Clarify pros and cons of schema.org's Stat vocab vs RDF Data Cube?

[lapidus]

In issue #2291 – Proposal for representing Aggregate Statistical Data (@danbri) – the new types StatisticalPopulation and Observation are introduced, along with properties such as populationType, numConstraints, constrainingProperties.

Providing context to the original issue, @rvguha writes:

Our interest is not in describing a data set or mapping columns in csv files, but in representing the actual data itself. Other efforts have focused on characterizing data cubes in terms of dimensions, etc. While we draw upon their work, our goals are different.

In follow-up comments, @VladimirAlexiev remarks:

Compared to the proposal https://lists.w3.org/Archives/Public/public-schemaorg/2019Jun/0021.html, this task is not implemented well and should be reopened. #2291 (comment)

How would you you represent this situation: an air balloon is rising, and every 10s records altitude, pressure and temperature. [...] Seems inferior to W3 Cube's option to use multiple Measures in Observation. #2291 (comment)

Being an implementer of rdf-oriented cubes, this conversation raises a number of questions:

1. When @rvguha writes that the work "draws on earlier work", is it primarily this RDF Data Cube Vocab he is referring to?

2. When he states that "our goals are different", and that the proposal is focusing on "representing the actual data itself", how is that manifested exactly? I.e. how does this model better facilitate representing the data itself?

3. More specifically, what are the actual Pros and Cons of the Schema.org approach versus the RDF Data Cube Vocab (https://www.w3.org/TR/vocab-data-cube/) or its 'compacted cousin' Cube Schema (https://cube.link/).

As the W3 cube model remains a centerpiece in many discussions around harmonization of statistics (see conversations and documents re. mappings between SDMX<>StatDCAT<>RDFCube etc. etc.), it would be excellent with more practical examples of how #2291 relates to, and perhaps aims to supersede, earlier work.

Any further explanations, clarifications or links would be much appreciated.