Abstract Crime and perception of safety are two intertwined concepts affecting the quality of life and the economic development of a society. However, few studies have quantitatively examined the difference between the two due to the lack of granular data documenting public perceptions in a given geographic context. Here, by applying a pre-trained scene understanding algorithm, we infer the perception of safety score of streetscapes for census block groups in the city of Houston using a large number of Google Street View images. Then, using this inferred perception of safety, we create “perception bias” categories for each census block group. These categories capture the level of mismatch between people’s visually perceived safety and the actual crime rates. This measure provides scalable guidance in deciphering the relationship between the built environment and crime. Finally, we construct a series of models to examine the “perception bias” with static and dynamic urban factors, including socioeconomic features (e.g., unemployment rate and ethnic compositions), urban diversity (e.g., number and diversity of Points of Interest), and urban livelihood (i.e., hourly count of visitors). Analytical and numerical results suggest that the association between characteristics of urban space and “perception bias” over crime could be paradoxical. On the one hand, neighborhoods with a higher volume of day-time visitors appear more likely to be safer than it looks (low crime rate and low safety score). On the other hand, those with a higher volume of night-time visitors are likely to be more dangerous than it looks (high crime rate). The findings add further knowledge to the long-recognized relationship between built environment and crime as well as highlight the perception of safety in cities, which in turn enhances our capacity to design urban management strategies that prevent the emergence of extreme “perception bias”.

中文翻译：

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“认知偏差”：​​解读城市犯罪与安全认知之间的不匹配

摘要 犯罪和安全感是影响生活质量和社会经济发展的两个相互交织的概念。然而，由于缺乏记录特定地理环境下公众看法的细粒度数据，很少有研究对两者之间的差异进行定量研究。在这里，通过应用预训练的场景理解算法，我们使用大量谷歌街景图像推断休斯顿市人口普查街区组对街景安全评分的感知。然后，使用这种推断的安全感知，我们为每个人口普查区块组创建“感知偏差”类别。这些类别反映了人们视觉感知的安全性与实际犯罪率之间的不匹配程度。该措施为破译建筑环境与犯罪之间的关系提供了可扩展的指导。最后，我们构建了一系列模型来检验静态和动态城市因素的“感知偏差”，包括社会经济特征（例如失业率和种族构成）、城市多样性（例如兴趣点的数量和多样性）以及城市生活（即每小时的游客人数）。分析和数值结果表明，城市空间特征与对犯罪的“感知偏见”之间的关联可能是自相矛盾的。一方面，白天游客较多的社区似乎比看起来更安全（低犯罪率和低安全评分）。另一方面，那些夜间访客较多的人可能比看起来更危险（高犯罪率）。研究结果进一步加深了人们对建筑环境与犯罪之间长期公认的关系的认识，并强调了对城市安全的看法，这反过来又增强了我们设计城市管理策略的能力，以防止出现极端“感知偏差”。