Goodchild begins with a notion that originally shocked me. The metric scale was supposedly unsuitable in digital cartography. I had never considered this notion before, but he nonetheless present a convincing explanation of his reasoning. Confusion over what he means by scale is widespread, which either significance the spatial extent of the map or the level of granularity that the data represents. There has also been issues over what kind of information is appropriate to represent along this axis of scalability. Goodchild proposes a new dimension of scales that is more appropriate for computer programming.
He offers two different bases of scale.
The 1st is object model scales, which is based on the choice of objects that the GI scientist wishes to study. Typically, the smallest object studied would figure clearly on this map. The second would be the field models which would simply be the size of the pixel’s fields.
While I read the description of these last two models, I felt as if I’d reached the climax of some detective novel. Obviously, the object model sounded very much like vector, and the field model sounded very much like raster. I had never considered these two ways of data representation as a type of scale.
These two scales are most useful when handling data, but they are typically misunderstood for interpretation by non-experts. The metric system is still important for the visualization of this data, which often appears as exclusively spatial extent when finalizing a map and making it legible for a general public.
It is very interesting to see a paper creating a tangible link between traditional and digital cartographic models.