The article presents a spatial data model describing a geographical space which makes it possible to automatically generalize data once the scale of a map has changed. To that end the Molenaar FDS data structure, extended by the geometrical rule, has been applied. The so formed spatial data model has been checked upon the example of the road network of ,,Downtown Cracow", confirmation of the accepted rules for the FDS structure having been acquired.

The paper discusses the global algorithm of broken line simplification, that: does not depend on parameters set by a map editor and maintains the accuracy of the 0-1 Instruction (General principles of surveying practice) of the Head Office of Geodesy and Cartography, Poland, for each map scale (smaller than the source map scale). In the discussed process of line simplification parameters depend on the map scale and on the smallest length of an elementary triangle (this length is a measure of the ability of the drawing recognition). In the process of simplification performed with the use of the discussed algorithm, the same shape of a line is ensured (maintaining the ability of the drawing recognition), since generalised data differ with the bigger or smaller range of scales from the source data. Besides, limits of intervals of generalisation thresholds have been specified, which are required for the process of automated selection of cartographic presentation methods exhibiting the results of line simplification.

The paper presents the least admissible dimensions of black lines of spatial object images, according to Saliszczew, adjusted to the needs of database generalization. It is pointed out, that the adjusted dimensions are in agreement with the cartographic norm included in the National Map Accuracy Standards , and their application to the generalization 1 will allow, for any map scale, the determination of the: • value of the scale-dependent parameter of the generalization process, without user action; • measure of recognizability of the shortest black line section on the map, what helps to obtain unique results of line generalization; • measure of recognizability of black lines in the image – using a standard (elementary triangle) – helpful in obtaining unique result of line simplification, and an assessment of the process; • recognizability distance between lines of close buildings, securing unique aggregation of them; • verification of spatial object image lines visualization. The new solutions were tested with the Douglas-Peucker (1973) generalization algorithm, modified by the author, which treats the minimal dimensions as geometric attributes, while object classes and their data hierarchy as descriptive attributes. This approach secures uniqueness of results on any level of generalization process, in which data of spatial objects in the DLM model are transformed to conform with the requirements for the DCM model data.