街面犯罪对公众的生活安全构成一定的威胁。以往对于公共盗窃和寻衅滋事等街面犯罪的研究往往停留在社区甚至更宏观的层面,难以向微观尺度深入,它们忽略了通过环境设计预防犯罪(Crime Prevention Through Environmental Design,CPTED)...街面犯罪对公众的生活安全构成一定的威胁。以往对于公共盗窃和寻衅滋事等街面犯罪的研究往往停留在社区甚至更宏观的层面,难以向微观尺度深入,它们忽略了通过环境设计预防犯罪(Crime Prevention Through Environmental Design,CPTED)理论中所主张的地址级的建成环境的精确特征。地址级的微观建成环境被广泛认为对各类犯罪的发生有着直接或间接的影响,然而对微观建成环境的度量一直是一个挑战。先前的大多数研究都是通过调查样本来表征建成环境,会受到两方面的限制:(1)建成环境特征描述不完整的限制;(2)数据在空间覆盖方面具有稀疏性的限制。百度街景图像作为一个新的数据来源,可以被用来提取地址级的微型环境的建成特征,从而使犯罪研究可以聚焦在更微观的尺度中。本研究使用深度学习全卷积图像分割算法从百度街景图像中提取地理位置的环境变量,共选取树木、通车道路、人行道等8种变量来表现研究区微观建成环境的差异。在控制了与街面犯罪有关的其他因素后,采用贝叶斯逻辑回归模型来评估微观建成环境影响因素对公共盗窃和寻衅滋事案件的影响。结果表明加入了微观建成环境物理特征之后的模型表现更好。对比寻衅滋事案件,树木多的隐蔽地方更容易发生公共盗窃案件,通车道路、人行道多的地方更不易发公共盗窃案件,这也说明了更隐秘的地方公共盗窃案多发。总的来说,全卷积深度学习图像分割算法可以有效地提取街景衍生变量,这些变量为微观空间尺度的犯罪分析增加了新的维度。本研究不仅对于犯罪地理文献具有贡献,而且为基于CPTED原则的犯罪预防提供了新的视角。展开更多
In recent years,the police intervention strategy“Hot spots policing”has been effective in combating crimes.However,as cities are under the intense pressure of increasing crime and scarce police resources,police patr...In recent years,the police intervention strategy“Hot spots policing”has been effective in combating crimes.However,as cities are under the intense pressure of increasing crime and scarce police resources,police patrols are expected to target more accurately at finer geographic units rather than ballpark“hot spot”areas.This study aims to develop an algorithm using geographic information to detect crime patterns at street level,the so-called“hot street”,to further assist the Criminal Investigation Department(CID)in capturing crime change and transitive moments efficiently.The algorithm applies Kernel Density Estimation(KDE)technique onto street networks,rather than traditional areal units,in one case study borough in London;it then maps the detected crime“hot streets”by crime type.It was found that the algorithm could successfully generate“hot street”maps for Law Enforcement Agencies(LEAs),enabling more effective allocation of police patrolling;and bear enough resilience itself for the Strategic Crime Analysis(SCA)team’s sustainable utilization,by either updating the inputs with latest data or modifying the model parameters(i.e.the kernel function,and the range of spillover).Moreover,this study explores contextual characteristics of crime“hot streets”by applying various regression models,in recognition of the best fitted Geographically Weighted Regression(GWR)model,encompassing eight significant contextual factors with their varied effects on crimes at different streets.Having discussed the impact of lockdown on crime rates,it was apparent that the land-use driven mobility change during lockdown was a fundamental reason for changes in crime.Overall,these research findings have provided evidence and practical suggestions for crime prevention to local governors and policy practitioners,through more optimal urban planning(e.g.Low Traffic Neighborhoods),proactive policing(e.g.in the listed top 10“Hot Streets”of crime),publicizing of laws and regulations,and installations of security infras展开更多
文摘街面犯罪对公众的生活安全构成一定的威胁。以往对于公共盗窃和寻衅滋事等街面犯罪的研究往往停留在社区甚至更宏观的层面,难以向微观尺度深入,它们忽略了通过环境设计预防犯罪(Crime Prevention Through Environmental Design,CPTED)理论中所主张的地址级的建成环境的精确特征。地址级的微观建成环境被广泛认为对各类犯罪的发生有着直接或间接的影响,然而对微观建成环境的度量一直是一个挑战。先前的大多数研究都是通过调查样本来表征建成环境,会受到两方面的限制:(1)建成环境特征描述不完整的限制;(2)数据在空间覆盖方面具有稀疏性的限制。百度街景图像作为一个新的数据来源,可以被用来提取地址级的微型环境的建成特征,从而使犯罪研究可以聚焦在更微观的尺度中。本研究使用深度学习全卷积图像分割算法从百度街景图像中提取地理位置的环境变量,共选取树木、通车道路、人行道等8种变量来表现研究区微观建成环境的差异。在控制了与街面犯罪有关的其他因素后,采用贝叶斯逻辑回归模型来评估微观建成环境影响因素对公共盗窃和寻衅滋事案件的影响。结果表明加入了微观建成环境物理特征之后的模型表现更好。对比寻衅滋事案件,树木多的隐蔽地方更容易发生公共盗窃案件,通车道路、人行道多的地方更不易发公共盗窃案件,这也说明了更隐秘的地方公共盗窃案多发。总的来说,全卷积深度学习图像分割算法可以有效地提取街景衍生变量,这些变量为微观空间尺度的犯罪分析增加了新的维度。本研究不仅对于犯罪地理文献具有贡献,而且为基于CPTED原则的犯罪预防提供了新的视角。
基金partly supported by King’s Global Engagement Partnership Fund[2020-2021#PF2021_Mar_005].
文摘In recent years,the police intervention strategy“Hot spots policing”has been effective in combating crimes.However,as cities are under the intense pressure of increasing crime and scarce police resources,police patrols are expected to target more accurately at finer geographic units rather than ballpark“hot spot”areas.This study aims to develop an algorithm using geographic information to detect crime patterns at street level,the so-called“hot street”,to further assist the Criminal Investigation Department(CID)in capturing crime change and transitive moments efficiently.The algorithm applies Kernel Density Estimation(KDE)technique onto street networks,rather than traditional areal units,in one case study borough in London;it then maps the detected crime“hot streets”by crime type.It was found that the algorithm could successfully generate“hot street”maps for Law Enforcement Agencies(LEAs),enabling more effective allocation of police patrolling;and bear enough resilience itself for the Strategic Crime Analysis(SCA)team’s sustainable utilization,by either updating the inputs with latest data or modifying the model parameters(i.e.the kernel function,and the range of spillover).Moreover,this study explores contextual characteristics of crime“hot streets”by applying various regression models,in recognition of the best fitted Geographically Weighted Regression(GWR)model,encompassing eight significant contextual factors with their varied effects on crimes at different streets.Having discussed the impact of lockdown on crime rates,it was apparent that the land-use driven mobility change during lockdown was a fundamental reason for changes in crime.Overall,these research findings have provided evidence and practical suggestions for crime prevention to local governors and policy practitioners,through more optimal urban planning(e.g.Low Traffic Neighborhoods),proactive policing(e.g.in the listed top 10“Hot Streets”of crime),publicizing of laws and regulations,and installations of security infras
