The Beta version of the site for the RAGLD (Rapid Assembly of Geo-centred Linked Data applications) project was recently released. RAGLD is a collaborative project between the Ordnance Survey, the University of Southampton and Seme4. Its main aim is to build tools to enable developers to make greater use of geo-centred linked data.
The RAGLD project started in October 2011 and is due for completion in March 2013. It is part-funded by the Technology Strategy Boards Harnessing Large and Diverse Sources of Data programme.
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, the provision of access to tools and technologies that enable data consumers to easily select, filter, manipulate, visualise, transform and communicate data in ways that are suited to specific decision-making processes is needed.
This project will enable organisations to press maximum value from the UKs growing portfolio of linked data assets. In particular, a suite of software components that enables diverse organisations to rapidly assemble goal-oriented linked data applications and data processing pipelines in order to enhance their awareness and understanding of the UKs geographic, economic and socio-cultural landscape will be developed.
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 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 will be incorporated. The technical outcomes of this project will lead to significant improvements in the ability to exploit large-scale linked data sets for the purposes of strategic decision-making.
A presentation on the project can be found