The Whereabouts London project was developed by the team at the Future Cities Catapult, featured in the Economist Magazine (Economist Article). The methods are available here (Whereabouts London Tutorial).

Whereabouts London is an ongoing experiment by the Future Cities Catapult to explore how open data can be used to help cities and citizens see their environment in a new light. By blending 235 types of data, we‘re investigating what London could look like if we drew its boundaries afresh, grouping neighbourhoods based on how we live – not where we live.

Reimagining neighbourhoods in this way could help us all. Local authorities could work out how to share their services with each other; transport providers could tailor their services to travellers better than ever; behavioural change campaigns could be targeted in new ways to make them work more effectively. The possibilities are just as rich as the data.


Whereabouts London uses data from the Greater London Authority’s London Datastorealong with other publicly available datasets, and much of the code is open source.

The London Datastore is a hub for data about all aspects of the city. You can find, explore and build on over 500 different datasets that the city generates, by either downloading them or accessing them through APIs.

For the Whereabouts project, for instance, we’ve used the Datastore’s new spatial search function to help us extract data about neighbourhoods across the city. Then, we’ve merged it with open data from the Food Standards Agency, the Office for National StatisticsLand RegistryOpenStreetMapFlickr and Transport for London to understand what makes our local areas similar to, and distinct from, each other. You can explore the results on our interactive map.

But our Whereabouts are only one way of interpreting the data. The same information could be used by anyone to create their own maps, tailored to their own needs and interests. Fancy a go? The code for the generation of Whereabouts has been released under an Open Source licence and can be accessed here. Try it out.

No carsNo crime
No food agencyNo general health
Above are four examples of alternate clusters we created using a variety of datasets