Algebra of core concept transformations: Procedural meta-data for geographic information

Publication date

2022-12-15

Authors

Steenbergen, NielsISNI 0000000512623827
Top, Eric JacobISNI 0000000512624408
Scheider, SimonORCID 0000-0002-2267-4810ISNI 0000000382824363
Nyamsuren, EnkboldISNI 0000000436391015

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Document Type

/dk/atira/pure/researchoutput/researchoutputtypes/workingpaper/preprint
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License

cc_by

Abstract

Transformations are essential for dealing with geographic information. They are involved not only in converting between geodata formats and reference systems, but also in turning geodata into useful information according to some purpose. However, since a transformation can be implemented in various formats and tools, its function and purpose usually remains hidden underneath the technicalities of a workflow. To automate geographic information procedures, we therefore need to model the transformations implemented by workflows on a conceptual level, as a form of procedural knowledge. Although core concepts of spatial information provide a useful level of description in this respect, we currently lack a model for the space of possible transformations between such concepts. In this article, we present the algebra of core concept transformations (CCT). It consists of a type hierarchy which models core concepts as relations, and a set of basic transformations described in terms of function signatures that use such types. Type inference allows us to enrich GIS workflows with abstract machine-readable metadata, by compiling algebraic tool descriptions. This allows us to automatically infer goal concepts across workflows and to query over such concepts across raster and vector implementations. We evaluate the algebra over a set of expert GIS workflows taken from online tutorials.

Keywords

Conceptual transformations, core concepts, geographicinformation, workflows

Citation

Steenbergen, N, Top, E, Scheider, S & Nyamsuren, E 2022 'Algebra of core concept transformations : Procedural meta-data for geographic information' OSFPREPRINTS, pp. 1-41. https://doi.org/10.31219/osf.io/j6krv