We analyze a large corpus of police incident narrative documents in
understanding the spatial distribution of the topics. The motivation for doing
this is that police narratives in each incident report contains very
fine-grained information that is richer than the category that is manually
assigned by the police. Our approach is to split the corpus into topics using
two different unsupervised machine learning algorithms - Latent Dirichlet
Allocation and Non-negative Matrix Factorization. We validate the performance
of each learned topic model using model coherence. Then, using a k-nearest
neighbors density ratio estimation (kNN-DRE) approach that we propose, we
estimate the spatial density ratio per topic and use this for data discovery
and analysis of each topic, allowing for insights into the described incidents
at scale. We provide a qualitative assessment of each topic and highlight some
key benefits for using our kNN-DRE model for estimating spatial trends.

本文运用无监督机器学习算法 —— 隐含狄利克雷分配和非负矩阵分解，分析了一个大型警察事件叙述文件的主题分布，并提出了一种 k - 最近邻密度比估计方法来评估每个主题的空间密度比，从而揭示其中的趋势及规律。