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1、 GWR4 User Manual GWR4 Windows Application for Geographically Weighted Regression Modelling Tomoki Nakaya Update 12 March 2014 Updated 20 Nov 2012 Updated 7 May 2012 3 June 2009 GWR 4 Development Team Tomoki Nakaya (Department of Geography, Ritsumeikan University) Martin Charlton, Paul Lewis, Chris
2、Brunsdon (NCG, National University of Ireland) Jing Yao, Stewart Fotheringham (School of Geography & Geosciences, University of St. Andrews) 1 Contents 1. Introduction . 2 What is GWR4? . 2 New features compared to GWR3.x . 3 Notes for use of GWR4 . 4 2. Installation / Uninstallation . 4 3. Starting
3、 the program, Exiting the program, and Tab design . 5 Five steps in GWR calibration . 6 4. Step 1: The Data Tab . 7 Data preparation . 7 Operations in the data tab page . 9 5. Step 2: The Model Tab . 10 Basic operations: using an example of Gaussian GWR . 11 Semiparametric GWR . 13 Extensions of GWR
4、: GWGLM . 15 Geographically weighted Poisson regression (GWPR) . 15 Geographically weighted logistic regression (GWLR) . 16 Modelling Options . 18 Standardisation . 18 Geographical variability test . 18 LtoG / GtoL variable selection. . 21 6. Step 3: The Kernel Tab . 22 Possible fixed and adaptive k
5、ernel functions for geographical weighting . 23 Bandwidth selection routines. 24 Selection criteria . 26 7. Step 4: The Output Tab . 27 Session Control File . 27 Common output files . 28 The “Prediction at non-regression points” option . 28 8. Step 5: The Execute Tab . 29 The Execute button . 29 Fie
6、lds in a listwise output . 31 Handling a session and batch mode (optional) . 32 Example output . 33 9. References . 39 2 1. Introduction What is GWR4? GWR4 is a new release of a Microsoft Windows-based application software for calibrating geographically weighted regression (GWR) models, which can be
7、 used to explore geographically varying relationships between dependent/response variables and independent/explanatory variables. A GWR model can be considered a type of regression model with geographically varying parameters. A conventional GWR is described by the equation kiikiikixvuy ,),( , where
8、 iy , ikx, and i are, respectively, dependent variable, kth independent variable, and the Gaussian error at location i; (iivu , ) is the x-y coordinate of the ith location; and coefficients ),(iikvu are varying conditionals on the location. Such modelling is likely to attain higher performance than traditional regression models, and reading the coefficients can lead to a new interpretation of t
