NDLI: Bias-variance decomposition of errors in data-driven land cover change modeling

Content Provider	Springer Nature Link
Author	Gao, Jing Burnicki, Amy C. Burt, James E.
Copyright Year	2016
Abstract	Careful model evaluation is essential for using the results of data-driven land cover change (LCC) models. A useful evaluation should help the analyst understand model behavior and improve model performance. Conventional error analysis methods provide limited insights on these two issues.We propose the use of bias-variance error decomposition (BVD) for LCC model evaluation and investigate its value through a pilot study.We examined the mathematical underpinnings of BVD and applied the approach to a model describing the expansion of development for a county in southeastern Michigan, USA.The spatial structure of the BVD error component maps effectively informed the design of model improvement efforts. We found that bias maps can help detect and eliminate the negative influence of spatial non-stationarity in LCC models, and variance maps can shed light on optimal training sample allocation and ways to mitigate model instability. In our case study, model improvements suggested by BVD showed potential to substantially reduce estimation error. Our analysis also revealed that fast and slow developments can result from different mechanisms even when they are geographically near to each other, and different development types may be best analyzed with separate models.BVD has advantages over conventional error analysis and can deepen our understanding of the underlying LCC process and the sources of modeling error. The BVD insights can help effectively design and apply model improvement strategies. These results and the BVD approach have great generality and are applicable to many other geospatial models.
Starting Page	2397
Ending Page	2413
Page Count	17
File Format	PDF
ISSN	09212973
Journal	Landscape Ecology
Volume Number	31
Issue Number	10
e-ISSN	15729761
Language	English
Publisher	Springer Netherlands
Publisher Date	2016-07-04
Publisher Place	Dordrecht
Access Restriction	Subscribed
Subject Keyword	Model evaluation Accuracy assessment Error decomposition Bias Variance Data-driven models Land cover change New development Landscape Ecology Ecology Nature Conservation Landscape/Regional and Urban Planning Sustainable Development Environmental Management
Content Type	Text
Resource Type	Article
Subject	Geography, Planning and Development Ecology Nature and Landscape Conservation

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