Quantifying scenic areas using crowdsourced data

For centuries, philosophers, policy-makers and urban planners have debated whether aesthetically pleasing surroundings can improve our wellbeing. To date, quantifying how scenic an area is has proved challenging, due to the difficulty of gathering large-scale measurements of scenicness. In this study we ask whether images uploaded to the website Flickr, combined with crowdsourced geographic data from OpenStreetMap, can help us estimate how scenic people consider an area to be. We validate our findings using crowdsourced data from Scenic-Or-Not, a website where users rate the scenicness of photos from all around Great Britain. We find that models including crowdsourced data from Flickr and OpenStreetMap can generate more accurate estimates of scenicness than models that consider only basic census measurements such as population density or whether an area is urban or rural. Our results provide evidence that by exploiting the vast quantity of data generated on the Internet, scientists and policy-makers may be able to develop a better understanding of people's subjective experience of the environment in which they live

Quantifying the impact of scenic environments on health

Few people would deny an intuitive sense of increased wellbeing when spending time in beautiful locations. Here, we ask: can we quantify the relationship between environmental aesthetics and human health? We draw on data from Scenic-Or-Not, a website that crowdsources ratings of “scenicness” for geotagged photographs across Great Britain, in combination with data on citizen-reported health from the Census for England and Wales. We find that inhabitants of more scenic environments report better health, across urban, suburban and rural areas, even when taking core socioeconomic indicators of deprivation into account, such as income, employment and access to services. Our results provide evidence in line with the striking hypothesis that the aesthetics of the environment may have quantifiable consequences for our wellbeing

Quantifying the link between art and property prices in urban neighbourhoods

Is there an association between art and changes in the economic conditions of urban neighbourhoods? While the popular media and policymakers commonly believe this to be the case, quantitative evidence remains lacking. Here, we use metadata of geotagged photographs uploaded to the popular image-sharing platform Flickr to quantify the presence of art in London neighbourhoods. We estimate the presence of art in neighbourhoods by determining the proportion of Flickr photographs which have the word ‘art’ attached. We compare this with the relative gain in residential property prices for each Inner London neighbourhood. We find that neighbourhoods which have a higher proportion of ‘art’ photographs also have greater relative gains in property prices. Our findings demonstrate how online data can be used to quantify aspects of the visual environment at scale and reveal new connections between the visual environment and crucial socio-economic measurements

Using deep learning to quantify the beauty of outdoor places

Beautiful outdoor locations are protected by governments and have recently been shown to be associated with better health. But what makes an outdoor space beautiful? Does a beautiful outdoor location differ from an outdoor location that is simply natural? Here, we explore whether ratings of over 200 000 images of Great Britain from the online game Scenic-Or-Not, combined with hundreds of image features extracted using the Places Convolutional Neural Network, might help us understand what beautiful outdoor spaces are composed of. We discover that, as well as natural features such as ‘Coast’, ‘Mountain’ and ‘Canal Natural’, man-made structures such as ‘Tower’, ‘Castle’ and ‘Viaduct’ lead to places being considered more scenic. Importantly, while scenes containing ‘Trees’ tend to rate highly, places containing more bland natural green features such as ‘Grass’ and ‘Athletic Fields’ are considered less scenic. We also find that a neural network can be trained to automatically identify scenic places, and that this network highlights both natural and built locations. Our findings demonstrate how online data combined with neural networks can provide a deeper understanding of what environments we might find beautiful and offer quantitative insights for policymakers charged with design and protection of our built and natural environments

Historical analysis of national subjective wellbeing using millions of digitized books

In addition to improving quality of life, higher subjective wellbeing leads to fewer health problems and higher productivity, making subjective wellbeing a focal issue among researchers and governments. However, it is difficult to estimate how happy people were during previous centuries. Here we show that a method based on the quantitative analysis of natural language published over the past 200 years captures reliable patterns in historical subjective wellbeing. Using sentiment analysis on the basis of psychological valence norms, we compute a national valence index for the United Kingdom, the United States, Germany and Italy, indicating relative happiness in response to national and international wars and in comparison to historical trends in longevity and gross domestic product. We validate our method using Eurobarometer survey data from the 1970s and demonstrate robustness using words with stable historical meanings, diverse corpora (newspapers, magazines and books) and additional word norms. By providing a window on quantitative historical psychology, this approach could inform policy and economic history

Happiness is greater in more scenic locations

Does spending time in beautiful settings boost people’s happiness? The answer to this question has long remained elusive due to a paucity of large-scale data on environmental aesthetics and individual happiness. Here, we draw on two novel datasets: first, individual happiness data from the smartphone app, Mappiness, and second, crowdsourced ratings of the “scenicness” of photographs taken across England from the online game Scenic-Or-Not. We find that individuals are happier in more scenic locations, even when we account for a range of factors such as the activity the individual was engaged in at the time, weather conditions and the income of local inhabitants. Crucially, this relationship holds not only in natural environments, but in built-up areas too, even after controlling for the presence of green space. Our results provide evidence that the aesthetics of the environments that policymakers choose to build or demolish may have consequences for our everyday wellbeing

Street-Frontage-Net: urban image classification using deep convolutional neural networks

Quantifying aspects of urban design on a massive scale is crucial to help develop a deeper understanding of urban designs elements that contribute to the success of a public space. In this study, we further develop the Street-Frontage-Net (SFN), a convolutional neural network (CNN) that can successfully evaluate the quality of street frontage as either being active (frontage containing windows and doors) or blank (frontage containing walls, fences and garages). Small-scale studies have indicated that the more active the frontage, the livelier and safer a street feels. However, collecting the city-level data necessary to evaluate street frontage quality is costly. The SFN model uses a deep CNN to classify the frontage of a street. This study expands on the previous research via five experiments. We find robust results in classifying frontage quality for an out-of-sample test set that achieves an accuracy of up to 92.0%. We also find active frontages in a neighbourhood has a significant link with increased house prices. Lastly, we find that active frontage is associated with more scenicness compared to blank frontage. While further research is needed, the results indicate the great potential for using deep learning methods in geographic information extraction and urban design

Historical analysis of national subjective wellbeing using millions of digitized books

In addition to improving quality of life, higher subjective wellbeing leads to fewer health problems and higher productivity, making subjective wellbeing a focal issue among researchers and governments. However, it is difficult to estimate how happy people were during previous centuries. Here we show that a method based on the quantitative analysis of natural language published over the past 200 years captures reliable patterns in historical subjective wellbeing. Using sentiment analysis on the basis of psychological valence norms, we compute a national valence index for the United Kingdom, the United States, Germany and Italy, indicating relative happiness in response to national and international wars and in comparison to historical trends in longevity and gross domestic product. We validate our method using Eurobarometer survey data from the 1970s and demonstrate robustness using words with stable historical meanings, diverse corpora (newspapers, magazines and books) and additional word norms. By providing a window on quantitative historical psychology, this approach could inform policy and economic history

From landscapes to cityscapes : quantifying the connection between scenic beauty and human wellbeing

Intuitively, we often seek out beautiful scenery when we want a respite from our busy lives, but do such settings actually help to boost our wellbeing? While architects, urban planners and policymakers have puzzled over this question for centuries, quantitative analyses have been held back by a lack of data on the beauty of our environment. However, the vast volumes of geotagged images readily shared on the Internet, alongside developments in computer vision and deep learning, are opening up opportunities to quantify aspects of the visual environment that were previously hard to measure. In the research reported here, we ask: might the beauty of outdoor environments have a quantifiable association with increased wellbeing? This thesis explores the following related strands of work: (1) How accurately can we automatically predict the beauty of scenes for which we do not have survey or crowdsourced scenicness data? (2) Is there a quantifiable connection between the beauty of the environment, as measured by scenicness, and people’s wellbeing? (3) Can we develop a broader understanding of what beautiful outdoor spaces are composed of? In the first strand, we investigate whether a deep learning model can be trained to automatically infer the scenicness of images. We find that a retrained convolutional neural network performs remarkably well, and that this network highlights not only natural but also built-up locations as being scenic. In the second strand, we explore the connection between beautiful scenery and different types of wellbeing: happiness, mental distress and life satisfaction. We find that individuals experience more happiness when visiting more scenic locations, even when we account for a range of factors such as weather conditions and the income of local inhabitants. However, in terms of mental distress and life satisfaction, we do not find evidence that individuals who live in more scenic locations report higher levels of wellbeing. In the third and final strand, we analyse crowdsourced data and discover that beautiful places are composed of natural features such as ‘Coast’, ‘Mountain’ and ‘Canal Natural’ as well as man-made structures such as ‘Tower’, ‘Castle’ and ‘Viaduct’. Importantly, while scenes containing ‘Trees’ tend to rate highly, places containing more bland natural green features such as ‘Grass’ and ‘Athletic Fields’ are considered less scenic. The research reported in this thesis takes an important step towards providing evidence that the beauty of the environment, and therefore decisions made about the design of environments, might have a crucial impact on people's everyday wellbeing. Our results also demonstrate that online data combined with neural networks can provide a deeper understanding of which environments humans might find beautiful

Data for Using Deep Learning to Quantify the Beauty of Outdoor Places

This database lists all the Geograph images we used from Scenic-Or-Not (http://scenicornot.datasciencelab.co.uk/) to help us understand what beautiful outdoor spaces are composed of. We only include images in our analysis that have been rated more than three times