27 Feb 2017

Tranquil City: Making Data Useful for Citizens


Londonlondon, Open Data

Figure 4: Tranquil Pavement Map with a ‘low pollution’ index showing zones of low noise and air pollution as well crowd-sourced tranquil spaces locations
Tranquil City, one of our London experimenters, discuss how they are making data useful for citizens.

We are living through an historical change in society, as citizens from around the world begin to exploit the potential of the Information Age. Although largely unknown to the general public, extensive datasets are now available, which can inform individuals on how their choices might affect their health, the environment and their quality of life.

These datasets are especially useful in cities, where dense populations mean that space and mobility are key issues for citizens. Understanding data on how people inhabit, move through and explore the city is increasingly being used to make cities more efficient. However, this data is only useful to individuals if it is presented in a clear and usable format.

A key intention of our project is to transform data into knowledge for citizens; knowledge that we can used to help make better choices on matters of health and the environment. Making data usable also encourages people to trust the available datasets and see the potential benefits that they can bring to their lives.

Understanding urban tranquillity and its benefits

Tranquil City is a project exploring urban calm. We believe that by understanding, promoting and creating areas of tranquillity in the city, everyone can improve their health and wellbeing as city dwellers.

OrganiCity is a project funded by the European Commission, which supports experimentation with citizen engagement to push innovation for our future smart cities. OrganiCity has funded Tranquil City to undertake an experiment into the use of environmental data to engage citizens in London. This experiment is now almost complete and we would like to share our initial findings.

In the first phase of the experiment, Tranquil City explored the meaning of tranquillity in the urban context, engaging citizens to contribute their own definition of the term by posting images and videos to Instagram. By doing this, people were sharing their own data to the platform and could see how their data was being used and hopefully become inspired by the contributions of others.

Figure 1: Key question of Tranquil City’s experimentation supported by OrganiCity

Figure 1: Key question of Tranquil City’s experimentation supported by OrganiCity

As part of our experimentation, supported by OrganiCity, we have been exploring the relationships between these crowdsourced tranquil spaces in London and the noise and air pollution environment across the city. This is in support of the concept that by travelling through or visiting tranquil areas, we can reduce our exposure to noise and pollution and also benefit our wellbeing by having more exposure to nature and positive spaces.

Linking tranquillity with noise and air pollution data

Our experiment relied upon several big open data sets, including noise maps produced by the UK government, and air quality (NO2 and PM2.5) maps produced by the Greater London Authority, as part of the London Atmospheric Emissions Inventory (LAEI).

The data sets were plotted using the open source Geographic Information Systems mapping software QGIS. The data looked pretty much unintelligible when combined!

Figure 2: First attempt at merging Tranquil City, OrganiCity and open noise and air quality data sets together

Figure 2: First attempt at merging Tranquil City, OrganiCity and open noise and air quality data sets together

The next stage was to combine this data in a way that was clear and usable for the public. We did this by prioritising noise and air pollutant concentrations in reference to national and international guidance and weighing them accordingly. This enabled us to streamline the data and create a conditioning statement with data points colour-coded depending on their pollution characteristics. Noise, NO2 and PM2.5 were combined in the Tranquil City ‘Low Pollution’ Index as described in Figure 3.

Figure 3: Tranquil City ‘low pollution’ index based on conditional statement between noise, NO2 and PM2.5 parameters

Figure 3: Tranquil City ‘low pollution’ index based on conditional statement between noise, NO2 and PM2.5 parameters

By carefully prioritising the important information and presenting it in a simple, straightforward way in our ‘Tranquil Pavement Map’ (shown in Figure 4), we hope that the data will be used by many people as soon as possible.

Our intention is that the map will eventually be used by citizens to find tranquil spaces and navigate the city, by highlighting areas of low noise and air pollution, which can be incorporated into routes – as well as travelling via crowdsourced tranquil spaces along the way.

Figure 4: Tranquil Pavement Map with a ‘low pollution’ index showing zones of low noise and air pollution as well crowd-sourced tranquil spaces locations

Figure 4: Tranquil Pavement Map with a ‘low pollution’ index showing zones of low noise and air pollution as well crowd-sourced tranquil spaces locations

We hope that by giving usable data back to the people who contributed to the crowdsourced map, we can demonstrate the power of data in shaping our cities, as well as encouraging more people to share their own tranquil spaces throughout the city.

For more information on Tranquil City, visit www.tranquilcity.co.uk or contact hello@tranquilcity.co.uk.


Sign up for updates from Organicity

We'll keep you up to date on events in your city, opportunities to get involved, progress on our Open Calls and citizen experiments.