How to interpret result of geographically weighted regression?

How to interpret result of geographically weighted regression?

– Geographic Information Systems Stack Exchange Interpreting result of Geographically Weighted Regression (GWR)? I want to use the Geographically Weighted Regression (GWR) to model local relationships between my dependent variable and a set of independent variables.

When to use GWR in a regression model?

Once the independent variables that you wish to retain in the model are identified, and there is a theoretical basis for thinking that the relationships may differ by space, GWR may be an appropriate next step. The regression models that underlie GWR:

How to interpret GWR results in ArcGIS Desktop?

Message window report of overall model results Supplementary table showing model variables and diagnostic results Each of the above outputs is shown and described below as a series of steps for running GWR and interpreting GWR results.

Which is the most important diagnostic in GWR?

Each of the diagnostics reported is described below. Bandwidth or Neighbors: This is the bandwidth or number of neighbors used for each local estimation and is perhaps the most important parameter for Geographically Weighted Regression. It controls the degree of smoothing in the model.

How is a GWR a local regression model?

GWR is a local regression model. Coefficients are allowed to vary. GWR constructs a separate equation for every feature in the dataset incorporating the dependent and explanatory variables of features falling within the bandwidth of each target feature.

When to use no predictions in ArcGIS regression?

If a Prediction locations feature class is provided, but no Prediction explanatory variables are specified, the Output prediction feature class is created with computed coefficients for each location only (no predictions). A regression model is misspecified if it is missing a key explanatory variable.

How is the distance of a regression determined?

The weighting is based on the distance between the regression location and its nearest neighbours, defined as bandwidth. The points in closer proximity to location is given more weight and therefore has more influence on the estimation of than the observations that are further away to location .

How is a GWR different from a fixed effect model?

GWR is instead a collection of local spatial regressions where the dependence between regression coefficients at different data locations is not specified in the model, which results in a fixed effects model with no pooling in estimates.

How is kernel density used in Geographic weighted regression?

The size of the window (i.e., the bandwidth) can be calibrated in various ways, and a kernel density function is used to weight data as a function of distance from the fit point. In this way, the regression equation is modified over geographic space to factor out variation due to changing spatial autocorrelation structure across a landscape.

Which is an example of a GWR analysis?

For our example of GWR analysis, we used the density surface calculated from kernel density estimates of UD wells as the predictor variable, and each of eight chemical constituents (TDS, chloride, chloroform, benzene, bromide, dichloromethane, toluene, and methanol) as dependent variables.