论文标题
通过主题建模和相对密度估计，犯罪热点建模
Crime Hot-Spot Modeling via Topic Modeling and Relative Density Estimation
论文作者
论文摘要
我们提出了一种捕获类似呼叫的分组并从犯罪记录叙事集中确定其相对空间分布的方法。我们首先获得每个叙述的主题分布，然后提出最近的邻居相对密度估计（KNN-RDE）方法，以获得每个主题的空间相对密度。来自亚特兰大警察局的叙事文档的大型语料库（$ n = 475,019美元）的实验表明，我们的方法可生存在捕获地理热点趋势方面的可行性，这些趋势最初并未接收到这些趋势，并且由于总体上与升高的事件密度相结合而导致这些趋势没有被忽视。
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.
