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Title: A spatial genetic algorithm for automating land partitioning
Authors: Demetriou, Demetris 
See, Linda 
Stillwell, John 
Major Field of Science: Engineering and Technology
Field Category: Civil Engineering
Keywords: genetic algorithms;GIS;land partitioning;single and multi-objective optimisation;Thiessen polygons
Issue Date: 1-Dec-2013
Source: International Journal of Geographical Information Science, 2013, vol. 27, iss. 12, pp. 2391 - 2409
Volume: 27
Issue: 12
Start page: 2391
End page: 2409
Journal: International Journal of Geographical Information Science 
Abstract: Land fragmentation is a widespread situation which may often hinder agricultural development. Land consolidation is considered to be the most effective land management planning approach for controlling land fragmentation and hence improving agricultural efficiency. Land partitioning is a basic process of land consolidation that involves the subdivision of land into smaller sub-spaces subject to a number of constraints. This paper explains the development of a module called LandParcelS (Land Parcelling System) that is a part of an integrated planning and decision support system called LACONISS (LAndCONsolidation Integrated Support System) which has been developed to assist land consolidation planning in Cyprus. LandParcelS automates the land partitioning process by designing and optimising land parcels in terms of their shape, size and value. The methodology integrates geographical information systems and a genetic algorithm that has been applied to two land blocks that are part of a larger case study area in Cyprus. Partitioning is treated as either a single or multi-objective problem for various optimisation cases. The results suggest that a step forward has been made in solving this complex spatial problem, although further research is needed to improve the algorithm. This approach may have relevance to other spatial planning tasks that involve single or multi-objective optimisation problems, especially those dealing with space partitioning. © 2013 © Taylor & Francis.
ISSN: 13658816
DOI: 10.1080/13658816.2013.819977
Rights: © Taylor & Francis
Attribution-NonCommercial-NoDerivatives 4.0 International
Type: Article
Affiliation : University of Leeds 
International Institute for Applied Systems Analysis 
Appears in Collections:Άρθρα/Articles

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