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SC5.9

Effective gridded spatial data aggregation and analysis with Discrete Global Grid Systems on the example of Uber H3

Convener: Alexander KmochECSECS | Co-conveners: Holger VirroECSECS, Evelyn Uuemaa

The main objective this workshop is introducing the attendees to practical use cases for Discrete Global Grid Systems (DGGS) for spatial data aggregation and analysis. After a short background on current real-world software implementations with exemplary use cases, we walk through an interactive exploration of solving traditional GIS and spatial analysis challenges with a hexagonal DGGS on the example of Uber H3. H3 is a software library that, besides many other programming languages, can be used in Python. We demonstrate grid generation, data indexing and sampling in a unified Jupyter notebook. We apply spatial analysis methods that exploit the specific grid properties and discuss eventually DGGS for datacube applications.The main objective of this workshop is to introduce the attendees to practical use cases for Discrete Global Grid Systems (DGGS) for spatial data aggregation and analysis. While still in gridded form, DGGS have several topological advantages over classic raster, e.g. equal-area properties and reliable unique cell indexing. After a short background on current real-world software implementations with exemplary use cases, we walk through an interactive exploration of solving traditional GIS and spatial analysis challenges with a hexagonal DGGS on the example of Uber H3. H3 is a software library that, besides many other programming languages, can be used in Python. We demonstrate grid generation, data indexing and sampling in a unified Jupyter notebook. We apply spatial analysis methods that exploit the specific grid properties and discuss eventually DGGS for datacube applications.