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.

自Bivand和Gebhardt（J Geogr Syst 2（3）：307-317，2000.https：//doi.org/10.1007/PL00011460）指出，当时新生的开源R编程语言和环境以及研究人员分析空间数据的需求。回顾Bivand等人以书本形式提出的空间数据类的发展。（R. Springer的应用空间数据分析，纽约，2008，R的应用空间数据分析，第二版，Springer，纽约，2013年），重要的是要呈现当前在空间数据表示中正在发生的进展，并且可能空间数据处理和空间数据统计分析的后果。超出此，