Automated aggregation of geographic objects: A new approach to the conceptual generalisation of geographic databases

91 Samenvatting.93 Curriculum Vitae.95 Acknowledgements . 96 Chapter 1: Introduction 1.1 What is generalisation? Geographic information is gathered, structured and stored with a certain purpose in mind. Optimising data for a specific use often means that they become less suitable for other purposes. One of the issues relevant for the intended use is the determination of the appropriate level of spatial and thematic detail. Using a very detailed dataset is often not desirable as this may make it difficult to get an overview. Generalising the data is then an option. One possibility is to ‘thin’ the original data, but more often the original data will have to be transformed to create new, composite objects. In cartography, generalisation is generally carried out manually, focusing on the graphic representation at different scale levels. Most of the early research into the automation of procedures for map generalisation also focused mainly on cartographic, and therefore graphic aspects. Nowadays generalisation is part of the framework of geographic information processing. In this context we speak of conceptual or model generalisation, i.e. the process that creates a derived dataset with properties that are more desirable and usually less complex than the ones in the original dataset (João et al. 1993). Conceptual generalisation disregards the graphic limitations of the output medium and related operations like exaggeration, displacement and smoothing. Instead, it concentrates on operations such as classification, aggregation and elimination. Attempts have been made to create comprehensive systems for automated map generalisation. These attempts were often based on rules for manual generalisation, but these proved unsuitable for automated environments. Systems based on new rules specific to automated generalisation were more successful but their use was still limited to well-defined situations, regarding either the input data or the purpose of the generalised data, or both. Multi-purpose systems do not exist. Generalisation holds an important position in the development of a theoretical framework for handling geographic information, as it deals with the structure and transformation of complex spatial notions at different levels of abstraction. A better understanding of these processes will also improve our understanding of data acquisition, analysis and visualisation. Generalising a geographic dataset is like creating an abstract of a text: main and secondary issues are differentiated. A text is written for a certain purpose (to entertain, to inform, to influence etc.) and for a certain audience (children, students, employees etc.) An abstract is either meant for readers to determine if the text is worth reading, by giving an impression of the contents, or to be informed to a basic level. The target group an abstract is aimed at is often larger than the group that would read the text itself. The same applies to the generalisation of geo-data. A generalised presentation is a tool to aid in understanding and gaining access to the underlying, more detailed data. It all has to do with the context in which the information is used: what is communicated to whom and why? 1.2 Why generalise? Maps of different scale levels have coexisted for centuries, so apparently there is a need for information at different levels of detail. There is a need to present data at a less