DNF Demonstrator - Social Exclusion

This particular example demonstrates how DNF principles can be used in the context of cross referencing information to identify areas and groups of households that are not receiving the necessary services, but that might well benefit from them.

Historically, in Local Authorities, many datasets were managed separately by individual departments. There was little impetus in cross-referencing and ensuring quality, consistent datasets were being used, particularly address datasets describing the locations of their customers!

This has caused poor service delivery and has hampered social research programmes. With both, much time was spent on data cleansing before any data processing and analysis could be carried out.

Now, the maintenance of a common address gazetter, such as the Local Land and Property Gazetter (LLPG) is firmly embedded in the information strategies of most local authorities. This addressing hub enables multiple datasets to be cross-referenced and derived directly from the address gazetteer.

In this demonstrator, many datasets from different departments have been combined to enable accurate statistics about worklessness and social exclusion to be calculated and monitored. The data used in this demonstrator is not real, but has been produced to show how such information can cluster in particular neighbourhoods.

Users can choose from a list of pre saved queries or they can generate their own.

While the base reference data in each of these examples are real, all attribution in the application data and address data contained in each example is fictional. This includes, and is not limited to, water quality, flood risk, social indicators and fault reports about street furniture. Attributes in the data have however been modelled in such a way as to approximate the structure and nature of real application data.

Use this Demonstrator

View Worked Example

View Examples of Different Geographies

View saved queries

Find trends in the data

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