Local spatial regression models: a comparative analysis on soil contamination

Spatial data analysis focuses on both attribute and locational information. Local analyses deal with differences across space whereas global analyses deal with similarities across space. This paper addresses an experimental comparative study to analyse the spatial data by some weighted local regress...

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Bibliographic Details
Published in:Stochastic environmental research and risk assessment 2012-10, Vol.26 (7), p.1013-1023
Main Authors: Tutmez, Bulent, Kaymak, Uzay, Tercan, A. Erhan
Format: Article
Language:English
Subjects:
Aquatic Pollution
Chemistry and Earth Sciences
Comparative analysis
Comparative studies
Computational Intelligence
Computer Science
Earth and Environmental Science
Earth Sciences
Environment
Math. Appl. in Environmental Science
Objective function
Original Paper
Physics
Probability Theory and Stochastic Processes
Regression analysis
Soil contamination
Spatial analysis
Statistics for Engineering
Waste Water Technology
Water Management
Water Pollution Control
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Summary:Spatial data analysis focuses on both attribute and locational information. Local analyses deal with differences across space whereas global analyses deal with similarities across space. This paper addresses an experimental comparative study to analyse the spatial data by some weighted local regression models. Five local regression models have been developed and their estimation capacities have been evaluated. The experimental studies showed that integration of objective function based fuzzy clustering to geostatistics provides some accurate and general models structures. In particular, the estimation performance of the model established by combining the extended fuzzy clustering algorithm and standard regional dependence function is higher than that of the other regression models. Finally, it could be suggested that the hybrid regression models developed by combining soft computing and geostatistics could be used in spatial data analysis.
ISSN:1436-3240
1436-3259
DOI:10.1007/s00477-011-0532-2