A Comparison of Spatially Varying Regression Coefficient Estimates Using Geographically Weighted and Spatial‐Filter‐Based Techniques

By Taylor M. Oshan, A. Stewart Fotheringham. 2018

69 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/c99d195af5a560b7288218a3161d87d46ec3e143

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. A. Stewart Fotheringham
2. Levi John Wolf
3. Wei Kang
4. Taylor M. Oshan
5. Ziqi Li

In Global and Planetary Change

1. A. Stewart Fotheringham
2. Taylor M. Oshan

Citations in the corpus

• mgwr: A Python Implementation of Multiscale Geographically Weighted Regression for Investigating Process Spatial Heterogeneity and Scale (2018, background): “In addition, it has been demonstrated that multicollinearity is not inherently more problematic in GWR [17] than a traditional regression and some of the patterns theorized to be associated with multicollinearity may be indicative [18] of reality or due to scale misspecification [19].”