R Packages for Analyzing Spatial Data: A Comparative Case Study with Areal Data

IF 3.3 3区 地球科学 Q1 GEOGRAPHY Geographical Analysis Pub Date : 2022-02-06 DOI:10.1111/gean.12319
Roger Bivand
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引用次数: 52

Abstract

The count of open source software packages hosted by the Comprehensive R Archive Network (CRAN) using key spatial data handling packages has now passed 1,000. Providing a comprehensive review of these packages is beyond the scope of an article. Consequently, this review takes the form of a comparative case study, reproducing some of the approach and workflow of a spatial analysis of a data set including almost all the census tracts in the coterminous United States. The case study moves from visualization and the construction of a spatial weights matrix, to exploratory spatial data analysis and spatial regression. For comparison, implementations of the same steps in PySAL and GeoDa are interwoven, and points of convergence and divergence noted and discussed. Conclusions are drawn about the usefulness of open source software, the significance of sharing contributions both in software implementation but also more broadly in reproducible research, and in opportunities for exchanging ideas and solutions with other research domains.

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分析空间数据的R包:与区域数据的比较案例研究
由综合R档案网络(CRAN)托管的使用关键空间数据处理包的开源软件包的数量现已超过1000个。提供对这些包的全面回顾超出了本文的范围。因此,这次审查采取了比较个案研究的形式,再现了对一组数据进行空间分析的一些方法和工作流程,其中包括几乎所有毗邻美国的人口普查区。案例研究从可视化和空间权重矩阵的构建,到探索性的空间数据分析和空间回归。为了进行比较,在PySAL和GeoDa中对相同步骤的实现进行了交织,并注意和讨论了收敛点和分歧点。结论是关于开源软件的有用性,在软件实现以及更广泛的可重复研究中分享贡献的重要性,以及与其他研究领域交换想法和解决方案的机会。
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来源期刊
CiteScore
8.70
自引率
5.60%
发文量
40
期刊介绍: First in its specialty area and one of the most frequently cited publications in geography, Geographical Analysis has, since 1969, presented significant advances in geographical theory, model building, and quantitative methods to geographers and scholars in a wide spectrum of related fields. Traditionally, mathematical and nonmathematical articulations of geographical theory, and statements and discussions of the analytic paradigm are published in the journal. Spatial data analyses and spatial econometrics and statistics are strongly represented.
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