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    Evaluating geo-environmental variables using a clustering based areal model
    (Pergamon-Elsevier Science Ltd, 2012) Tutmez, Bulent; Kaymak, Uzay; Tercan, A. Erhan; Lloyd, Christopher D.
    Global regression models do not accurately reflect the spatial heterogeneity which characterises most geo-environmental variables. In analysing the relationships between such variables, an approach is required which allows the model parameters to vary spatially. This paper proposes a new framework for exploring local relationships between geo-environmental variables. The method is based on extended objective function based fuzzy clustering with the environmental parameters estimated through on a locally weighted regression analysis. The case studies and prediction evaluations show that the fuzzy algorithm yields well-fitted models and accurate predictions. In addition to an increased accuracy of prediction relative to the widely-used geographically weighted regression (GWR), the proposed algorithm provides the search radius (bandwidth) and weights for local estimation directly from the data. The results suggest that the method could be employed effectively in tackling real world kernel-based modelling problems. (C) 2012 Elsevier Ltd. All rights reserved.
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    Local models for the analysis of spatially varying relationships in a lignite deposit
    (European Soc Fuzzy Logic & Technology, 2009) Tutmez, Bulent; Tercan, A. Erhan; Kaymak, Uzay; Lloyd, Christopher D.
    Relationships between geographically referenced variables are usually spatially heterogeneous and, to account for such variations, local models are necessary. This paper compares the Geographically Weighted Regression (GWR) model, usually used to integrate and examine the spatial heterogeneity of a relationship, and the Fuzzy Clustering-Based Least Squares (FCBLS) model for the analysis of spatially varying relationships. Both models use the same model parameters and bandwidth values derived from the Akaike Information Criterion. The results show that FCBLS outperforms the GWR model.