Global urban visual perception varies across demographics and personalities
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arXiv
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| Format: | Preprint |
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2025
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| author | Quintana, Matias Gu, Youlong Liang, Xiucheng Hou, Yujun Ito, Koichi Zhu, Yihan Abdelrahman, Mahmoud Biljecki, Filip |
| author_facet | Quintana, Matias Gu, Youlong Liang, Xiucheng Hou, Yujun Ito, Koichi Zhu, Yihan Abdelrahman, Mahmoud Biljecki, Filip |
| contents | 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 largescale 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 (SPECS), 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_12758 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Global urban visual perception varies across demographics and personalities Quintana, Matias Gu, Youlong Liang, Xiucheng Hou, Yujun Ito, Koichi Zhu, Yihan Abdelrahman, Mahmoud Biljecki, Filip Computer Vision and Pattern Recognition Machine Learning 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 largescale 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 (SPECS), 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. |
| title | Global urban visual perception varies across demographics and personalities |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2505.12758 |