Toward Self-Generalizing Objects and On-the-Fly Map Generalization

Abstract

Map generalization is a complex task that sometimes requires human intervention. In order to support such a process on the fly, we propose a generalization approach based on self-generalizing objects (SGOs) that encapsulate geometric patterns (forms common to several cartographic features), generalization algorithms, and spatial integrity constraints. During a database enrichment process, an SGO is created and associated with a cartographic feature or a group of features. Each SGO created is then transformed into a software agent (SGO agent) in a multi-agent on-the-fly map-generalization system. SGO agents are equipped with behaviours that enable them to coordinate the generalization process. This article presents the concept of the SGO and two prototypes developed to support this approach: a prototype for the creation of SGOs and another for the on-the-fly map generalization (which uses the created SGOs).