Spatial resolution cardinality

[jakubklimek]

In GeoDCAT-AP 3.0.0 revision, a discussion about the relations and cardinality of Spatial resolution on different classes (Dataset, Data Service, Distribution) was had, resulting in resolution of SEMICeu/GeoDCAT-AP#100.

It resulted in the following conclusions:

1. For Distribution, spatial resolution [0..1] represents the spatial resolution of the described file.​
2. For Data Service – spatial resolution [0..*] describes the capabilities of the data service, i.e. in which spatial resolutions it can serve data.​
3. For Dataset – spatial resolution [0..*] describes the spatial resolution of the original data in the dataset, i.e. regardless of how it is distributed using distributions.​

This is now in conflict with DCAT-AP 3.0, where for Dataset, the spatial resolution as a cardinality [0..1] (related to #372)

[bertvannuffelen]

Dear community,

Do you agree with the relaxation of the cardinalities as described in the proposel by the GeoDCAT-AP community?

[HendrikBorgelt]

As I can only interpret the "conclusions from abocve" based on the definitions in the DCAT Vocabulary, spatial resolution would de defined as "Minimum spatial separation resolvable in a dataset, measured in meters.", "The minimum spatial separation resolvable in a dataset distribution, measured in meters." and/or "The minimum spatial separation resolvable in a data service, measured in meters.". From my knowledge of various Ontologies, this poses some challenges, because since every distribution is a dataset, it inherits the possibility of having a minimum resolvable spatial resolution of the original dataset while simultaneously having aspatial resolution based on being a distribution. As these definitions basically conflict, this is already critical. You can't distinguish between the definitions the definition of the spatial resolution of a dataset and its distribution. A dataset that is distributed might as such want to describe how precise the measurements of the distribution could have been, based on the original dataset, while it may still want to describe how precise it is based on just being a subset of the dataset. So I think it would be best to differentiate between the "spatial resolution of the original dataset" and the "spatial resolution of a distribution".

Coming back to the example and ignoring the inconsistencies in the definition. A dataset would in theory only have the best of all spatial resolutions, by the definition of spatial resolution. Why should one have multiple "Minimum resolvable spatial separation[s] "? x_1 < x_2 < x_3 --> min of (x_1, x_2, x_3) = x_1 ! Something multivalued would not be feasible and as such not interpretable.