It is not always greener on the other side: Greenery perception across demographics and personalities in multiple cities

📅 2025-12-18
📈 Citations: 0
Influential: 0
📄 PDF
🤖 AI Summary
This study investigates the discrepancy between residents’ subjective perception of urban greenness and objective vegetation metrics (e.g., Green View Index, GVI), and its underlying determinants across global cities. Method: Leveraging street-view image perception experiments with 1,000 participants from five countries—integrated with demographic and personality data, GVI computation, pairwise comparison scoring, and multivariate cross-regional modeling—we quantified perceptual–objective alignment and identified key predictors. Results: Subjective greenness perception exhibits a robust positive correlation with objective GVI; however, residential location (proxying cultural and environmental exposure) and spatial distribution of greenness within scenes emerge as the strongest predictors of perceptual variation, whereas age, gender, and personality traits show negligible effects. This work provides the first systematic empirical evidence that environmentally acquired experience—not innate individual traits—predominantly shapes visual greenness cognition, thereby establishing a foundational evidence base for equity-oriented urban greening assessment and perception-informed planning.

Technology Category

Application Category

📝 Abstract
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.
Problem

Research questions and friction points this paper is trying to address.

Measures differences between objective and subjective urban greenery assessments
Explains perception gaps using human, geographic, and spatial factors
Analyzes how demographics and location influence greenery perception globally
Innovation

Methods, ideas, or system contributions that make the work stand out.

Combining street view imagery with perception surveys
Analyzing discrepancies between objective and subjective greenery measures
Identifying location as key factor in perception differences
🔎 Similar Papers
No similar papers found.
Matias Quintana
Matias Quintana
Singapore-ETH Centre
urban data scienceGeoAIhuman-building interactionintelligent environments
F
Fangqi Liu
Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong, , Hong Kong SAR
J
Jussi Torkko
Digital Geography Lab, Department of Geosciences and Geography, University of Helsinki, Helsinki, , Finland
Youlong Gu
Youlong Gu
National University of Singapore
Urban PlanningUrban AnalyticsMachine Learning
Xiucheng Liang
Xiucheng Liang
PhD candidate, Urban Analytics Lab, National University of Singapore
urban analyticsGeoAIcomputer visionhuman perception
Yujun Hou
Yujun Hou
Department of Architecture, National University of Singapore, 4 Architecture Drive, Singapore, 117566, Singapore
K
Koichi Ito
Department of Architecture, National University of Singapore, 4 Architecture Drive, Singapore, 117566, Singapore
Y
Yihan Zhu
Department of Architecture, National University of Singapore, 4 Architecture Drive, Singapore, 117566, Singapore
M
Mahmoud Abdelrahman
Department of Architecture, National University of Singapore, 4 Architecture Drive, Singapore, 117566, Singapore
T
Tuuli Toivonen
Digital Geography Lab, Department of Geosciences and Geography, University of Helsinki, Helsinki, , Finland
Y
Yi Lu
Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong, , Hong Kong SAR
Filip Biljecki
Filip Biljecki
Assistant Professor, Urban Analytics Lab, National University of Singapore
urban data scienceurban informaticsurban analyticsgeographic data scienceGeoAI