Skip to Main Content
Spatial residual analysis of six modeling techniquesAuthor(s): Lianjun Zhang; Jeffrey H. Gove; Linda S. Heath; Linda S. Heath
Source: Ecological Modelling. 186: 154-177.
Publication Series: Miscellaneous Publication
PDF: View PDF (1.39 MB)
- Check the Northern Research Station web site to request a printed copy of this publication.
- Our on-line publications are scanned and captured using Adobe Acrobat.
- During the capture process some typographical errors may occur.
- Please contact Sharon Hobrla, firstname.lastname@example.org if you notice any errors which make this publication unusable.
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
CitationZhang, Lianjun; Gove, Jeffrey H.; Heath, Linda S. 2005. Spatial residual analysis of six modeling techniques. Ecological Modelling. 186: 154-177.
Keywordsspatial autocorrelation, local indicator of spatial autocorrelation (LISA), ordinary least squares (OLS), linear mixed model (LMM), generalized additive model (GAM), multi-layer perceptron (MLP) neural network, radial basis function (RBF) neural network, geographically weighted regression (GWR)
- Fine resolution probabilistic land cover classification of landscapes in the southeastern United States
- Spatial Assessment of Model Errors from Four Regression Techniques
- Estimating regional plant biodiversity with GIS modelling
XML: View XML