Spatial weights matrices used in quantitative geography furnish maps with their individual latent eigenvectors, whose geographic distributions portray distinct spatial autocorrelation (SA) components. These polygon patterns on maps have specific meaning, partially in terms of geographic scale, which this article describes. The goal of this description is to enable spatial analysts to better understand and interpret these maps individually, as well as mixtures of them, when accounting for SA in a spatial analysis. Linear combinations of Moran eigenvector maps supply a powerful and relatively simple tool that can explain SA in regression residuals, with an ability to render reasonably accurate reproductions of empirical geographic distributions with or without the aid of substantive covariates. The focus of this article is positive SA, the most commonly encountered nature of autocorrelation in georeferenced data. The principal innovative contribution of this article is establishing a better clarification of what the synthetic SA variates extracted from spatial weights matrices epitomize with regard to global, regional, and local clusters of similar values on a map. This article shows that the Getis-Ord G_i* statistic provides a useful tool for classifying Moran eigenvector maps into these three qualitative categories, illustrating findings with a range of specimen geographic landscapes.

定量地理学中使用的空间权重矩阵为地图提供了它们各自的潜在特征向量，其地理分布描绘了不同的空间自相关 (SA) 分量。地图上的这些多边形图案具有特定的含义，部分是在本文描述的地理比例方面。此描述的目的是使空间分析人员在空间分析中考虑 SA 时，能够更好地理解和解释这些单独的地图以及它们的混合地图。Moran 特征向量图的线性组合提供了一种强大且相对简单的工具，可以解释回归残差中的 SA，能够在有或没有实质性协变量的帮助下合理准确地再现经验地理分布。这篇文章的重点是正面SA，地理参考数据中最常见的自相关性质。本文的主要创新贡献是更好地阐明从空间权重矩阵中提取的合成 SA 变量在地图上具有相似值的全局、区域和局部集群的缩影。本文显示 Getis-Ord G_i * 统计提供了一个有用的工具，可将 Moran 特征向量图分为这三个定性类别，用一系列样本地理景观说明发现。