Greater London Authority to utilise predictive data modelling

Better use of flexible and predictive data modelling is to be introduced at The Greater London Authority (GLA) via a new predictive data modelling platform that is currently under construction.

A blog post on the London Datastore website says that big data company Mastodon C is working together with the Greater London Authority to develop a flexible approach to city modelling. The aim is to take forecasting beyond the limitations of Excel, while providing modellers with the benefits of sophisticated data management tools, such as version control, security levels and scaling to very big or complex datasets.


First features

The blog post revealed that the first features of the platform – also known as the Witan Project – are being worked on together with the GLA demography team. Population projections are vital to London planning and provides a base for the London Plan, the Mayor’s strategic plan for London up until 2036. London population projections are also used by multiple departments within the GLA and underlie many other models, and including these models early on means that the Witan platform can be beneficial throughout the organisation.

Another feature being implemented is an interface for London boroughs to input data and run population projection scenarios themselves, without having to rely on GLA modelling resources. The platform will provide boroughs with a powerful tool to take charge of their own, local, modelling requirements.


Going forward

Going forward, this interface can be used for other city modelling purposes and be expanded to other stakeholders beyond the London boroughs, providing the ability to run different models and access a host of datasets, both open and private, to play out city modelling scenarios.

The first features of Witan in the autumn and hope to provide updates on the platform build as we progress. The platform will be built using open source and interested parties will be able to access the platform code freely.

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