AUTOMATED GENERALISATION IN A MULTIPLE REPRESENTATION DATABASE
Mats Dunkars · 2004
A multiple representation database contains several geographic data sets defined at different resolutions covering the same area. Objects within the different datasets that represent the same real world entity are connected with bi-directional links. Multiple representation databases can be used to increase the efficiency of updating topographic data sets. Updates in the large-scale dataset can be generalised and propagated to the small-scale datasets automatically. In this paper a method for automated propagation of updates in a multiple representation database is presented. Currently the method is designed to handle updates of roads and buildings. A difficult issue in research on cartographic generalisation is how to obtain and formalise the cartographers’ knowledge in such a way that it can be used to develop an automated procedure. In this paper data mining methods are used to extract such knowledge from a multiple representation database. An unsupervised classification of the objects in the largescale data set is performed using the ISODATA algorithm. For each class the probability that members of the class are represented in a certain way in the small-scale data set is calculated. This knowledge is used for the automated propagation of updates. The method is currently being tested on topographical data sets provided by the Swedish National Land Survey on a scale of 1:10 000, 1:50 000 and 1:100 000.