Figure 5 from Aesthetic capital: what makes london look beautiful, quiet, and happy? | Semantic Scholar (2024)

A comparative analysis of crowdsourced impressions generated by local and non-local observers shows that non-local observers reach higher inter-observer agreement compared to local ones, and that these two groups tend to disagree on what they report to perceive with respect to dimensions like danger and interest.

This work uses data from a crowd-sourcing platform to quantify the extent to which urban locations are pleasant, and finds that the recommended routes add just a few extra walking minutes and are indeed perceived to be more beautiful, quiet, and happy.

This study examines how images collected from social media can be used for the crowdsourced characterization of indoor ambiance impressions in popular urban places and designs a crowdsourcing framework to understand suitability of social images as data source to convey place ambiance.

This study examines urban perceptions of three cities in central Mexico, which integrates a mobile crowdsourcing framework to collect geo-localized images of urban environments by a local youth community, and an online crowdsourcing platform to gather impressions ofurban environments along twelve physical and psychological dimensions.

The possibility of using social media data to reliably map the ambiance of neighborhoods in the entire city of London and the ability to explain each prediction of a neighborhood's ambiance by identifying the picture that best reflects the meaning of that ambiance is explored.

Analysis of human perceptions from residents of a mid-sized Brazilian city shows that features of the urban form proposed by urbanists can be combined with sociodemographics to improve the accuracy of machine learning models that predict which scene a person will find more safe or pleasant.

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This experiment was designed to investigate the relationship between the fundamental visual, spatial properties of existing complex streetscapes and aesthetic judgments, and the development of the theoretical understanding of the link between the influence of selected spatial properties and the aesthetic judgment of the visual quality of urban spaces.

This work uses data from a crowd-sourcing platform to quantify the extent to which urban locations are pleasant, and finds that the recommended routes add just a few extra walking minutes and are indeed perceived to be more beautiful, quiet, and happy.

The results show that online images can be used to create reproducible quantitative measures of urban perception and characterize the inequality of different cities, using thousands of geo-tagged images to measure the perception of safety, class and uniqueness.

A web game that puts the recognizability of London's streets to the test and finds that some boroughs have little cognitive representation and that areas with low Recognizability do not fare any worse on the economic indicators of income, education, and employment, but they do significantly suffer from social problems of housing deprivation, poor living conditions, and crime.

It is shown that geographically representative image elements can be discovered automatically from Google Street View imagery in a discriminative manner and it is demonstrated that these elements are visually interpretable and perceptually geo-informative.

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