It is not always greener on the other side: Greenery perception across demographics and personalities in multiple cities
Not on the map. Why?
Abstract (arXiv)
Quantifying and assessing urban greenery is consequential for planning and development, reflecting the everlasting importance of green spaces for multiple climate and well-being dimensions of cities. Evaluation can be broadly grouped into objective (e.g., measuring the amount of greenery) and subjective (e.g., polling the perception of people) approaches, which may differ -- what people see and feel about how green a place is might not match the measurements of the actual amount of vegetation. In this work, we advance the state of the art by measuring such differences and explaining them through human, geographic, and spatial dimensions. The experiments rely on contextual information extracted from street view imagery and a comprehensive urban visual perception survey collected from 1,000 people across five countries with their extensive demographic and personality information. We analyze the discrepancies between objective measures (e.g., Green View Index (GVI)) and subjective scores (e.g., pairwise ratings), examining whether they can be explained by a variety of human and visual factors such as age group and spatial variation of greenery in the scene. The findings reveal that such discrepancies are comparable around the world and that demographics and personality do not play a significant role in perception. Further, while perceived and measured greenery correlate consistently across geographies (both where people and where imagery are from), where people live plays a significant role in explaining perceptual differences, with these two, as the top among seven, features that influences perceived greenery the most. This location influence suggests that cultural, environmental, and experiential factors substantially shape how individuals observe greenery in cities.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 4 of its 73 references that are in the map. First posted on arXiv 19 Dec 2025.
Builds on
4 of 73 references.
References · 4
- 2024Coverage and Bias of Street View Imagery in Mapping the Urban Environmentcited by 12
- 2025Global urban visual perception varies across demographics and personalitiescited by 10
- 2022Estimating building energy efficiency from street view imagery, aerial imagery, and land surface temperature datacited by 8
- 2024ZenSVI: An Open-Source Software for the Integrated Acquisition, Processing and Analysis of Street View Imagery Towards Scalable Urban Sciencecited by 3
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Citing papers per year: 2026: 1.
1 paper.