Important aims, interesting data analysis

Throughout our time in business, we’ve been lucky in being able to contribute to research which is both important in its aims and fascinating in the methods which can be applied.

The Environment Agency was an early adopter of spatial analysis, not just to manage assets and immediate interventions, but to plan for future policy and delivery. The ability to integrate a number of unrelated datasets and analyse what they reveal together is a huge benefit in real-world situations where all of these factors are at work.

Geofutures was commissioned by the Environment Agency and environmental consultancy Brook Lyndhurst to create an online weighted overlay model to determine the location of the 50 worst places to live in England and Wales. Internally, we tried hard not to call it Crap Towns – the data science behind it demanded more respect!

The model processed a range of social, economic, and environmental factors, including ambient air pollution, flood risk, housing in poor condition, street cleanliness, and the index of multiple deprivation.  Each factor could be viewed individually as a theme or layer of data on the map, giving an immediate and compelling overview of environmental quality.

Allowing the user to define their relative importance, individual factors could be combined and weighted on the map, and used to determine the location of the 50 worst districts to live in England and Wales.  Different combinations and weights yielded different results.

The results fed into into other analyses and planning scenarios, and Geofutures’ work with The Environment Agency continued with a research into designing a methodology for a single metric of environmental inequity.

This research examined the feasibility of creating a composite measure of environmental inequity. While the subject matter is dissimilar, our earlier work designing a composite measure for the Town Centres statistical series informed the proposed methodology.

We applied this experience to selecting and evaluating the indicators, representing them in a mapping environment, standardising data, integrating indicators by location and identifying, classifying and ranking resulting locations.

The study also explored issues of defining inequity and the advantages and drawbacks of composite indicators. Methodological questions were also explored: challenges of scale, error and uncertainty, and the best means of incorporating ‘soft knowledge’.

No matter the subject area, where policy and investment decisions need to be made based on complex information across multiple topics, spatial analysis techniques can bring the required information together. Map-based visualisations can make the results easy for everyone to share and understand.

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