Poverty has been studied across many social science disciplines, resulting in a large body of literature. Scholars of poverty research have long recognized that the poor are not uniformly distributed across space. Understanding the spatial aspect of poverty is important because it helps us understand place-based structural inequalities. There are many spatial regression models, but there is a learning curve to learn and apply them to poverty research. This manuscript aims to introduce the concepts of spatial regression modeling and walk the reader through the steps of conducting poverty research using R: standard exploratory data analysis, standard linear regression, neighborhood structure and spatial weight matrix, exploratory spatial data analysis, and spatial linear regression. We also discuss the spatial heterogeneity and spatial panel aspects of poverty. We provide code for data analysis in the R environment and readers can modify it for their own data analyses. We also present results in their raw format to help readers become familiar with the R environment.

中文翻译：

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R中贫困的空间回归分析

贫穷已在许多社会科学学科中得到研究，从而产生了大量文学作品。贫困研究的学者们早就认识到，贫困人口并不是均匀分布在整个太空中的。了解贫困的空间方面很重要，因为它有助于我们理解基于地点的结构性不平等。空间回归模型很多，但是有一条学习曲线可以学习并将其应用于贫困研究。该手稿旨在介绍空间回归建模的概念，并引导读者完成使用R进行贫困研究的步骤：标准探索性数据分析，标准线性回归，邻域结构和空间权重矩阵，探索性空间数据分析和空间线性回归。我们还将讨论贫困的空间异质性和空间面板方面。我们提供用于R环境中数据分析的代码，读者可以对其进行修改以用于自己的数据分析。我们还以原始格式显示结果，以帮助读者熟悉R环境。