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Global urban visual perception varies across demographics and personalities

Nature Cities volume 2, pages 1092–1106 (2025)Cite this article

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Abstract

Understanding people’s preferences is crucial for urban planning, yet current approaches often combine responses from multi-cultural populations, obscuring demographic differences and risking amplifying biases. We conducted a large-scale urban visual perception survey of streetscapes worldwide using street view imagery, examining how demographics—including gender, age, income, education, race and ethnicity, and personality traits—shape perceptions among 1,000 participants with balanced demographics from five countries and 45 nationalities. This dataset, Street Perception Evaluation Considering Socioeconomics, reveals demographic- and personality-based differences across six traditional indicators—safe, lively, wealthy, beautiful, boring, depressing—and four new ones: live nearby, walk, cycle, green. Location-based sentiments further shape these preferences. Machine-learning models trained on existing global datasets tend to overestimate positive indicators and underestimate negative ones compared to human responses, underscoring the need for local context. Our study aspires to rectify the myopic treatment of street perception, which rarely considers demographics or personality traits.

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Fig. 1: Research gaps and key questions in urban visual perception studies, followed by our methodology to bridge them.
Fig. 2: Statistical difference of perception scores by demographics.
Fig. 3: Statistical difference of perception scores by personality traits.
Fig. 4: Street-level images perception Q scores statistical comparisons.
Fig. 5: Correlation between our four new indicators and the six predominantly used indicators across all locations.

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Data availability

Annotated and labeled SVI data are obtained from the NUS Global Streetscapes dataset at https://huggingface.co/datasets/NUS-UAL/global-streetscapes. Raw images were obtained from the crowd-sourced platforms Mapillary and KartaView. We share openly our dataset SPECS (Street Perception Evaluation Considering Socioeconomics), consisting of survey responses and participants’ demographic data at https://huggingface.co/datasets/matiasqr/specs.

Code availability

The step-by-step process and code for all analyses are available via Github at https://github.com/matqr/specs.

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Acknowledgements

We thank the survey participants from around the world for their time and responses. We also thank A. G. Fernandez, F. Hammer and S. Morgenstern for the initial discussions and Milieu Insight Pte Ltd for the data-collection work. This research was conducted at the Future Cities Lab Global at Singapore-ETH Centre. Future Cities Lab Global is supported and funded by the National Research Foundation, Prime Minister’s Office, Singapore, under its Campus for Research Excellence and Technological Enterprise (CREATE) program and ETH Zürich (ETHZ), with additional contributions from the National University of Singapore (NUS), Nanyang Technological University (NTU), Singapore and the Singapore University of Technology and Design (SUTD) (M.Q., Y.G., F.B.). This research supported by the Singapore International Graduate Award (SINGA) scholarship provided by the Agency for Science, Technology and Research (A*STAR) (K.I.), the NUS Graduate Research Scholarship (X.L.) and the NUS (Y.Z.). This research is part of the project Multi-scale Digital Twins for the Urban Environment: From Heartbeats to Cities, which is supported by the Singapore Ministry of Education Academic Research Fund Tier 1 (M.A., F.B.). This research is part of the project Large-scale 3D Geospatial Data for Urban Analytics, which is supported by the National University of Singapore under the start-up grant R-295-000-171-133 (Y.H., F.B.).

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Authors and Affiliations

  1. Future Cities Lab Global, Singapore-ETH Centre, Singapore, Singapore

    Matias Quintana & Youlong Gu

  2. Department of Architecture, National University of Singapore, Singapore, Singapore

    Youlong Gu, Xiucheng Liang, Yujun Hou, Koichi Ito, Yihan Zhu, Mahmoud Abdelrahman & Filip Biljecki

  3. Department of Real Estate, National University of Singapore, Singapore, Singapore

    Filip Biljecki

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Contributions

M.Q.: conceptualization, methodology, software, validation, formal analysis, investigation, data curation, writing–original draft, visualization, project administration. Y.G.: methodology, investigation, software, writing–review and editing. X.L.: methodology, visualization, writing–review and editing. Y.H.: methodology, data curation, writing–review and editing. K.I.: methodology, software, writing–review and editing. Y.Z.: writing–review and editing. M.A.: writing–review and editing. F.B.: conceptualization, methodology, resources, writing–review and editing, supervision, funding acquisition.

Corresponding author

Correspondence to Filip Biljecki.

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Nature Cities thanks Deepank Verma, Yu Ye and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Statistical difference of perception scores by nested demographic.

Perception Q scores are calculated from ratings by participants in each nested group (combinations of ‘gender × age group × AHI’) for all (without location grouping) and each location. We performed the Games-Howell post-hoc test and the minimum sample size n (rated images with at least four pairwise comparison by participants per group) is shown for each demographic profile. Locations with no significant differences in any indicator and nested demographic groups with fewer than n samples are not shown. Significance thresholds at p < 0.05.

Extended Data Fig. 2 Expanded Pearson correlation between our proposed four new indicators and the six indicators predominantly used in visual urban perception studies.

Expanded Pearson correlation between our proposed four new indicators (live nearby, walk, cycle, and green) and the six indicators predominantly used in visual urban perception studies (safe, lively, wealthy, beautiful, boring, and depressing). Minimum n is 277 (rated images with at least 22 pairwise comparison by participants per indicator)

Extended Data Fig. 3 Polynomial regression analyses between the proposed perceptual indicators ((a) live nearby, (b) walk, (c)cycle, and (d) green) and the six traditional ones: safe, lively, wealthy, beautiful, boring, and depressing.

Best fitting curve between linear, quadratic (normal or inverted U-shaped) and cubic, in terms of R2, are plotted in red. Linear fitting curve is shown alongside the best fitting 18model with R values. Minimum n is 277 (rated images with at least 22 pairwise comparison by participants per indicator)

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Quintana, M., Gu, Y., Liang, X. et al. Global urban visual perception varies across demographics and personalities. Nat Cities 2, 1092–1106 (2025). https://doi.org/10.1038/s44284-025-00330-x

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