A review and conceptual framework of automated map generalization

Abstract

This paper reviews the prospects of computer-assisted generalization of spatial data. Generalization as a general human activity is first considered in a broad context and map generalization is defined as a special variant of spatial modelling. It is then argued that in computer-assisted generalization, the spatial modelling process can be simulated only by strategies based on understanding and not by a mere sequence of operational processing steps. A conceptual framework for knowledge-based generalization is then presented which can be broken down into five steps: structure recognition, process recognition, process modelling, process execution and display. With reference to the goals of map generalization we identified tasks of statistical and cartographic generalization. The use of these types of tasks is discussed in relation to the concepts of digital landscape models (DLM) and digital cartographic models (DCM). A literature review is then presented in the context of this conceptual framework. It considers theoretical aspects of generalization and technical procedures on attributes and geometrical generalization. Specific sections of this review include statistical generalization, structure recognition and processes of cartographic generalization (point, line, area features, surfaces) and efforts for system integration. The paper concludes with an evaluation of the state of the art and an outlook on the future. Major efforts have to be devoted to developing an understanding of structures and processes involved in generalization (structure recognition, process recognition) and to modelling these processes. New data models will be a prerequisite for success in this field.