Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/30428
Title: Integrating GIS and genetic algorithms for automating land partitioning
Authors: Demetriou, Demetris 
See, Linda 
Stillwell, John 
Editors: Hadjimitsis, Diofantos G. 
Themistocleous, Kyriacos 
Michaelides, Silas 
Papadavid, Giorgos 
Major Field of Science: Engineering and Technology
Field Category: Civil Engineering
Keywords: Genetic algorithms;GIS;Land partitioning;Multi-objective optimisation;Thiessen polygons
Issue Date: 7-Apr-2014
Source: 2nd International Conference on Remote Sensing and Geoinformation of the Environment, RSCy 2014, Paphos, Cyprus, 7 - 10 April 2014
Volume: 9229
Conference: Proceedings of SPIE - The International Society for Optical Engineering 
Abstract: 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 integrates geographical information systems and a genetic algorithm to automate the land partitioning process by designing and optimising land parcels in terms of their shape, size and value. This new module has been applied to two land blocks that are part of a larger case study area in Cyprus. Partitioning is carried out by guiding a Thiessen polygon process within ArcGIS and it is treated as a multiobjective problem. 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. The contribution of this research extends land partitioning and space partitioning in general, since these approaches may have relevance to other spatial processes that involve single or multi-objective problems that could be solved in the future by spatial evolutionary algorithms. © 2014 SPIE.
URI: https://hdl.handle.net/20.500.14279/30428
ISBN: 9781628412765
ISSN: 0277786X
DOI: 10.1117/12.2064520
Rights: © SPIE
Attribution-NonCommercial-NoDerivatives 4.0 International
Type: Conference Papers
Affiliation : University of Leeds 
International Institute for Applied Systems Analysis 
University College London 
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation

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