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• W179637349 abstract "Abstract Spatial data are common in ecological studies. A major problem with the analysis of such data is the presence of spatial autocorrelation. This chapter describes a method for dealing with the effects of spatial autocorrelation and its application to the problem of predicting gene flow from genetically modified (GM) to non-GM maize fields. We first present the SCLUS (Spatial Predictive Clustering System) method, which learns spatially aware predictive clustering trees (PCTs). SCLUS can consider the effects of spatial autocorrelation, both locally and globally, and can deal with the “ecological fallacy” problem according to which different regions may follow different data distributions. Then, we present the use of this approach for gene flow modeling under real multifield crop management practices at a local scale." @default.
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• W179637349 date "2012-01-01" @default.
• W179637349 modified "2023-09-27" @default.
• W179637349 title "Dealing with Spatial Autocorrelation in Gene Flow Modeling" @default.
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• W179637349 doi "https://doi.org/10.1016/b978-0-444-59396-2.00003-1" @default.
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