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

PSC In The News

RSS Feed icon

Inglehart says European social democracy is a victim of its own success

Bound, Khanna, and Morales find multiple effects of H1-B visas on US tech industry

Prescott says public criminal registries have downside

More News

Highlights

Heather Ann Thompson wins Bancroft Prize for History for 'Blood in the Water'

Michigan ranks in USN&WR top-10 grad schools for sociology, public health, labor economics, social policy, social psychology

Paula Lantz to speak at Women in Health Leadership Summit, March 24, 2:30-5:30 Michigan League

New site highlights research, data, and publications of Relationship Dynamics and Social Life study

More Highlights

Next Brown Bag

Mon, March 20, 2017, noon:
Dean Yang, Taken by Storm

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