Center of Geoinformatics for Public Security, School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China.
Department of Geography, University of Cincinnati, Cincinnati, OH 45221, USA.
Int J Environ Res Public Health. 2022 Oct 24;19(21):13833. doi: 10.3390/ijerph192113833.
Street crime is a common social problem that threatens the security of people's lives and property. Understanding the influencing mechanisms of street crime is an essential precondition for formulating crime prevention strategies. Widespread concern has contributed to the development of streetscape environment features as they can significantly affect the occurrence of street crime. Emerging street view images are a low-cost and highly accessible data source. On the other hand, machine-learning models such as XGBoost (eXtreme Gradient Boosting) usually have higher fitting accuracies than those of linear regression models. Therefore, they are popular for modeling the relationships between crime and related impact factors. However, due to the "black box" characteristic, researchers are unable to understand how each variable contributes to the occurrence of crime. Existing research mainly focuses on the independent impacts of streetscape environment features on street crime, but not on the interaction effects between these features and the community socioeconomic conditions and their local variations. In order to address the above limitations, this study first combines street view images, an objective detection network, and a semantic segmentation network to extract a systematic measurement of the streetscape environment. Then, controlling for socioeconomic factors, we adopted the XGBoost model to fit the relationships between streetscape environment features and street crime at the street segment level. Moreover, we used the SHAP (Shapley additive explanation) framework, a post-hoc machine-learning explainer, to explain the results of the XGBoost model. The results demonstrate that, from a global perspective, the number of people on the street, extracted from street view images, has the most significant impact on street property crime among all the street view variables. The local interpretability of the SHAP explainer demonstrates that a particular variable has different effects on street crime at different street segments. The nonlinear associations between streetscape environment features and street crime, as well as the interaction effects of different streetscape environment features are discussed. The positive effect of the number of pedestrians on street crime increases with the length of the street segment and the number of crime generators. The combination of street view images and interpretable machine-learning techniques is helpful in better accurately understanding the complex relationships between the streetscape environment and street crime. Furthermore, the readily comprehensible results can offer a reference for formulating crime prevention strategies.
街头犯罪是一种常见的社会问题，威胁着人们的生命和财产安全。了解街头犯罪的影响机制是制定犯罪预防策略的重要前提。由于街景环境特征对街头犯罪的发生有显著影响，因此它们受到了广泛关注。新兴的街景图像是一种低成本、高可用性的数据来源。另一方面，XGBoost（极端梯度提升）等机器学习模型通常比线性回归模型具有更高的拟合精度。因此，它们常用于建立犯罪与相关影响因素之间的关系模型。然而，由于其“黑箱”特性，研究人员无法了解每个变量对犯罪发生的贡献程度。现有的研究主要集中在街景环境特征对街头犯罪的独立影响上，而没有考虑这些特征与社区社会经济条件及其局部变化之间的交互作用。为了解决上述局限性，本研究首先结合街景图像、目标检测网络和语义分割网络，提取街景环境的系统测量值。然后，在控制社会经济因素的情况下，我们采用 XGBoost 模型拟合街景环境特征与街道犯罪之间的关系，且在街道段层面进行分析。此外，我们使用 SHAP（Shapley 可加性解释）框架，一种事后机器学习解释器，来解释 XGBoost 模型的结果。结果表明，从全局角度来看，从街景图像中提取的街道上的人数对所有街景变量中街道财产犯罪的影响最大。SHAP 解释器的局部可解释性表明，特定变量对不同街道段的街道犯罪有不同的影响。讨论了街景环境特征与街道犯罪之间的非线性关系以及不同街景环境特征之间的交互作用。行人数量对街道犯罪的积极影响随着街道段的长度和犯罪源数量的增加而增加。街景图像与可解释机器学习技术的结合有助于更好地理解街景环境与街道犯罪之间的复杂关系。此外，易于理解的结果可为制定犯罪预防策略提供参考。
