Generalization

From Wiki.GIS.com

A generalization of a concept is an extension of the concept to less-specific criteria. It is a foundational element of logic and human reasoning. Generalization posits the existence of a domain or set of elements, as well as one or more common characteristics shared by those elements. As such, it is the essential basis of all valid deductive inference. The process of verification is necessary to determine whether a generalization holds true for any given situation.

The concept of generalization has broad application in many related disciplines, sometimes having a specialized context-specific meaning.

For any two related concepts, A and B; A is considered a generalization of concept B if and only if:

• every instance of concept B is also an instance of concept A; and
• there are instances of concept A which are not instances of concept B.

For instance, animal is a generalization of bird because every bird is an animal, and there are animals which are not birds (dogs, for instance).

On a side note, a common joke goes like this:

"All generalizations are false!"

This is known as a self-contradictory statement.

Contents 1 Hypernym and Hyponym 2 Cartographic generalization of geo-spatial data 2.1 Content Operators 2.2 Geometry Operators 2.3 Symbology Operators 3 GIS and automated generalization 4 References 5 See also

[edit] Hypernym and Hyponym

This kind of generalization versus specialization (or particularization) is reflected in the mirror of the contrasting words of the three word pair hypernym and hyponym. A hypernym as a generic stands for a class or group of equally-ranked items such as tree does for peach and oak; or ship for cruiser and steamer. Whereas a hyponym is one of the items included in the generic, such as peach and oak are included in tree, and cruiser and steamer in ship. A hypernym is superordinate to a hyponym, and a hyponym is subordinate to a hypernym.

[edit] Cartographic generalization of geo-spatial data

Generalization has a long history in cartography as an art of creating maps for different scale and purpose. Cartographic generalization is the process of selecting and representing information of a map in a way that adapts to the scale of the display medium of the map. In this way, every map has, to some extent, been generalized to match the criteria of display. This includes small-scale maps, which cannot convey every detail of the real world. Cartographers must decide and then adjust the content within their maps to create a suitable and useful map that conveys geospatial information within their representation of the world.

Generalization is meant to be context-specific. This is to say that correctly generalized maps are those that emphasize the most important map elements while still representing the world in the most faithful and recognizable way. The level of detail and importance in what is remaining on the map must outweigh the insignificance of items that were generalized, as to preserve the distinguishing characteristics of what makes the map useful and important.

[edit] Content Operators

• Add
• Eliminate
• Reclassify
• Reorder

[edit] Geometry Operators

• Aggregate
• Collapse
• Displace
• Exaggerate
• Merge
• Simplify
• Smooth

[edit] Symbology Operators

• Adjust
  • Color
  • Shape
  • Size
  • Pattern
  • Transparency
• Rotate
• Typify
• Enhance
• Rotate

[edit] GIS and automated generalization

As GIS came up in the last century and the demand for producing maps automatically increased automated generalization became an important issue for National Mapping Agencies (NMAs) and other data providers. Thereby automated generalization describes the automated extraction of data (becoming then information) regarding purpose and scale. Different researchers invented conceptual models for automated generalization:

• Gruenreich model
• Brassel & Weibel model
• McMaster & Shea model

Besides these established model, different views on automated generalization have been established. The representation-oriented view and the process-oriented view. The first view focuses on the representation of data on different scales, which is related to the field of Multi-Representation Databases (MRDB). The latter view focuses on the process of generalization.

In the context of creating databases on different scales additionally it can be distinguished between the ladder and the star-approach. The ladder-approach is a stepwise generalization, in which each derived dataset is based on the other database of the next larger scale. The star-approach describes the derived data on all scales is based on a single (large-scale) data base.

[edit] References

[edit] See also

• Abstraction
• Ceteris paribus
• Generic
• Generic antecedent
• inheritance (object-oriented programming),
• Faulty generalization
• Hasty generalization
• Mutatis mutandis
• -onym
• Class diagram
• Ramer-Douglas-Peucker algorithm
• Specialization, the opposite process