The drug-related problem poses a serious threat to human health and safety. Previous studies have associated drug places with factors related to place management and accessibility, often at several scattered places, as data at the micro level are hard to obtain at a city-wide scale. Google Street View imagery presents a new source for deriving micro built environment characteristics, including place management and accessibility in larger areas. In this study, we calculate an overall safety score by the Streetscore algorithm and extract physical elements at the address location by the Pyramid Scene Parsing Network (PSPNet) model from every Google Street View image. Additionally, to distinguish drug activities from other types of crime, we compare drug-related calls for service (CFS) data with street robbery incident data. We build the binary logistic regression models to assess the impact of the micro built environment variables on drug activities after controlling for other criminological elements pertaining to drug places. Results show that the safety score, traffic lights, and poles make statistically significant and negative (or deterring) impacts on drug activities, whilst traffic signs and roads make statistically significant and positive (or contributing) impacts. The positive impact of buildings is also notable as its p-value is slightly over 0.05. This study provides evidence at the micro level that less place management and higher accessibility can increase the risk of drug activities. These street-view variables may be generally applicable to other types of crime research in the context of the micro built environment.

与毒品有关的问题对人类健康和安全构成了严重威胁。以前的研究已经将毒品场所与场所管理和可及性相关的因素相关联，这些因素通常在几个分散的地方，因为很难在整个城市范围内获取微观水平的数据。Google街景图像为推导微型建筑环境特征（包括地方管理和较大区域的可及性）提供了新的来源。在本研究中，我们通过Streetscore算法计算总体安全评分，并通过金字塔场景解析网络（PSPNet）模型从每个Google Street View图像中提取地址位置处的物理元素。此外，为了将毒品活动与其他类型的犯罪区分开来，我们将毒品相关的服务请求（CFS）数据与街头抢劫事件数据进行了比较。我们建立了二进制逻辑回归模型，在控制了与毒品场所有关的其他犯罪学要素之后，评估了微环境变量对毒品活动的影响。结果表明，安全评分，交通信号灯和电线杆对毒品活动产生了统计上的显着和负面（或威慑）影响，而交通标志和道路在统计学上产生了显着的正面和（或贡献）影响。建筑物的积极影响也很明显，因为它 而交通标志和道路在统计上会产生重大影响并带来积极（或促成）影响。建筑物的积极影响也很明显，因为它 而交通标志和道路在统计上会产生重大影响并带来积极（或促成）影响。建筑物的积极影响也很明显，因为它p值略高于0.05。这项研究在微观水平上提供了证据，即较少的场所管理和较高的可及性会增加毒品活动的风险。这些街景变量通常可以在微型建筑环境中应用于其他类型的犯罪研究。