An Improved Spatial Weights Matrix Construction Strategy

In the study of geographical spatial autocorrelation, the spatial weights matrix is regarded as a fundamental and essential field area of research. In this paper, we firstly analyze the advantages and disadvantages of several common weights matrixes like ROOK matrix, QUEEN matrix and K-Nearest matrix. Considering the improvements for in the traditional spatial weights matrix, we put forward RLA (Ratio of Length and Area) which isthat can be applied with a stricter measurement standards and representsing the relative relationship of a spatial unit to other adjacent ones units instead of a simple true or false discrimination to improve the accuracy. For verification, we carry carried out experiments on the Mainland China viral hepatitis statistical data from 2004 to 2012 of Mainland China based on the provincial administrative divisions. The rResults indicate that the proposed weights matrix not only achieves the fundamental functions of a spatial weights matrix, but also is treated as a general definition of ROOK matrix, freely applicable to when implementing spatial autocorrelation analysis. The adoption of this RLA spatial weights matrix will further reveal the spatial relationships among different geographical units, also providing support for the prevention of epidemics.