We present a method to capture groupings of similar calls and determine their relative spatial distribution from a collection of crime record narratives. We first obtain a topic distribution for each narrative, and then propose a nearest neighbors relative density estimation (kNN-RDE) approach to obtain spatial relative densities per topic. Experiments over a large corpus ($n=475,019$) of narrative documents from the Atlanta Police Department demonstrate the viability of our method in capturing geographic hot-spot trends which call dispatchers do not initially pick up on and which go unnoticed due to conflation with elevated event density in general.

翻译：我们提出了一种方法，用于从犯罪记录叙述集合中捕捉相似警情的分组并确定其相对空间分布。首先获取每条叙述的主题分布，进而提出一种基于k近邻的相对密度估计（kNN-RDE）方法，以计算每个主题的空间相对密度。通过对亚特兰大警局大型语料库（$n=475,019$）叙述文档的实验，验证了该方法在捕捉地理热点趋势方面的可行性——这些趋势因通常与整体事件密度增高混淆而未被警情调度员初始察觉并长期被忽视。