Progress in the R ecosystem for representing and handling spatial data
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Progress in the R ecosystem for representing and handling spatial data Roger S. Bivand1 Received: 9 October 2019 / Accepted: 8 September 2020 © The Author(s) 2020
Abstract Twenty years have passed since Bivand and Gebhardt (J Geogr Syst 2(3):307–317, 2000. https://doi.org/10.1007/PL00011460) indicated that there was a good match between the then nascent open-source R programming language and environment and the needs of researchers analysing spatial data. Recalling the development of classes for spatial data presented in book form in Bivand et al. (Applied spatial data analysis with R. Springer, New York, 2008, Applied spatial data analysis with R, 2nd edn. Springer, New York, 2013), it is important to present the progress now occurring in representation of spatial data, and possible consequences for spatial data handling and the statistical analysis of spatial data. Beyond this, it is imperative to discuss the relationships between R-spatial software and the larger open-source geospatial software community on whose work R packages crucially depend. Keywords Spatial data analysis · Open-source software · R programming language JEL Classification C00 · C88 · R15
1 Introduction While Bivand and Gebhardt (2000) did provide an introduction to R as a statistical programming language and to why one might choose to use a scripted language like R (or Python), this article is both retrospective and prospective. It is possible that those approaching the choice of tools for spatial analysis and for handling spatial data will find the following less than inviting; in that case, perusal of early chapters of Lovelace et al. (2019) will provide useful context. Two further pointers include the fact that R and most R add-on packages are open-source software and so without licence fees or other such restrictions. The second pointer is that all scripting * Roger S. Bivand [email protected] 1
Department of Economics, Norwegian School of Economics, Helleveien 30, 5045 Bergen, Norway
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languages provide the structures needed for reproducible research, and open-source software gives the interested researcher the means to run the scripts needed to replicate results without cost, given access to adequate hardware. Hence, an overview of the development of the use of R for handling spatial data can cast light on how and why steps fashioning today’s software were taken. Of course, an overview of the use of R for analysing spatial data would also be tempting, but, with about 900 R packages using spatial data handling classes and objects, would far exceed the bounds of a single article. The R statistical programming language and environment has been used for handling and analysing spatial data since its inception, partly building on its heritage from S and S-Plus. When the conceptualization of spatial data was introduced in the sp package (Pebesma and Bivand 2005, 2020, on the Comprehensive R Archive Network (CRAN) since 2005), it was expected that some packages would adopt its classes. Some ye
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