Objectives

Crime diversity is a measure of the variety of criminal offenses in a local environment, similar to ecological diversity. While crime diversity distributions have been explained via neutral models, to date the environmental and social mechanisms behind crime diversity have not been investigated. Building on recent work demonstrating that crime rates can be inferred from street level imagery with neural network computer vision models, in this paper we consider the task of inferring crime diversity through street level imagery.

Methods

We use the Google Vision API, a deep learning image tagging service, to extract objects from sampled Google Street View (GSV) images in each census block of Los Angeles. For each census block we then compute indices for (1) object diversity, (2) diversity related to commonly employed census variables, and (3) crime diversity from reports provided by the Los Angeles Police Department. We then build ordinary least squares and geographically weighted regression models to explain crime diversity as a function of environmental diversity, population diversity, and population size.

Results

We show that crime diversity arises via a combination of environmental diversity (as measured through street view object diversity), household diversity (as measured through the census), and population size. Population size and area of the census block both lend credence to the neutral model proposed by Brantingham for crime diversity. However, environmental and demographic diversity combined play an equally important role in explaining variation in crime diversity.

Conclusions

Our study has two primary implications for research on crime and place. First, Google Street View (via the Google Vision API) can provide important, cost-effective empirical insights to best understand distinct geographic environments of crime. Second, environmental diversity, as measured by image tagging in GSV, was observed to be more predictive of crime diversity (variety of crime types) than commonly used census measures.

中文翻译：

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使用Google Street View解释犯罪多样性

目标

犯罪多样性是对当地环境中各种犯罪行为的一种度量，类似于生态多样性。虽然已经通过中性模型解释了犯罪多样性的分布，但迄今为止，尚未研究犯罪多样性背后的环境和社会机制。在最近的研究表明可以使用神经网络计算机视觉模型从街道图像推断犯罪率的基础上，本文考虑了通过街道图像推断犯罪多样性的任务。

方法

我们使用Google Vision API（一种深度学习图像标记服务）从洛杉矶每个人口普查区的采样Google Street View（GSV）图像中提取对象。然后，对于每个人口普查区块，我们根据洛杉矶警察局提供的报告为以下各项计算指标：（1）对象多样性，（2）与常用普查变量相关的多样性以及（3）犯罪多样性。然后，我们建立普通的最小二乘和地理加权回归模型，以将犯罪多样性解释为环境多样性，人口多样性和人口规模的函数。

结果

我们表明，犯罪多样性是通过环境多样性（通过街景对象多样性衡量），家庭多样性（通过人口普查衡量）和人口规模的组合而产生的。人口规模和人口普查面积都阻碍了布兰廷汉为犯罪多样性提出的中立模型。但是，环境和人口多样性的结合在解释犯罪多样性的变化中起着同等重要的作用。

结论

我们的研究对于犯罪和场所研究有两个主要含义。首先，Google街景视图（通过Google Vision API）可以提供重要的，具有成本效益的经验见解，以最好地了解犯罪的不同地理环境。第二，通过在GSV中通过图像标记来衡量，环境多样性被认为比常用的普查手段更能预测犯罪多样性（犯罪类型的多样性）。