The Role of Spatial Context Information in the Generalization of Geographic Information: Using Reducts to Indicate Relevant Attributes

Abstract

Generalization of geographic information enables cognition and understanding not only of objects and phenomena located in space but also the relations and processes between them. The automation of this process requires formalization of cartographic knowledge, including information on the spatial context of objects. However, the question remains which information is crucial to the decisions regarding the generalization (in this paper: selection) of objects. The article presents and compares the usability of three methods based on rough set theories (rough set theory, dominance-based rough set theory, fuzzy rough set theory) that facilitate the designation of the attributes relevant to a decision. The methods are using different types (levels of measurements) of attributes. The author determines reducts and their cores (common elements) that show the relevance of attributes stemming from the spatial context. The fuzzy rough set theory method proved the least useful, whereas the rough set theory and dominance-based rough set theory methods seem to be recommendable (depending on the governing level of measurement).

Map Generalization for the Future: Editorial Comments on the Special Issue

“…Since cartographic generalization necessarily involves consideration of many criteria, this approach may simulate choices made by experts. Fiedukowicz [3] compares different variations of fuzzy and rough set theory to determine attributes that can be used for automatic selection or omission of roads, buildings and watercourses on small-scale maps. Focusing on the concept of reducts (subsets of attributes that are sufficient for discriminating between features selected vs. omitted by experts), it is shown that some variations of fuzzy or rough sets can better identify core attributes than others.…”