Area aggregation in map generalisation by mixed-integer programming

• Published in: International journal of geographical information science : IJGIS Vol. 24; no. 12; pp. 1871 - 1897
• Main Authors: Haunert, Jan-Henrik, Wolff, Alexander
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 01.12.2010; Taylor & Francis LLC
• Subjects: aggregation; combinatorial optimisation; Computer programs; Graph theory; Heuristic; Integer programming; map generalisation; mixed-integer programming; NP-hardness; Optimization algorithms; Optimization techniques; Semantics; Software; Topographic databases
• ISSN: 1365-8816; 1362-3087; 1365-8824
• DOI: 10.1080/13658810903401008

Topographic databases normally contain areas of different land cover classes, commonly defining a planar partition, that is, gaps and overlaps are not allowed. When reducing the scale of such a database, some areas become too small for representation and need to be aggregated. This unintentionally but unavoidably results in changes of classes. In this article we present an optimisation method for the aggregation problem. This method aims to minimise changes of classes and to create compact shapes, subject to hard constraints ensuring aggregates of sufficient size for the target scale. To quantify class changes we apply a semantic distance measure. We give a graph theoretical problem formulation and prove that the problem is NP-hard, meaning that we cannot hope to find an efficient algorithm. Instead, we present a solution by mixed-integer programming that can be used to optimally solve small instances with existing optimisation software. In order to process large datasets, we introduce specialised heuristics that allow certain variables to be eliminated in advance and a problem instance to be decomposed into independent sub-instances. We tested our method for a dataset of the official German topographic database ATKIS with input scale 1:50,000 and output scale 1:250,000. For small instances, we compare results of this approach with optimal solutions that were obtained without heuristics. We compare results for large instances with those of an existing iterative algorithm and an alternative optimisation approach by simulated annealing. These tests allow us to conclude that, with the defined heuristics, our optimisation method yields high-quality results for large datasets in modest time.