研究发现不同人群对城市景观的感知差异显著，揭示了个性化设计的重要性。
Global urban visual perception varies across demographics and personalities
- 基于全球街景数据，分析5国45国籍1000人对城市视觉的感知差异
- 发现性别、年龄、收入、人格等变量显著影响对安全、美丽、绿色等6类指标评价
- 提醒智能系统需考虑本地化与人口多样性，避免算法偏见
理解人们对城市环境的偏好对城市规划至关重要，但现有方法常将多文化群体反应混为一谈，掩盖了人口差异并可能放大偏见。本研究利用街景图像在全球范围内开展大规模城市视觉感知调查，考察性别、年龄、收入、教育、种族与民族、人格特质等人口因素如何影响来自五个国家、45个国籍的1000名参与者对街道景观的感知。该数据集名为「考虑社会经济因素的城市感知评估（SPECS）」，揭示了在传统六项指标（安全、热闹、富裕、美丽、无聊、压抑）及四项新指标（就近居住、步行、骑行、绿色）上的群体与人格差异。地理位置带来的情感倾向也进一步塑造了这些偏好。基于现有全球数据集训练的机器学习模型相比人类判断，往往高估正面指标、低估负面指标，凸显本地情境的重要性。本研究旨在纠正城市感知研究中忽视人口与人格特征的片面性。
原文摘要 · Abstract (English)
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.
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