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Proceedings Paper

Spatially constrained clustering over GIS generated suitability maps
Author(s): Panagiotis Partsinevelos; Kostas Papadakis
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Paper Abstract

An abundance of GIS and Remote Sensing based spatial analysis studies result in various types of suitability maps, where selected regions are classified according to application driven qualitative or quantitative rules. Often, upon the resulting classified regions which define spatially constrained classes, users intent to position facilities in order to satisfy a series of demand sites spread throughout the study area. This fine tuning procedure, not tackled under classic clustering and location analysis algorithms, is addressed through the extension of k-means algorithm, by restricting cluster centers inside a priori outlined regions, while minimizing distance metrics towards demand locations. Experimentation in both synthetic and real based datasets shows the applicability of the approach and demonstrates the overall performance of the algorithm.

Paper Details

Date Published: 19 June 2015
PDF: 8 pages
Proc. SPIE 9535, Third International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2015), 95351O (19 June 2015); doi: 10.1117/12.2194432
Show Author Affiliations
Panagiotis Partsinevelos, Technical Univ. of Crete (Greece)
Kostas Papadakis, Technical Univ. of Crete (Greece)


Published in SPIE Proceedings Vol. 9535:
Third International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2015)
Diofantos G. Hadjimitsis; Kyriacos Themistocleous; Silas Michaelides; Giorgos Papadavid, Editor(s)

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