A New Spatial-Attribute Weighting Function For Geographically Weighted Regression

November 29, 2005 - Author: Haijin Shi; Lianjun Zhang;

Journal or Book Title: Canadian Journal of Forest Research

Volume/Issue: 36/4

Page Number(s): 996-1005

Year Published: 2006

In recent years, geographically weighted regression (GWR) has become popular for modeling spatial heterogeneity in a regression context. However, the current weighting function used in GWR only considers the geographical distances of trees in a stand, while the attributes (e.g., tree diameter) of the neighboring trees are totally ignored. In this study, we proposed a new weighting function that combines the “geographical space” and “attribute space” between the subject tree and its neighbors, such that (1) neighbors with greater geographical distances from the subject tree are assigned smaller weights, and (2) at a given geographical distance, neighboring trees with sizes that are similar to that of the subject tree are assigned larger weights. The results indicate that the GWR model with the new spatialattribute weighting function performs better than the one with the spatial weighting function in terms of model residuals and predictions for different spatial patterns of tree locations.

DOI: 10.1139/X05-295

Type of Publication: Journal Article

Tags: center for systems integration and sustainability


Authors

Jianguo

Jianguo "Jack" Liu
liuji@msu.edu

You Might Also Be Interested In

Accessibility Questions:

For questions about accessibility and/or if you need additional accommodations for a specific document, please send an email to ANR Communications & Marketing at anrcommunications@anr.msu.edu.