Objectives

We illustrate how a machine learning algorithm, Random Forests, can provide accurate long-term predictions of crime at micro places relative to other popular techniques. We also show how recent advances in model summaries can help to open the ‘black box’ of Random Forests, considerably improving their interpretability.

Methods

We generate long-term crime forecasts for robberies in Dallas at 200 by 200 feet grid cells that allow spatially varying associations of crime generators and demographic factors across the study area. We then show how using interpretable model summaries facilitate understanding the model’s inner workings.

Results

We find that Random Forests greatly outperform Risk Terrain Models and Kernel Density Estimation in terms of forecasting future crimes using different measures of predictive accuracy, but only slightly outperform using prior counts of crime. We find different factors that predict crime are highly non-linear and vary over space.

Conclusions

We show how using black-box machine learning models can provide accurate micro placed based crime predictions, but still be interpreted in a manner that fosters understanding of why a place is predicted to be risky.

中文翻译：

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使用机器学习绘制犯罪风险地形图

目标

我们说明了相对于其他流行技术，机器学习算法Random Forests如何在微观场所提供准确的长期犯罪预测。我们还将展示模型摘要的最新进展如何帮助打开随机森林的“黑匣子”，从而大大提高其可解释性。

方法

我们会针对200到200英尺网格单元中达拉斯的抢劫案生成长期犯罪预测，从而使整个研究区域的犯罪产生者和人口统计因素在空间上具有不同的关联。然后，我们说明如何使用可解释的模型摘要来促进对模型内部工作的理解。

结果

我们发现，在使用不同的预测准确性度量来预测未来犯罪方面，随机森林的性能大大超过了“风险地形模型”和“内核密度估计”，但在使用先验犯罪数量的情况下，其性能仅略胜一筹。我们发现预测犯罪的不同因素之间存在高度的非线性关系，并且随着空间的变化而变化。

结论

我们展示了如何使用黑匣子机器学习模型来提供准确的基于微场所的犯罪预测，但仍以一种有助于理解为何预测该场所具有风险的方式进行解释。