Does living in a beauty spot make us healthier? And what do we consider a scenic view? These were the questions faced by researchers at the Turing Institute when they began their study using large-scale data capture to look at the role our environment plays in our health. Here, Chanuki Illushka Seresinhe of Warwick University shares some tantalising initial findings from the innovative research carried out at the Turing Institute, where she is spending an enrichment year.

For centuries, philosophers, policy-makers and urban planners have debated whether living in more picturesque surroundings can improve our wellbeing. However, finding evidence to inform this debate has proven to be tricky, as gathering large-scale survey data on people’s perceptions of their surroundings is a highly time-consuming and costly endeavour. Luckily, today we have a new resource: all the data generated through our increasing interactions with the internet has allowed us to measure human experience on an unprecedented scale.

We were thrilled to discover the online game Scenic-Or-Not, where Internet users rate the “scenicness” of photos that cover nearly 95% of the 1 km grid squares of Great Britain. Over 1.5 million ratings of more than 212,000 pictures of Britain have been collected so far. In our first study exploring the connection between scenic places and human wellbeing, we decided to combine these ratings with data from the 2011 Census for England and Wales, where people report their health status. We wanted to find out if people feel healthier in more scenic environments.

However, we also had to account for a wide range of confounding factors that might be related to people’s reports about their health. For example, it could be that richer people are living in more scenic areas, and thus reporting better health. Or, scenic places might be only those that are in rural areas. After building a variety of such factors into our analysis, including neighbourhood income and access to services, we still found that people feel healthier when they live in more scenic locations, and this holds across urban, suburban and rural areas. 

Crucially, we also found that scenic areas were not simply green areas. While our analysis confirmed that people do report better health in areas with more green land cover, we found that reports of health can be better explained when considering ratings of scenicness, rather than purely by measurements of green space.

So, you might ask, what are these beautiful places actually composed of? We decided to get a deeper understanding of the all the images being rated on Scenic-Or-Not by using an AI algorithm, specifically MIT Places, to analyse over 200,000 Scenic-or-Not images to uncover what attributes, such as “trees”, “mountain”, “hospital” and “highway”, corresponded to high and low scenic ratings.

We discovered that features such as “Valley”, “Coast”, “Mountain” and “Trees” were associated with higher scenicness. However, some man-made elements also tended to improve scores, including historical architecture such as “Church”, “Castle”, “Tower” and “Cottage”, as well as bridge-like structures such as “Viaduct” and “Aqueduct”. Interestingly, large areas of greenspace such as “Grass” and “Athletic Field” led to lower ratings of scenicness, rather than boosting scores. You can read that research here.

It appears that the old adage ‘natural is beautiful’ seems to be incomplete: flat and uninteresting green spaces are not necessarily beautiful, while characterful buildings and stunning architectural features can improve the beauty of a scene.

In order to ensure the wellbeing of local residents, urban planners and policy makers might find it valuable to consider the aesthetics of the environment when embarking on new projects. Our findings imply that simply introducing greenery, without considering the beauty of the resulting environment, might not be enough.

In the next phase of this research we are exploring whether people are also happier in more beautiful environment, using data from the innovative iPhone app Mappiness. Follow us on Twitter at @thedatascilab or @thoughtsymmetry for further developments on this research.