Reclaiming independence in spatial-clustering datasets: A series of data-driven spatial weights matrices.

ABSTRACT: Most spatial models include a spatial weights matrix (W) derived from the first law of geography to adjust the spatial dependence to fulfill the independence assumption. In various fields such as epidemiological and environmental studies, the spatial dependence often shows clustering (or geographic discontinuity) due to natural or social factors. In such cases, adjustment using the first-law-of-geography-based W might be inappropriate and leads to inaccuracy estimations and loss of statistical power. In this work, we propose a series of data-driven Ws (DDWs) built following the spatial pattern identified by the scan statistic, which can be easily carried out using existing tools such as SaTScan software. The DDWs take both the clustering (or discontinuous) and the intuitive first-law-of-ge

SUBMITTER: Wang W 

PROVIDER: S-EPMC9313839 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature