Exploring associations between streetscape factors and crime behaviors using Google Street View images
Exploring associations between streetscape factors and crime behaviors using Google Street View images
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使用 Google 街景图像探索街景因素与犯罪行为之间的关联
DOI：
10.1007/s11704-020-0007-z
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发表时间：
2022-08-01
影响因子：
4.2
通讯作者：
Liu, Chenxi
中科院分区：
文献类型：
--
作者：
Deng, Mingyu;Yang, Wei;Liu, Chenxi
了解城市街景对犯罪的影响机制，对于预防犯罪和城市管理具有重要意义。近年来，深度学习技术和街景图像大数据的发展，使得定量探讨街景与犯罪之间的关系成为可能。本研究首先利用Google街景图像计算了街道建成环境的8个街景指数。然后，利用泊松回归模型和地理加权泊松回归模型揭示了这八个指标与犯罪事件之间的关联。实验在纽约的市中心和住宅区曼哈顿进行。总体回归结果表明，机动化指数对犯罪的影响是显著的、正的，而光观指数和绿观指数对犯罪的影响在很大程度上取决于社会经济因素。从局部角度看，步行空间指数、绿色景观指数、灯光景观指数和机动化指数对犯罪有显著的空间影响，而由于社会经济、文化和街景要素的组合差异，相同的视觉街景要素对不同街道的影响不同。影响特定犯罪活动的特定街道的关键街景元素可以根据关联的强度来确定。研究结果为犯罪理论和犯罪预防工作提供了理论和实践上的启示。
Understanding the influencing mechanism of the urban streetscape on crime is fairly important to crime prevention and urban management. Recently, the development of deep learning technology and big data of street view images, makes it possible to quantitatively explore the relationship between streetscape and crime. This study computed eight streetscape indexes of the street built environment using Google Street View images firstly. Then, the association between the eight indexes and recorded crime events was revealed with a poisson regression model and a geographically weighted poisson regression model. An experiment was conducted in downtown and uptown Manhattan, New York. Global regression results show that the influences ofMotorization Indexon crimes are significant and positive, while the effects of theLight View IndexandGreen View Indexon crimes depend heavily on the socioeconomic factors. From a local perspective, thePedestrian Space Index, Green View Index, Light View IndexandMotorization Indexhave a significant spatial influence on crimes, while the same visual streetscape factors have different effects on different streets due to the combination differences of socioeconomic, cultural and streetscape elements. The key streetscape elements of a given street that affect a specific criminal activity can be identified according to the strength of the association. The results provide both theoretical and practical implications for crime theories and crime prevention efforts.