Modèle de généralisation de données urbaines à base de contraintes et d’autonomieUrban data generalization models using constraints and autonomy - Anne Ruas (Cybergeo)
This paper proposes a model to generalise urban information. The model is based on autonomy, on constraints and on the representation of several levels of analysis. A geographical entity (named situation) chooses an operation which satisfies its own constraints. Generalisation is performed step by step, in an autonomous way. The final state aims at finding a compromise between constraints which incite generalisation and those which incite a preservation of geographical meaning. In this model, constraints represent the user needs on each situation. They are qualified by a level of non-satisfaction which changes during the process. In order to preserve group properties and to apply contextual operations (e.g. object removal or displacement), we introduce the concept of meso situations which are groups of objects that either generalise themselves together or analyse their properties to provide finer guideline for simple object self generalisation. Among such analysis we emphasise distribution analysis either to ensure dissociation between values or to maintain exceptional values within a group. Generalisation at the lowest level (i.e. independent) has the tendency to remove differences between characters which in term destroys geographic space specificity. These meso analysis should be increasingly considered when wanting to improve the quality of generalisation process. These concepts are illustrated by urban generalisation experiments.