1 Center for Data Intensive Science, University of Chicago , Chicago, Illinois.
2 Computation Institute, University of Chicago , Chicago, Illinois.
Big Data. 2017 Sep;5(3):213-224. doi: 10.1089/big.2017.0028.
We introduce a method called neighbor-based bootstrapping (NB2) that can be used to quantify the geospatial variation of a variable. We applied this method to an analysis of the incidence rates of disease from electronic medical record data (International Classification of Diseases, Ninth Revision codes) for ∼100 million individuals in the United States over a period of 8 years. We considered the incidence rate of disease in each county and its geospatially contiguous neighbors and rank ordered diseases in terms of their degree of geospatial variation as quantified by the NB2 method. We show that this method yields results in good agreement with established methods for detecting spatial autocorrelation (Moran's I method and kriging). Moreover, the NB2 method can be tuned to identify both large area and small area geospatial variations. This method also applies more generally in any parameter space that can be partitioned to consist of regions and their neighbors.
我们介绍了一种名为基于邻居的自举（NB2）的方法，可用于量化变量的地理空间变化。我们将该方法应用于对美国约 1 亿个人的电子病历数据（国际疾病分类，第九版代码）中疾病发病率的分析，时间跨度为 8 年。我们考虑了每个县及其地理上相邻的县的疾病发病率，并根据 NB2 方法量化的地理空间变化程度对疾病进行了排序。我们表明，该方法的结果与检测空间自相关的已有方法（莫兰指数法和克里金法）吻合良好。此外，NB2 方法可以进行调整以识别大面积和小面积的地理空间变化。该方法还可以更广泛地应用于可以划分为区域及其邻居的任何参数空间。
