RS
Rafael Schouten
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Spatial data science languages
Commonalities and needs
Review
(2026)
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Edzer Pebesma, Martin Fleischmann, Josiah Parry, Jakub Nowosad, Anita Graser, Dewey Dunnington, Maarten Pronk, Rafael Schouten, Robin Lovelace, More authors...
Recent workshops brought together several developers, educators and users of software packages extending popular languages for spatial data handling, with a primary focus on R, Python and Julia. Common challenges discussed included handling of spatial or spatio-temporal support, geodetic coordinates, in-memory vector data formats, data cubes, inter-package dependencies, packaging upstream libraries, differences in habits or conventions between the GIS and physical modeling communities, and statistical models. The following set of recommendations have been formulated:
(i) considering software problems across data science language silos helps to understand and standardise analysis approaches, also outside the domain of formal standardisation bodies;
(ii) whether attribute variables have block or point support, and whether they are spatially intensive or extensive has consequences for permitted operations, and hence for software implementing those;
(iii) handling geometries on the sphere rather than on the flat plane requires modifications to the logic of simple features,
(iv) managing communities and fostering diversity is a necessary, on-going effort, and
(v) tools for cross-language development need more attention and support. ...
(i) considering software problems across data science language silos helps to understand and standardise analysis approaches, also outside the domain of formal standardisation bodies;
(ii) whether attribute variables have block or point support, and whether they are spatially intensive or extensive has consequences for permitted operations, and hence for software implementing those;
(iii) handling geometries on the sphere rather than on the flat plane requires modifications to the logic of simple features,
(iv) managing communities and fostering diversity is a necessary, on-going effort, and
(v) tools for cross-language development need more attention and support. ...
Recent workshops brought together several developers, educators and users of software packages extending popular languages for spatial data handling, with a primary focus on R, Python and Julia. Common challenges discussed included handling of spatial or spatio-temporal support, geodetic coordinates, in-memory vector data formats, data cubes, inter-package dependencies, packaging upstream libraries, differences in habits or conventions between the GIS and physical modeling communities, and statistical models. The following set of recommendations have been formulated:
(i) considering software problems across data science language silos helps to understand and standardise analysis approaches, also outside the domain of formal standardisation bodies;
(ii) whether attribute variables have block or point support, and whether they are spatially intensive or extensive has consequences for permitted operations, and hence for software implementing those;
(iii) handling geometries on the sphere rather than on the flat plane requires modifications to the logic of simple features,
(iv) managing communities and fostering diversity is a necessary, on-going effort, and
(v) tools for cross-language development need more attention and support.
(i) considering software problems across data science language silos helps to understand and standardise analysis approaches, also outside the domain of formal standardisation bodies;
(ii) whether attribute variables have block or point support, and whether they are spatially intensive or extensive has consequences for permitted operations, and hence for software implementing those;
(iii) handling geometries on the sphere rather than on the flat plane requires modifications to the logic of simple features,
(iv) managing communities and fostering diversity is a necessary, on-going effort, and
(v) tools for cross-language development need more attention and support.