Self Stella, Overby Anna, Zgodic Anja, White David, McLain Alexander, Dyckman Caitlin
Arnold School of Public Health, University of South Carolina, 921 Assembly Street, Columbia, SC 29208, USA.
College of Architecture, Arts and Humanities, Clemson University, Fernow Street, Clemson, SC 29634, USA.
Spat Stat. 2023 Jun;55. doi: 10.1016/j.spasta.2023.100757. Epub 2023 May 19.
Spatial clustering detection has a variety of applications in diverse fields, including identifying infectious disease outbreaks, pinpointing crime hotspots, and identifying clusters of neurons in brain imaging applications. Ripley's K-function is a popular method for detecting clustering (or dispersion) in point process data at specific distances. Ripley's K-function measures the expected number of points within a given distance of any observed point. Clustering can be assessed by comparing the observed value of Ripley's K-function to the expected value under complete spatial randomness. While performing spatial clustering analysis on point process data is common, applications to areal data commonly arise and need to be accurately assessed. Inspired by Ripley's K-function, we develop the and use it to develop a hypothesis testing procedure for the detection of spatial clustering and dispersion at specific distances in areal data. We compare the performance of the proposed PAPF hypothesis test to that of the global Moran's I statistic, the Getis-Ord general G statistic, and the spatial scan statistic with extensive simulation studies. We then evaluate the real-world performance of our method by using it to detect spatial clustering in land parcels containing conservation easements and US counties with high pediatric overweight/obesity rates.
空间聚类检测在多个不同领域有多种应用，包括识别传染病爆发、确定犯罪热点以及在脑成像应用中识别神经元簇。Ripley's K函数是一种用于检测点过程数据在特定距离处的聚类（或离散）的常用方法。Ripley's K函数测量任何观测点给定距离内的预期点数。可以通过将Ripley's K函数的观测值与完全空间随机性下的预期值进行比较来评估聚类情况。虽然对点过程数据进行空间聚类分析很常见，但对区域数据的应用也经常出现且需要准确评估。受Ripley's K函数的启发，我们开发了[具体内容缺失]并使用它来开发一种假设检验程序，用于检测区域数据在特定距离处的空间聚类和离散情况。我们通过广泛的模拟研究，将所提出的PAPF假设检验的性能与全局莫兰指数统计量、Getis-Ord一般G统计量以及空间扫描统计量的性能进行比较。然后，我们通过使用该方法检测包含保护地役权的地块和小儿超重/肥胖率高的美国县中的空间聚类，来评估我们方法的实际性能。
