A Computational Robust Method for Spatial Decomposition

This paper presents a case study of a novel spatial decomposition algorithm in the field of Geographic Information Systems (GIS). Real estate cadastral data of a district, consisting of parcels and buildings, are used as a test data set. A cadastral map is a set of parcels and buildings, or more generally GIS features. Each feature is geometrically represented as a polygon. The legally binding nature of the cadastral map (rights, restrictions and responsibilities on land) requires that the polygons do not overlap. There must also be no gaps between the parcels.
Based on the test cases, it is shown that the computationally robust space decomposition presented here with a complete, gapless and overlap-free two-dimensional topology can be used very well in this domain. With the added benefit that the results provided are completely error-free and reliable, and spatial queries can be easily formulated using set operations. The foundations of the algorithm were presented in previous research papers, and are summarized shortly in this paper.
While a previous paper provided a proof of concept using artificial datasets, this paper now uses real cadastral data. The fully automated procedure transforms OGC simple features in a space decomposition model. For validation purposes, all cases are additionally tested with a geospatial ETL-software, namely the Feature Manipulation Engine (FME).

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