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A multiple criteria decision-making approach to GIS-based land suitability evaluation

Pereira, José M. C., Duckstein, Lucien·1993·International journal of geographical information systems

Abstract

Land suitability evaluation in a raster GIS environment is conceptualized as a multiple-criteria decision-making (MCDM) problem. A combination of MCDM techniques selected for implementing the methodology included value and priority assessment techniques for scaling the interval and ordinal data respectively, and compromise programming (CP) to aggregate the unidimensional evaluations. The contribution of the proposed methodology to handle problems of scaling and dependence that often affect expert-based suitability analyses is discussed. A case-study of habitat evaluation for the endangered Mount Graham red squirrel is presented. The multiple-criteria models resulting from the CP analysis of an expert's perception of the habitat preference structure of the red squirrel are compared with data of actual habitat use. The predictive power of the models is good and sensitivity analysis based on the distance-metric parameter p of CP reveals some interesting differences between alternative strategies for data aggregation.

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Metadata

Authors
Pereira, J. M. C., & Duckstein, L.
Venue
International journal of geographical information systems
Published
1993-09
Volume
7
Issue
5
Pages
407-424
ISSN
0269-3798 (ISSN-L)
OpenAlex
W2017639333
Cited by
488
References
0
FWCI
7.67
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: A multiple criteria decision-making approach to GIS-based land suitability evaluation. https://app.cochat.ai/papers/10.1080/02693799308901971
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Pereira, J. M. C., & Duckstein, L. (1993). A multiple criteria decision-making approach to GIS-based land suitability evaluation. International journal of geographical information systems. https://doi.org/10.1080/02693799308901971

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.