In this article, a new method called spatial amplifier filtering is proposed. The presented method is related to Moran eigenvector filtering and allows the accentuation of spatial structures in heterogeneous data sets. The spatial amplifier filtering technique is based on the inclusion of certain eigenvectors of a spatial weights matrix into a regression model. The application of this method can be seen as a pre-processing step prior to subsequent analyses, and to separate different types of spatially correlated components in a data set. For this purpose, three different types of the so-called spatial amplifiers are proposed, each consisting of different subsets of eigenvectors of the weights matrix. These amplifiers can either emphasise the positive or negative spatial autocorrelation, or spatial structuring in general. In this way, it is possible to make desired spatial structures more visible, especially in spatially highly mixed data sets, whereby the focus here is on geosocial media data. In the empirical part of the article, it is first shown why georeferenced social media data are difficult to handle from a spatial analysis perspective, motivating the need for the method proposed. Subsequently, the technique of amplifier filtering is applied to two data sets: a census data set from Brazil and Twitter data from London. The results obtained show that the method is capable of strengthening existing spatial structures and mitigating potentially disturbing spatial randomness patterns and other nuisances. This facilitates the interpretation especially of the Twitter data used. While the analysis of the unfiltered Twitter data with established methods reveals little information about possible spatial structures in the tweets, the filtered data offer a much clearer picture with distinguishable clusters. In addition, the method also provides insights into the internal irregularity of spatial clusters and thus complements the toolbox for investigating spatial heterogeneity.

在本文中，提出了一种称为空间放大器滤波的新方法。提出的方法与Moran特征向量滤波有关，并允许强调异构数据集中的空间结构。空间放大器滤波技术基于将空间权重矩阵的某些特征向量包含到回归模型中。可以将这种方法的应用视为后续分析之前的预处理步骤，并将数据集中不同类型的空间相关成分分开。为此，提出了三种不同类型的所谓的空间放大器，每种由权重矩阵的特征向量的不同子集组成。这些放大器通常可以强调正或负空间自相关或空间结构。如此，可以使所需的空间结构更加可见，尤其是在空间高度混合的数据集中，其中的重点是地理社会媒体数据。在本文的实证部分中，首先显示了为什么从空间分析的角度来看，很难处理地理参考社交媒体数据，从而激发了对所提出方法的需求。随后，将放大器过滤技术应用于两个数据集：来自巴西的普查数据集和来自伦敦的Twitter数据。获得的结果表明，该方法能够加强现有的空间结构，并减轻潜在的干扰性空间随机性模式和其他麻烦。这尤其有助于解释所使用的Twitter数据。尽管使用已建立的方法对未过滤的Twitter数据进行分析后，发现有关推文中可能的空间结构的信息很少，但过滤后的数据提供了具有明显群集的更清晰画面。另外，该方法还提供了对空间簇内部不规则性的见解，从而补充了用于研究空间异质性的工具箱。