The architecture of Geographic Information Systems (GISs) is changing: more and more systems are based on the integrated architecture, i.e. storing geometric data in the Data Base Management System (DBMS) together with administrative data. The first step in building a Geo-DBMS is by having data types and operators for simple features (i.e. geometric primitives): point, line and polygon. This has reached a level of standardisation and is now implemented in several commercial DBMSs. The next step is to have support for the topologically structured features in the DBMS, i.e. complex features. The DBMS can then check and guarantee consistency. In addition, complex operations can be executed within the DBMS. Despite the fact that topologically structured models are well known and that it is not difficult to store the topological references, it still remains an unresolved issue as to how to effectively implement these models within a relational DBMS. In this paper, we describe the design and implementation of a topologically structured management at the DBMS level. Our focus is to translate topological structures into geometric primitives. It is then possible to define a DBMS view on a topological primitive, which makes this appear as a geometric primitive. This process supports the best of both worlds: on the one hand there are advantages of the topological structure (no redundancy) and on the other hand the ease of explicit geometric primitives in querying, analysis and presentation is available.

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Accurate postal code maps could play an important role within GIS as the postal code has the potential to link the address description of buildings and their location in a certain global reference system. This relationship is possible in both directions: address matching and geocoding. These operators demand a certain mechanism in translating an exact geometric position (i.e. WGS84 coordinate) into a location indication (town, street, house number) and vice versa. As most built-up parcels are provided with a postal code this indicator could be used as the linkage. This paper describes the procedure, based on the Dutch cadastral registration, to obtain a reliable 6-position planar postal code map for the Netherlands. Problems with existing postal code maps, like intersecting of houses and arbitrary derived boundaries are avoided.For a planar coverage, non built-up parcels having no assigned postal code should be assigned a plausible postal code. Therefore special attention is given to infrastructural parcels. These parcels are divided at their skeleton first and then piecewise attached to their neighbor parcels. This new approach results in very reliable postal code maps, which are visually attractive too as infrastructure lines can be regognized. The procedure is generic and can be applied to other administrative parcel information as well.The algorithm is implemented using the Computational Geometry Algorithms Library (CGAL), and the possibilities and limitations of this library are addressed as well. The reliability of the derived planar postal code map is discussed and some results are shown by figures. A short overview of alternatives and improvements concludes this paper.

Spatial models are often based on polygons both in 2D and 3D. Many Geo-ICT products support spatial data types, such as the polygon, based on the OpenGIS 'Simple Features Specification'. OpenGIS and ISO have agreed to harmonize their specifications and standards. In this paper we discuss the relevant aspects related to polygons in these standards and compare several implementations. A quite exhaustive set of test polygons (with holes) has been developed. The test results reveal significant differences in the implementations, which causes interoperability problems. Part of these differences can be explained by different interpretations (definitions) of the OpenGIS and ISO standards (do not have an equal polygon definition). Another part of these differences is due to typical implementation issues, such as alternative methods for handling tolerances. Based on these experiences we propose an unambiguous definition for polygons, which makes polygons again the stable foundation it is supposed to be in spatial modelling and analysis. Valid polygons are well defined, but as they may still cause problems during data transfer, also the concept of (valid) clean polygons is defined.

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