Put in Your Postcode, Out Comes the Data: A Case Study

Abstract

A single datum or a set of a categorical data has little value on its own. Combinations of disparate sets of data increase the value of those data sets and helps to discover interesting patterns or relationships, facilitating the construction of new applications and services. In this paper, we describe an implementation of using open geographical data as a core set of "join point"(s) to mesh different public datasets. We describe the challenges faced during the implementation, which include, sourcing the datasets, publishing them as linked data, and normalising these linked data in terms of finding the appropriate "join points" from the individual datasets, as well as developing the client application used for data consumption. We describe the design decisions and our solutions to these challenges. We conclude by drawing some general principles from this work.

A framework for connecting two interoperability universes: OGC Web Feature Services and Linked Data

“…As described in Section 3, RDF is one of the standard languages in which information has to be made available, according to the Linked Data principles. The reason for this is that it offers several advantages, such as the provision of an extensible schema, de‐referenceable URIs, and as RDF links are typed, safe merging (linking) of different datasets (Omitola et al, ).…”

Investigating The Use Of Semantic Technologies In Spatial Mapping Applications

“…With respect to data.gov.uk datasets, Prof Shadbolt and Sir Tim Berners Lee were instrumental in the process of opening-up government data (Omitola et al 2010). However, only a small percentage of the data is accessible as semantic datasets.…”

From Information to Sense-Making: Fetching and Querying Semantic Repositories

“…al. [4] carried out a case study showing how United Kingdom geographical data, from the UK's Ordnance Survey, can be used as a set of "join points" to mesh public data for crime, mortality rates, and hospital waiting times. Meshing was defined as the ability to naturally merge together a dynamic set of information sources, and a join-point as a point of reference shared by all datasets.…”

“…Sourcing the datasets: Since many of the datasets of interest were in commaseparated-value (CSV) format, a process of converting the datasets to RDF had to be undertaken, 2. Modelling the datasets: The sources of the datasets contained little or no explicit semantic description of the data, [4] had to provide schema definitions for these data, 3. Aligning of datasets: The datasets were harvested from disparate public bodies, the problem of unintentional coreference, i.e.…”