Maria Kamenetsky, Guangqing Chi, Donghui Wang, Jun Zhu
{"title":"Spatial Regression Analysis of Poverty in R.","authors":"Maria Kamenetsky, Guangqing Chi, Donghui Wang, Jun Zhu","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":43022,"journal":{"name":"Spatial Demography","volume":"7 2-3","pages":"113-147"},"PeriodicalIF":1.1000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6857788/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Spatial Demography","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2019/3/4 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"DEMOGRAPHY","Score":null,"Total":0}
引用次数: 0
Abstract
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.
许多社会科学学科都对贫困问题进行了研究,从而产生了大量文献。研究贫困问题的学者早已认识到,贫困人口在空间上的分布并不均匀。理解贫困的空间性非常重要,因为它有助于我们理解基于地方的结构性不平等。目前有许多空间回归模型,但要学习并将其应用于贫困研究,还需要一定的学习曲线。本手稿旨在介绍空间回归模型的概念,并指导读者使用 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、邻里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。我们还讨论了贫困的空间异质性和空间面板方面。我们提供了 R 环境下的数据分析代码,读者可以根据自己的数据分析对代码进行修改。我们还提供了原始格式的结果,以帮助读者熟悉 R 环境。
期刊介绍:
Spatial Demography focuses on understanding the spatial and spatiotemporal dimension of demographic processes. More specifically, the journal is interested in submissions that include the innovative use and adoption of spatial concepts, geospatial data, spatial technologies, and spatial analytic methods that further our understanding of demographic and policy-related related questions. The journal publishes both substantive and methodological papers from across the discipline of demography and its related fields (including economics, geography, sociology, anthropology, environmental science) and in applications ranging from local to global scale. In addition to research articles the journal will consider for publication review essays, book reviews, and reports/reviews on data, software, and instructional resources.