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• W2610579654 abstract "This paper looks at the error inherent in Predictive Vegetation Mapping (PVM). I review past work on assessing and tracking error in the production of the most common form in which natural resource data is stored and visualised, the area-class map. Using the extensive and well-studied Kioloa forest dataset, an alternative method of storage and display based on datasets for each individual species is described. Whilst this alternative approach is more accurate, and can carry estimates of probability for the estimated distribution of each species, it is cumbersome. Looking again at the area-class map it is suggested that, for many users, the degree of error built-into such a representation may be an acceptable cost for the convenience of a simple presentation. Having looked at the degree of error, and attempts to minimise it, the paper concludes with the observation that some types of error are the inevitable cost of attempts to simplify representation." @default.
• W2610579654 created "2017-05-12" @default.
• W2610579654 creator A5033884592 @default.
• W2610579654 date "2017-01-01" @default.
• W2610579654 modified "2023-09-23" @default.
• W2610579654 title "Error in Spatial Ecology (PVM)" @default.
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• W2610579654 doi "https://doi.org/10.1007/978-981-10-4424-3_4" @default.
• W2610579654 hasPublicationYear "2017" @default.
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