I agree the central point (or the essence) of this guideline, but would like to explore its boundary in more detail where researchers, authors, and reviewers often do not agree with one another. (Note: metaphorically, “central point” and “boundary” are spatial.)
The guideline is for non-spatial data, and we may consider the follow categorization of spatial and non-spatial data:
(a) physically spatial (e.g., geographical maps, medical volume datasets);
(b) inferably spatial (e.g., molecular geometry), which users are familiar with the spatialization;
© artificially spatial (e.g., color space), which users are familiar with the spatialization;
(d) associated with physically-spatial attributes that are often not used or unimportant to visualization tasks (e.g., metro maps, communication network architectures);
(e) non-spatial but commonly spatialized (e.g., height for price, length for time), which users can easily and quickly learn;
(f) non-spatial and seldom spatialized (e.g., a list of courses in a department);
(g) non-spatial and should not be spatialized in any circumstance (this is a placeholder as I have not found an example).
A dataset may feature components in multiple categories, e.g., a building on a map may have its x-y dimensions in (a) and its z dimension in (d).
The blurred boundary is the nature of user-dependence and task-dependence. Categories (b), ©, and (e) naturally make us ask the question “can users become accustomed to the visualization if some data in (f) is to be spatialized?” Category (d) makes us ask the question “in what scenario, bringing back the omitted physically-spatial attributes may be beneficial?” This seems to be matthias.kraus’s question. The benefits may include easy to learning and remember, context-awareness, connecting to other variables not in the data (e.g., rain cover condition), visual consistency with related visualizations that show physically-spatial data, etc.
I guess that we also need to define “replicate the real world” more precisely. What is considered as replicating the real world and what is not? Here is a tentative categorization:
(1) faithfully map all obtainable physically spatial attributes
(2) faithfully map some physically spatial attributes
(3) map some physically spatial attributes at a lower resolution (e.g., common practice)
(4) map some physically spatial attributes with deformation (e.g., metro map)
(5) map some physically spatial attributes to non-spatial visual channels
I guess that semantically “replicate the real world” does not apply to categories © and (e).
In addition, the tasks of disseminative visualization may present different scenarios from those of observational, analytical, and model-developmental visualization. For dissemination, one may justifiably use “replicate the real world” to provide users with novel experience and to grab their attention. There are also a fair amount of evidence to support “replicate the real world” in developing human models (e.g., in sports and medicine).