The role of least image dimensions in generalization of object in spatial databases

Journal title

Geodesy and Cartography




vol. 59


No 2

Publication authors


cartographic generalization ; databases – MRDB ; informatics ; statistics

Divisions of PAS

Nauki Techniczne


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.


Commitee on Geodesy PAS




Artykuły / Articles


ISSN 2080-7636


Aslanikaschvili A. (1974), Cartography. Main Problems (in Polish). ; Bertin J. (1971), Graphics (in Polish), 1. ; Brassel K. (1988), A review and conceptual framework of automated map generalization, International Journal of Geographical Information Systems, 2, 3, 229, ; Chrobak T. (1999), An Investigation of Elementary Triangle Usefulness for Computer Cartographic Generalization (in Polish). ; Chrobak T. (2007), Fundamentals of digital cartographic generalization (in Polish). ; Douglas D. (1973), Algorithms for the reduction of the number of points required to represent a digitized line or its caricature, The Canadian Cartographer, 10, 2, 112. ; Grünreich D. (1992), Research and Development in Computer-Assisted Generalization of Topographic Information at the Institute of Cartography, null, l, 36. ; Grünreich D. (1995), GIS and Generalization - Methodology and Practice, Great Britain, 47. ; Hake G. (1973), Kartographie und Kommunikation, Kartographische Nachrichten, 23, 4, 137. ; Kozioł K. (2006), Elimination of linear objects with the use of structural regions on the example of a road network, Annals of Geomatics, IV, 3, 109. ; Kulikowski J. (1986), Outline of graph theory. Applications of the technique (in Polish). ; Longley P. (2006), GIS and Science. ; (2007), Generalisation of geographic information: cartographic modeling and application. ; Molenaar M. (1996), The role of topologic and hierarchical spatial object models in database generalization, 13. ; Morrison J. (1974), A theoretical framework for cartographic generalization with emphasis on the process of symbolization, International Yearbook of Cartography, 14, 115. ; Nickerson B. (1986), Development of rule-based system for automatic map generalization, null, 537. ; Olszewski R. (2009), Cartographic modelling of terrain relief with the use of computational intelligence methods (in Polish). ; Ostrowski W. (2008), Semiotic Basis for Designing of Topographic Maps on the Example of Urban Areas (in Polish). ; Piątkowski F. (1969), Cartography, Editing of cartographic maps and their reproduction (in Polish). ; Preparata F. (1985), Computational Geometry. Introduction (in Polish), ; Ratajski L. (1989), Methodology of mapping the socio-economics (in Polish), 198. ; Richardson D.E., (1993): <i>Automatic spatial and thematic generalization using a context transformation model</i>, PhD Thesis, Wagering Agriculture University. Ottawa, Canada. ; Saalfeld A. (1999), Topologically Consistent Line Simplification with the Douglas - Peucker Algorithm, Cartography and Geographic Information Science, 26, 1, 7, ; Saliszczew K. (1998), General Cartography (in Polish). ; Shea K. (1989), Cartographic generalization in a digital environment: When and How to generalize, null, 56. ; Strahler A. (1964), Handbook of applied hydrology, 39. ; Weibel R. (1995), Map generalization in the context of digital systems, Cartography and GIS, 22, 4, 56.