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Tranquil City, Tranquil Pavement London

A mapping tool for exploring & celebrating tranquility in London

Visit Tranquil Pavement London

Tranquil Pavement London is a mobile web app we built for Tranquil City to help Londoners find and share tranquil places in their local communities. We all know quiet spaces we seek refuge in when we need some space away from normal life, using this app, you can now find more, wherever you are in London.

Tranquil City is a group of environmental enthusiasts committed to exploring and celebrating any and all tranquil places, especially around London. With the help of funding from OrganiCity, and the data collected by Tranquil City, we were able to develop and build a tool that changes how we see tranquility in our city, the Tranquil Pavement London!

This unique experience allows users to identify areas of tranquility, near them, in the urban, concrete jungle that is London. The application not only allows people to find areas of interest, but also to contribute to the community.

What does tranquility mean?

The beauty of using the Tranquil Pavement tool is that tranquility means whatever you want it to mean. There are two ways in which tranquility is represented:

Objective tranquility

You will notice that the base map interface has a layer of data on top of it. This data is displayed with the use of individual points, which appear as circular data points. These data points are an amalgamation of noise and air pollution for those specific areas.

These points are filled in, and range from white to green (see key). The whitest points represented higher levels of pollution, and the greenest points represent lower levels.

Subjective, crowd-sourced tranquility

Tranquility isn’t only determined by low levels of air and noise pollution, but by the users themselves. Users can take a picture on Instagram, upload their snap and give it the hashtag #tranquilcitylondon.

Once a user uploads the image to Instagram with the correct hashtag, it gets scraped using the Instagram API and pulled into the tool.

If you’d like to learn more about how we went about deciding on our approach, and the right technologies to use, be sure to read our blog (written by our senior developer on the project), Mapping Big Data.