ESRI.ArcGIS.SpatialStatisticsTools
GeographicallyWeightedRegression Class Members
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ESRI.ArcGIS.SpatialStatisticsTools Namespace : GeographicallyWeightedRegression Class




The following tables list the members exposed by GeographicallyWeightedRegression.

Public Constructors

 NameDescription
Public ConstructorGeographicallyWeightedRegression ConstructorOverloaded. Constructor that takes all required parameters for geoprocessor execution.  
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Public Properties

 NameDescription
Public PropertyAliasThe alias for this tool's toolbox.  
Public Propertybandwidth_methodBandwidth method (in)  
Public Propertycell_sizeThe cell size (a number) or reference to the cell size (a pathname to a raster dataset) to use when creating the coefficient rasters. (In, Optional)  
Public Propertycoefficient_raster_workspaceCoefficient raster workspace (in, Optional)  
Public Propertydependent_fieldThe numeric field containing values for what you are trying to model. (In, Required)  
Public PropertydistanceDistance (in, Optional)  
Public Propertyexplanatory_fieldA list of fields representing independent explanatory variables in your regression model. (In, Required)  
Public Propertyin_featuresThe feature class containing the dependent and independent variables. (In, Required)  
Public Propertyin_prediction_locationsA feature class containing features representing locations where estimates should be computed. Each feature in this dataset should contain values for all of the explanatory variables specified; the dependent variable for these features will be estimated using the model calibrated for the input feature class data. (In, Optional)  
Public Propertykernel_typeSpecifies if the kernel is constructed as a fixed distance, or if it is allowed to vary in extent as a function of feature density. (In, Required)  
Public Propertynumber_of_neighborsAn integer reflecting the exact number of neighbors to include in the local bandwidth of the Gaussian kernel whenever the kernel type is ADAPTIVE and the bandwidth method is BANDWIDTH PARAMETER. (In, Optional)  
Public Propertyout_featureclassThe output feature class to receive dependent variable estimates and residuals. (Out, Required)  
Public Propertyout_prediction_featureclassOutput prediction feature class (out, Optional)  
Public Propertyout_regression_rastersOutput regression rasters (out, Optional)  
Public Propertyout_tableOutput table (out, Optional)  
Public PropertyParameterInfoThe parameters used by this tool. For internal use only.  
Public Propertyprediction_explanatory_fieldA list of fields representing explanatory variables in the Prediction Locations feature class. These field names should be provided in the same order (a one-to-one correspondance) as those listed for the input feature class Explanatory variables parameter. If no prediction explanatory variables are given, the output prediction feature class will only contain computed coefficient values for each prediction location. (In, Optional)  
Public PropertyToolboxDirectoryThe directory of this tool's toolbox.  
Public PropertyToolboxNameThe name of this tool's toolbox.  
Public PropertyToolNameThe name of this tool.  
Public Propertyweight_fieldThe numeric field containing a spatial weighting for individual features. This weight field allows some features to be more important in the model calibration process than others. Primarily useful when the number of samples taken at different locations varies, values for the dependent and independent variables are averaged, and places with more samples are more reliable (should be weighted higher). If you have an average of 25 different samples for one location, but an average of only 2 samples for another location, you can use the number of samples as your weight field so that locations with more samples have a larger influence on model calibration that locations with few samples. (In, Optional)  
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