Emotions, public space, and urban images in the context of COVID-19
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Abstract
During the COVID-19 pandemic, confinement and mobility restrictions gave rise to different questions
regarding the use and perceptions on public space, where the relational and contextual properties of this
space may cause a diversity of emotions. We use machine learning and social network analysis to explore
emotions in relation to the public space, based on attributes extracted from photos of the city of Quito,
Ecuador, taken between April and June 2020. Our results show that an attribute of the urban landscape
can be associated with positive and negative emotions, and that opposite attributes of the images (i.e.,
glossy and dirty) can both influence positive emotions regarding public space. This research inaugurates
a new field of study in Latin America regarding urban emotions, and also supports a better understanding
of citizen perceptions of the public space during the pandemic crisis.
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