R Packages for Analyzing Spatial Data: A Comparative Case Study with Areal Data
2022/02/06 by Roger Bivand · 372 citations
Decision Sciences · Economics, Econometrics and Finance · Environmental Science · #Artificial intelligence #Computer science #Data mining #Data science #Data set #Database #Divergence (linguistics) #Exploratory data analysis #Geography #Key (lock) #Land Use and Ecosystem Services #Programming language #Scope (computer science) #Set (abstract data type) #Software #Spatial analysis #Spatial and Panel Data Analysis #Visualization #Workflow #demographic modeling and climate adaptation
paper · doi:10.1111/gean.12319
published in Geographical Analysis 54(3), 488-518 (Wiley)
openalex publication_date 2022/02/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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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