com.esri.arcgis.geoprocessing.tools.spatialstatisticstools
Class GeographicallyWeightedRegression

java.lang.Object
  com.esri.arcgis.geoprocessing.AbstractGPTool
      com.esri.arcgis.geoprocessing.tools.spatialstatisticstools.GeographicallyWeightedRegression

All Implemented Interfaces:

GPTool

public class GeographicallyWeightedRegression
extends AbstractGPTool

Performs GWR, a local form of linear regression used to model spatially varying relationships. The Geographically Weighted Regression tool is contained in the Spatial Statistics Tools tool box.
Learn more about how Geographically Weighted regression works

Software restrictions: none

Illustration:

Usage tips:

• This tool honors the Environment output coordinate system; . However, feature geometry; is projected to the output coordinate system; after analysis is complete. Consequently, the value entered for the Distance parameter should be specified in the same units as the input feature class. Values entered for the Output Cell Size should be specified in the same units and the output coordinate system.
• Some of the computations done by the GWR tool take advantage of multiple CPUs in order to increase performance, and will automatically use up to 8 threads/CPUs for processing.
• 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. The shape and extent of the bandwidth is dependent on user input for the Kernal Type, Bandwidth Method, Distance, and Number of Features parameters with one restriction: when the number of neighboring features would exceed 1000, only the closest 1000 are incorporated into each local equation.
• In global regression models, such as OLS; , results are unreliable when two or more variables exhibit multicollinearity (when two or more variables are redundant or together tell the same "story"). GWR builds a local regression equation for each feature in the dataset. When the values for a particular explanatory variable cluster spatially, you will very likely have problems with local multicolliearity. The condition number in the Output feature class indicates when results are unstable due to local multicollinearity. As a rule of thumb, do not trust results for features with a condition number larger than 30, equal to Null or, for shapefiles, equal to -1.7976931348623158e+308.
• Caution should be used when including nominal/categorical data in a GWR model. Where categories cluster spatially, there is strong risk of encountering local collinearity issues. The condition number included in the GWR output indicates when local collinearity is a problem (a condition number greater than zero or set to Null). Results in the presence of local collinearity are unstable.
• Don't use dummy explanatory variables to represent different spatial regimes in a GWR model. Because GWR allows coefficients to vary, these dummy variables are unnecessary, and if included, will create problems with local collinearity.
• GWR should be applied to datasets with several hundred features for best results. It is not an appropriate method for small datasets. It will not work with multipoint data.
• To better understand regional variation among the coefficients of your explanatory variables, examine the raster coefficient surfaces created by GWR. For polygon data, you can use graduated color rendering on each coefficient field in the Output Feature Class to observe changes across your study area.
• If a prediction 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. Statistically significant spatial autocorrelation of the regression residuals and/or unexpected spatial variation among the coefficients of one or more explanatory variables suggests that your model is misspecified. You should make every effort (through OLS residual analysis and GWR coefficient variation analysis) to discover what these key missing variables are so they may be included in the model.
• Always question whether or not it makes sense for an explanatory variable to be non-stationary. For example, suppose you are modeling the density of a particular plant species as a function of several variables including ASPECT. If you find that the coefficient for the ASPECT variable changes across the study area, you are likely seeing evidence of a key missing explanatory variable (perhaps prevailence of competing vegetation, for example). You should make every effort to include all key explanatory variables in your regression model.
• Whenever using shapefiles, keep in mind that they cannot store null values. Tools or other procedures that create shapefiles from non-shapefile inputs may, consequently, store null values as zero or as some very small negative number (-DBL_MAX = -1.7976931348623158e+308). This can lead to unexpected results.
• When the result of a computation is infinity or undefined, the result for non-shapefiles will be Null; for shapefiles the result will be -DBL_MAX = -1.7976931348623158e+308.

Field Summary

Fields inherited from class com.esri.arcgis.geoprocessing.AbstractGPTool

vals

Constructor Summary

GeographicallyWeightedRegression()
Creates the Geographically Weighted Regression tool with defaults.

GeographicallyWeightedRegression(Object inFeatures, Object dependentField, Object explanatoryField, Object outFeatureclass, String kernelType, String bandwidthMethod)
Creates the Geographically Weighted Regression tool with the required parameters.

Method Summary

String	getBandwidthMethod() Returns the Bandwidth method parameter of this tool .
Object	getCellSize() Returns the Output cell size parameter of this tool .
Object	getCoefficientRasterWorkspace() Returns the Coefficient raster workspace parameter of this tool .
Object	getDependentField() Returns the Dependent variable parameter of this tool .
double	getDistance() Returns the Distance parameter of this tool .
Object	getExplanatoryField() Returns the Explanatory variable(s) parameter of this tool .
Object	getInFeatures() Returns the Input feature class parameter of this tool .
Object	getInPredictionLocations() Returns the Prediction locations parameter of this tool .
String	getKernelType() Returns the Kernel type parameter of this tool .
int	getNumberOfNeighbors() Returns the Number of neighbors parameter of this tool .
Object	getOutFeatureclass() Returns the Output feature class parameter of this tool .
Object	getOutPredictionFeatureclass() Returns the Output prediction feature class parameter of this tool .
Object	getOutRegressionRasters() Returns the Output regression rasters parameter of this tool (Read only).
Object	getOutTable() Returns the Output table parameter of this tool (Read only).
Object	getPredictionExplanatoryField() Returns the Prediction explanatory variable(s) parameter of this tool .
String	getToolboxAlias() Returns the alias of the tool box containing this tool.
String	getToolboxName() Returns the name of the tool box containing this tool.
String	getToolName() Returns the name of this tool.
Object	getWeightField() Returns the Weights parameter of this tool .
void	setBandwidthMethod(String bandwidthMethod) Sets the Bandwidth method parameter of this tool .
void	setCellSize(Object cellSize) Sets the Output cell size parameter of this tool .
void	setCoefficientRasterWorkspace(Object coefficientRasterWorkspace) Sets the Coefficient raster workspace parameter of this tool .
void	setDependentField(Object dependentField) Sets the Dependent variable parameter of this tool .
void	setDistance(double distance) Sets the Distance parameter of this tool .
void	setExplanatoryField(Object explanatoryField) Sets the Explanatory variable(s) parameter of this tool .
void	setInFeatures(Object inFeatures) Sets the Input feature class parameter of this tool .
void	setInPredictionLocations(Object inPredictionLocations) Sets the Prediction locations parameter of this tool .
void	setKernelType(String kernelType) Sets the Kernel type parameter of this tool .
void	setNumberOfNeighbors(int numberOfNeighbors) Sets the Number of neighbors parameter of this tool .
void	setOutFeatureclass(Object outFeatureclass) Sets the Output feature class parameter of this tool .
void	setOutPredictionFeatureclass(Object outPredictionFeatureclass) Sets the Output prediction feature class parameter of this tool .
void	setPredictionExplanatoryField(Object predictionExplanatoryField) Sets the Prediction explanatory variable(s) parameter of this tool .
void	setWeightField(Object weightField) Sets the Weights parameter of this tool .

Methods inherited from class com.esri.arcgis.geoprocessing.AbstractGPTool

getParameterValues, toString

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

GeographicallyWeightedRegression

public GeographicallyWeightedRegression()

Creates the Geographically Weighted Regression tool with defaults.

Initializes the array of tool parameters with the default values specified when the tool was created.

GeographicallyWeightedRegression

public GeographicallyWeightedRegression(Object inFeatures,
                                        Object dependentField,
                                        Object explanatoryField,
                                        Object outFeatureclass,
                                        String kernelType,
                                        String bandwidthMethod)

Creates the Geographically Weighted Regression tool with the required parameters.

Initializes the array of tool parameters with the values as specified for the required parameters and with the default values for the other parameters.

Parameters:

inFeatures - Feature Layer, the feature class containing the dependent and independent variables.

dependentField - Field, the numeric field containing values for what you are trying to model.

explanatoryField - Field, a list of fields representing independent explanatory variables in your regression model.

outFeatureclass - Feature Class, the output feature class to receive dependent variable estimates and residuals.

kernelType - String, specifies if the kernal is always fixed or if it is allowed to vary in extent as a function of feature density.

bandwidthMethod - null

Method Detail

getInFeatures

public Object getInFeatures()

Returns the Input feature class parameter of this tool . This parameter is Feature Layer, the feature class containing the dependent and independent variables. This is a required parameter.

Returns:

the Input feature class

setInFeatures

public void setInFeatures(Object inFeatures)

Sets the Input feature class parameter of this tool . This parameter is Feature Layer, the feature class containing the dependent and independent variables. This is a required parameter.

Parameters:

inFeatures - Feature Layer, the feature class containing the dependent and independent variables.

getDependentField

public Object getDependentField()

Returns the Dependent variable parameter of this tool . This parameter is Field, the numeric field containing values for what you are trying to model. This is a required parameter.

Returns:

the Dependent variable

setDependentField

public void setDependentField(Object dependentField)

Sets the Dependent variable parameter of this tool . This parameter is Field, the numeric field containing values for what you are trying to model. This is a required parameter.

Parameters:

dependentField - Field, the numeric field containing values for what you are trying to model.

getExplanatoryField

public Object getExplanatoryField()

Returns the Explanatory variable(s) parameter of this tool . This parameter is Field, a list of fields representing independent explanatory variables in your regression model. This is a required parameter.

Returns:

the Explanatory variable(s)

setExplanatoryField

public void setExplanatoryField(Object explanatoryField)

Sets the Explanatory variable(s) parameter of this tool . This parameter is Field, a list of fields representing independent explanatory variables in your regression model. This is a required parameter.

Parameters:

explanatoryField - Field, a list of fields representing independent explanatory variables in your regression model.

getOutFeatureclass

public Object getOutFeatureclass()

Returns the Output feature class parameter of this tool . This parameter is Feature Class, the output feature class to receive dependent variable estimates and residuals. This is a required parameter.

Returns:

the Output feature class

setOutFeatureclass

public void setOutFeatureclass(Object outFeatureclass)

Sets the Output feature class parameter of this tool . This parameter is Feature Class, the output feature class to receive dependent variable estimates and residuals. This is a required parameter.

Parameters:

outFeatureclass - Feature Class, the output feature class to receive dependent variable estimates and residuals.

getKernelType

public String getKernelType()

Returns the Kernel type parameter of this tool . This parameter is String, specifies if the kernal is always fixed or if it is allowed to vary in extent as a function of feature density. This is a required parameter.

Returns:

the Kernel type

setKernelType

public void setKernelType(String kernelType)

Sets the Kernel type parameter of this tool . This parameter is String, specifies if the kernal is always fixed or if it is allowed to vary in extent as a function of feature density. This is a required parameter.

Parameters:

kernelType - String, specifies if the kernal is always fixed or if it is allowed to vary in extent as a function of feature density.

getBandwidthMethod

public String getBandwidthMethod()

Returns the Bandwidth method parameter of this tool . This is a required parameter.

Returns:

the Bandwidth method

setBandwidthMethod

public void setBandwidthMethod(String bandwidthMethod)

Sets the Bandwidth method parameter of this tool . This is a required parameter.

Parameters:

bandwidthMethod - null

getDistance

public double getDistance()

Returns the Distance parameter of this tool . This is an optional parameter.

Returns:

the Distance

setDistance

public void setDistance(double distance)

Sets the Distance parameter of this tool . This is an optional parameter.

Parameters:

distance - null

getNumberOfNeighbors

public int getNumberOfNeighbors()

Returns the Number of neighbors parameter of this tool . This parameter is Integer, an integer reflecting the exact number of neighbors to include in the local bandwidth of the Gaussian kernel in cases where the user selects ADAPTIVE for kernel type and BANDWIDTH PARAMETER for Bandwidth method. This is an optional parameter.

Returns:

the Number of neighbors

setNumberOfNeighbors

public void setNumberOfNeighbors(int numberOfNeighbors)

Sets the Number of neighbors parameter of this tool . This parameter is Integer, an integer reflecting the exact number of neighbors to include in the local bandwidth of the Gaussian kernel in cases where the user selects ADAPTIVE for kernel type and BANDWIDTH PARAMETER for Bandwidth method. This is an optional parameter.

Parameters:

numberOfNeighbors - Integer, an integer reflecting the exact number of neighbors to include in the local bandwidth of the Gaussian kernel in cases where the user selects ADAPTIVE for kernel type and BANDWIDTH PARAMETER for Bandwidth method.

getWeightField

public Object getWeightField()

Returns the Weights parameter of this tool . This parameter is Field, the numeric field containing a spatial weighting for individual features. 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). This is an optional parameter.

Returns:

the Weights

setWeightField

public void setWeightField(Object weightField)

Sets the Weights parameter of this tool . This parameter is Field, the numeric field containing a spatial weighting for individual features. 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). This is an optional parameter.

Parameters:

weightField - Field, the numeric field containing a spatial weighting for individual features. 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).

getCoefficientRasterWorkspace

public Object getCoefficientRasterWorkspace()

Returns the Coefficient raster workspace parameter of this tool . This is an optional parameter.

Returns:

the Coefficient raster workspace

setCoefficientRasterWorkspace

public void setCoefficientRasterWorkspace(Object coefficientRasterWorkspace)

Sets the Coefficient raster workspace parameter of this tool . This is an optional parameter.

Parameters:

coefficientRasterWorkspace - null

getCellSize

public Object getCellSize()

Returns the Output cell size parameter of this tool . This parameter is Analysis Cell Size, the default cell size is the shortest of the width or height of the extent specified in the Environment output coordinate system, divided by 250. This is an optional parameter.

Returns:

the Output cell size

setCellSize

public void setCellSize(Object cellSize)

Sets the Output cell size parameter of this tool . This parameter is Analysis Cell Size, the default cell size is the shortest of the width or height of the extent specified in the Environment output coordinate system, divided by 250. This is an optional parameter.

Parameters:

cellSize - Analysis Cell Size, the default cell size is the shortest of the width or height of the extent specified in the Environment output coordinate system, divided by 250.

getInPredictionLocations

public Object getInPredictionLocations()

Returns the Prediction locations parameter of this tool . This parameter is Feature Layer, a 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. This is an optional parameter.

Returns:

the Prediction locations

setInPredictionLocations

public void setInPredictionLocations(Object inPredictionLocations)

Sets the Prediction locations parameter of this tool . This parameter is Feature Layer, a 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. This is an optional parameter.

Parameters:

inPredictionLocations - Feature Layer, a 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.

getPredictionExplanatoryField

public Object getPredictionExplanatoryField()

Returns the Prediction explanatory variable(s) parameter of this tool . This parameter is Field, a 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. This is an optional parameter.

Returns:

the Prediction explanatory variable(s)

setPredictionExplanatoryField

public void setPredictionExplanatoryField(Object predictionExplanatoryField)

Sets the Prediction explanatory variable(s) parameter of this tool . This parameter is Field, a 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. This is an optional parameter.

Parameters:

predictionExplanatoryField - Field, a 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.

getOutPredictionFeatureclass

public Object getOutPredictionFeatureclass()

Returns the Output prediction feature class parameter of this tool . This is an optional parameter.

Returns:

the Output prediction feature class

setOutPredictionFeatureclass

public void setOutPredictionFeatureclass(Object outPredictionFeatureclass)

Sets the Output prediction feature class parameter of this tool . This is an optional parameter.

Parameters:

outPredictionFeatureclass - null

getOutTable

public Object getOutTable()

Returns the Output table parameter of this tool (Read only). This is an derived parameter.

Returns:

the Output table

getOutRegressionRasters

public Object getOutRegressionRasters()

Returns the Output regression rasters parameter of this tool (Read only). This is an derived parameter.

Returns:

the Output regression rasters

getToolName

public String getToolName()

Returns the name of this tool.

Returns:

the tool name

getToolboxName

public String getToolboxName()

Returns the name of the tool box containing this tool.

Returns:

the tool box name

getToolboxAlias

public String getToolboxAlias()

Returns the alias of the tool box containing this tool.

Returns:

the tool box alias