode to region we can now start to have a more finely grained view on which areas are getting more funding.

So how do we do this in practice? There are the steps I followed.

1. Download the BIS data from here and load it into a triple store (linked data database) of your choice. There are plenty of good open source ones available e.g. Sesame or TDB to name two.
2. I then then added the links to postcode URIs as described above.
3. Following that I loaded the data for the postcodes in a similar manner to that described here. A relatively simple script retrieved the RDF for the relevant postcodes and loaded the RDF into my store. The nice thing about linked data and RDF is that the stores are like a big bucket of data and you can keep throwing more and more in. Hopefully future linked data tools will make this step trivial, but for now some scripting was required.
4. Job done. I now have links from research projects to regions.

Basically what I created from this was an aggregation of various datasets that you can now query. This is something that is made very easy using linked data and URIs to identify things like postcodes. As more publishers release data in linked data form there is more and more potential for building services and applications on top of aggregations of these datasets.  So that’s what I decided to do…

This application (I make the usual apology for my lack of web development skills and for the slowness which some caching would not doubt sort out) builds a clickable map view of this data aggregation. The OS OpenSpace API makes it possible to retrieve the unit ID for selected polygons. I can then use this unit ID in a SPARQL query to find the projects funded in that region.

However, it would have been easier if there was a RESTful API on top of the data aggregation that would have let me retrieve these results instead of doing some SPARQL. So that is what I decided to build next using the Linked Data API. The linked data API basically lets you create RESTful type shortcuts to relatively complex SPARQL query. Due to my lack of PHP skills it was an initially bumpy ride getting it to work (see here) but I got there in the end and the result was an API that lets you return research projects by selecting regions either through their SNAC codes or Ordnance Survey IDs, e.g:

http://www.johngoodwin.me.uk/bis/api/project/county/os/{unit id},

e.g. http://www.johngoodwin.me.uk/bis/api/project/county/os/17765

results can be returned in different formats using content negotiation [1] or by simple adding the relevant .html, .json to the URI, e.g.:

http://www.johngoodwin.me.uk/bis/api/project/euro/os/41424.html

http://www.johngoodwin.me.uk/bis/api/project/euro/os/41424.json

I hope this example shows how linked data can be useful in building applications on top of data aggregations. To summarise:

1. Publishers release data in linked data format.
2. Having data in a common format (RDF) with dereferencable URIs makes it relatively each to retrieve and aggregate from a number of resources, especially if data is linked to URIs for ‘things’ and not just ‘strings’.
3. The linked data API makes it possible to build a RESTful service on top of a data aggregation so web developers need not be put of by complex SPARQL queries.
4. Applications can then built using these services.

[1] for some reason the HTML conneg only seems to work in Firefox.

I spoke a bit about the administrative geography data in a previous blog post – but the data has changed a bit since then. Just to re-cap the administrative geography linked data contains information about administrative and voting geographic regions. These include unitary authorities, counties, wards, constituencies, Welsh Assembly regions and a whole lot more [1]. Here are some examples:

• The City of Southampton
• The County of Hampshire
• The South East
• Tunbridge Wells
• North Wales
• Scotland

If you want to find a full list of the sorts of thing you can find in the data simply go to the query interface (or SPARQL endpoint as it is know) and try the following query:

select distinct ?type

where { ?a a ?type . }

Now you have the list all of type of things in the data you can as for lists of instances of those types.

For example, the following query will return all of the unitary authorities:

select ?a

where {

?a a <http://data.ordnancesurvey.co.uk/ontology/admingeo/UnitaryAuthority> .

}

All of the names of all the regions have now been modelled using the SKOS vocabulary. If you want to find the official names of all the unitary authorities you can simple issue a query like:

select ?a ?name

where

{

?a a <http://data.ordnancesurvey.co.uk/ontology/admingeo/UnitaryAuthority> .

?a <http://www.w3.org/2004/02/skos/core#prefLabel> ?name  .}

Also included in the data are two attributes called Unit ID and Area Code. These values are useful if you want to produce a mashup using this data and display it by boundary.

So for example, for Southampton (http://data.ordnancesurvey.co.uk/id/7000000000037256) the area code is UTA (for unitary authority) and the unit ID is 37256. These values can be used as follows:

/*here we set-up the our variable called ‘boundaryLayer’ with the strategies that we require. In this case, it is its ID and type i.e. Unitary Authority */

boundaryLayer = new OpenSpace.Layer.Boundary(“Boundaries”,

{ strategies: [new OpenSpace.Strategy.BBOX()], admin_unit_ids: ["37256"], area_code: ["UTA"] });

//then we add the bounadry to the map osMap.addLayer(boundaryLayer);

//this effectively refreshes the map, so that the boundary is visible

osMap.setCenter(osMap.getCenter());

to display the Southampton boundary using the OS OpenSpace API. See http://openspace.ordnancesurvey.co.uk/openspace/support.html for more details.

Arguably the most useful information in this data are the qualitative spatial relationships between different regions. Regions are related to the regions they contain, they are within and they touch. In the case of the touching relationship only regions of the same type have an explicit touching relationship. The exception to this are unitary authorities, counties, district and metropolitan district that also have touching relationships between each other. The following simple query will return a list of all counties, districts and unitary authorities that border The City of Southampton. It will also return their names:

PREFIX spatialrelations: <http://data.ordnancesurvey.co.uk/ontology/spatialrelations/>

select ?a ?name

where

{

?a spatialrelations:touches <http://data.ordnancesurvey.co.uk/id/7000000000037256> .

?a <http://www.w3.org/2004/02/skos/core#prefLabel> ?name  .

}

If you are only interested in the bordering counties you can add an extra line to your query:

PREFIX spatialrelations: <http://data.ordnancesurvey.co.uk/ontology/spatialrelations/>

select ?a ?name

where

{

?a spatialrelations:touches <http://data.ordnancesurvey.co.uk/id/7000000000037256> .

?a <http://www.w3.org/2004/02/skos/core#prefLabel> ?name  .

?a a <http://data.ordnancesurvey.co.uk/ontology/admingeo/County> .

}

Similarly, the following query returns all the county electoral divisions (and their names) within Hampshire:

PREFIX spatialrelations: <http://data.ordnancesurvey.co.uk/ontology/spatialrelations/>

select ?a ?name

where

{

?a spatialrelations:within <http://data.ordnancesurvey.co.uk/id/7000000000017765> .

?a <http://www.w3.org/2004/02/skos/core#prefLabel> ?name  .

?a a <http://data.ordnancesurvey.co.uk/ontology/admingeo/CountyElectoralDivision> .

}

For convenience some shortcuts have been added to the data in this release. For certain nesting geographies, such as the county – district – parish or district – ward nestings, various new properties have been added. For example, the property ‘counyElectoralDivision‘ relates all counties to their constituent county electoral divisions. The above query can now be done in a simpler way:

PREFIX admingeo: <http://data.ordnancesurvey.co.uk/ontology/admingeo/>

select ?a ?name

where

{

<http://data.ordnancesurvey.co.uk/id/7000000000017765> admingeo:countyElectoralDivision ?a .

?a <http://www.w3.org/2004/02/skos/core#prefLabel> ?name  .

}

Similar predicates such as ‘county‘, ‘district‘, ‘ward‘, ‘constituency‘ etc. provide similar shortcuts. For example, the following returns all the Westminster constituencies in South East England.

PREFIX admingeo: <http://data.ordnancesurvey.co.uk/ontology/admingeo/>

select ?a ?name

where {

<http://data.ordnancesurvey.co.uk/id/7000000000041421> admingeo:westminsterConstituency ?a .

?a <http://www.w3.org/2004/02/skos/core#prefLabel> ?name  . }

The most significant introduction to this data is the inclusion of postcode information. The data now contains information about postcode units, postcode sectors, postcode districts and postcode areas. For each postcode unit an easting/northing coordinate value is given [2] along with the district, ward and county (where applicable) that contains said postcode unit. An example of this can be seen for the Ordnance Survey postcode SO16 4GU. Each postcode is also related to its containinb postcode area, sector and district.

The properties ‘ward‘, ‘district‘ and ‘county‘ relate a postcode to the relevant regions. The simple query:

PREFIX postcode: <http://data.ordnancesurvey.co.uk/ontology/postcode/>

select ?district

where {

<http://data.ordnancesurvey.co.uk/id/postcodeunit/SO164GU> postcode:district ?district .

}

returns the unitary authority that contains the postcode SO16 4GU.

This query:

PREFIX spatialrelations: <http://data.ordnancesurvey.co.uk/ontology/spatialrelations/>

select ?postcode

where

{

?postcode spatialrelations:within <http://data.ordnancesurvey.co.uk/id/postcodearea/SO> .

}

returns all the postcodes in the SO postcode area.

We can combine the above two queries to find the areas, along with their names, covered by the postcode area SO:

PREFIX spatialrelations: <http://data.ordnancesurvey.co.uk/ontology/spatialrelations/>

PREFIX postcode: <http://data.ordnancesurvey.co.uk/ontology/postcode/>

select distinct ?district ?name

where

{

?postcode spatialrelations:within <http://data.ordnancesurvey.co.uk/id/postcodearea/SO> .

?postcode postcode:district ?district .

?district <http://www.w3.org/2004/02/skos/core#prefLabel> ?name  .

}

Hopefully these few examples will give you enough information to fully explore this new release of the Ordnance Survey linked data. For those of you who don’t like SPARQL watch this space – hopefully we will soon(ish) have an API built on top of this data to allow for even easy access.

[1] you’ll notice the ‘isDefinedBy’ link currently returns a 404 – not for long I hope

[2] lat/long to follow