Home > Publications . Search All . Browse All . Country . Browse PSC Pubs . PSC Report Series

PSC In The News

RSS Feed icon

Eisenberg says college athletes much less likely than other students to seek help with mental health conditions

Mitchell finds children who lose fathers suffer at cellular level

Seefeldt says hard work alone won't allow poor to reach middle-class status in America

More News

Highlights

Neal Krause wins GSA's Robert Kleemeier Award

U-M awarded $58 million to develop ideas for preventing and treating health problems

Bailey, Eisenberg , and Fomby promoted at PSC

Former PSC trainee Eric Chyn wins PAA's Dorothy S. Thomas Award for best paper

More Highlights

A new spatial-attribute weighting function for geographically weighted regression

Publication Abstract

Shi, H.J., L.J. Zhang, and Jianguo Liu. 2006. "A new spatial-attribute weighting function for geographically weighted regression." Canadian Journal of Forest Research, 36(4): 996-1005.

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.

Browse | Search : All Pubs | Next