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Loose programming of GIS workflows with geo-analytical concepts
Johannes F Kruiger1, Vedran Kasalica2, Rogier Meerlo1
1Department of Human Geography and Spatial Planning Utrecht University Utrecht the Netherlands.
Loose programming with core concept data types (CCD) improves geo-analytical workflow synthesis. This semantic type system enhances precision by specifying GIS functions, leading to higher quality analytical tasks.
Area of Science:
- Geographic Information Systems (GIS)
- Data Science
- Spatial Analysis
Background:
- Traditional programming requires detailed procedural code for data transformations.
- Loose programming offers a conceptual approach, leaving data transformations underspecified.
- Existing geodata models lack the abstraction needed for high-quality geo-analytical workflow synthesis.
Purpose of the Study:
- To evaluate the impact of core concept data types (CCD) on the precision and quality of automatically synthesized geo-analytical workflows.
- To demonstrate the application of a CCD ontology for constraining GIS functions in workflow synthesis.
Main Methods:
- Annotated GIS tools with CCD ontology to represent geo-analytical concepts and intentions.
- Synthesized workflows for common analytical tasks within an urban livability scenario.
- Measured workflow quality against a benchmark derived from common data types.
Main Results:
- Core concept data types (CCD) significantly enhance the precision of synthesized geo-analytical workflows.
- The CCD ontology provides a suitable abstraction level for generalizing across GIS implementations.
- Workflow synthesis using CCD concepts leads to demonstrably higher quality analytical outputs.
Conclusions:
- CCD ontology is effective in improving the precision and quality of automated geo-analytical workflow synthesis.
- This approach advances loose programming by enabling concept-based workflow generation in GIS.
- The findings support the utility of semantic type systems for sophisticated spatial data analysis.
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