Logically, one could just take any existing DE9IM validation dataset and import it into a DGGS and perform the equivalent tests. However DGGS provides the capability to specify the precison with which to do a test, and the results can be both precision and DGGS dependent. For example:
For the pair of shapes:
Logically we would expect square.overlaps(triangle) to return True
But if we import theses shapes into a DGGS at three different levels, we might get the following:
Further the nuances of the result are both DGGS architecture dependent (hexagons vs squares etc) and DGGS RS dependent even within the same architecture.
So how do we address this in order to get a robust test suite with consistent validation results for all DGGS RSs.
Logically, one could just take any existing DE9IM validation dataset and import it into a DGGS and perform the equivalent tests. However DGGS provides the capability to specify the precison with which to do a test, and the results can be both precision and DGGS dependent. For example:
For the pair of shapes:
Logically we would expect square.overlaps(triangle) to return True
But if we import theses shapes into a DGGS at three different levels, we might get the following:
Further the nuances of the result are both DGGS architecture dependent (hexagons vs squares etc) and DGGS RS dependent even within the same architecture.
So how do we address this in order to get a robust test suite with consistent validation results for all DGGS RSs.