Pixels, Plants, and People: Affective Evaluation of Urban Green Spaces

Abstract: Urban green spaces are critical for well-being, yet planners lack scalable ways to anticipate how environments will be perceived by users. We conducted an experiment with 27 participants who viewed 30 images of urban spaces while eye movements and brain activity were recorded. Image composition, parsed into 14 urban classes and aggregated as vegetation versus non-vegetation, systematically predicted responses: a higher proportion of vegetation drew more visual attention and was associated with higher attractiveness ratings, while images with less greenery elicited stronger pupillary responses. Brain analyses showed topographic patterns in theta and alpha activity between pleasant and unpleasant scenes, but these differences were not statistically significant. Taken together, our findings highlight systematic links between urban scene composition, attention, and affective responses. We will release our dataset
and software to support further research.

Link to the paper: https://doi.org/10.1145/3772318.3791826

Detailed description of the dataset and scripts: https://github.com/kayhan-latifzadeh/pixels-plants-people-chi26