# Interpreting results of GWR - what do the values mean?

I used R to run a Geographically Weighted Regression (GWR) model.

The resulting model comes with a bunch of values that are given in a table. These are:

• sum.w
• X.Intercept, X.Intercept_se (standard error), X.Intercept_se_EDF
• variable1, variable1_se, variable1_se_EDF
• variable2, variable2_se, variable2_se_EDF,
• variableX, ...
• gwr.e
• pred, pred_se, pred_se_1
• localR2

Could you provide a short summary of what the calculated values mean?

• Variable coefficient: I know that for each variable a coefficient is given, which shows the strength of the correlation. But are these values normalized and can be compared between variables? As an example: if variable1 has a coefficient of 5 in a certain area, does a coefficient for variable2 of 10 in the same area mean that its correlation is twice as high?

• Standard error, EDF: What does the standard error tell? And what is the value EDF?

• Sum, gwr.e, Intercept: I am also not sure what sum.w, gwr.e and the Intercept are for.

• Prediction: The prediction values show what y-value is expected through the model.

• Local R2: The local R2 shows the model performance at a local scale.

• have you read any of the literature on GWR? Commented Nov 17, 2020 at 10:33
• sure, but for me it is still unclear what some values mean. E.g. if the coefficients are normalized and can be compared or not. Commented Nov 17, 2020 at 10:54
• then you might need to check the source code Commented Nov 17, 2020 at 11:31
• instead of telling me that I should do more research (what I did, but I didnt understand everything, thats the reason why I asked here), you could either help me by answering my question or pointing me to a paper that explains it in a way that it can be understood Commented Nov 17, 2020 at 11:42