Label | Explanation | Data Type |
Input Features | The feature class containing the dependent and explanatory variables. | Feature Layer |
Dependent Variable | The numeric field containing the observed values that will be modeled. | Field |
Model Type
| Specifies the type of data that will be modeled.
| String |
Explanatory Variable(s) | A list of fields representing independent explanatory variables in the regression model. | Field |
Output Features | The new feature class containing the dependent variable estimates and residuals. | Feature Class |
Neighborhood Type
| Specifies whether the neighborhood used is constructed as a fixed distance or allowed to vary in spatial extent depending on the density of the features.
| String |
Neighborhood Selection Method
| Specifies how the neighborhood size will be determined. The neighborhood selected with the Golden search and Manual intervals options is based on minimizing the AICc value.
| String |
Minimum Number of Neighbors
(Optional) | The minimum number of neighbors each feature will include in its calculations. It is recommended that you use at least 30 neighbors. | Long |
Maximum Number of Neighbors
(Optional) | The maximum number of neighbors (up to 1000) each feature will include in its calculations. | Long |
Minimum Search Distance
(Optional) | The minimum neighborhood search distance. It is recommended that you use a distance at which each feature has at least 30 neighbors. | Linear Unit |
Maximum Search Distance
(Optional) | The maximum neighborhood search distance. If a distance results in features with more than 1000 neighbors, the tool will use the first 1000 in calculations for the target feature. | Linear Unit |
Number of Neighbors Increment
(Optional) | The number of neighbors by which manual intervals will increase for each neighborhood test. | Long |
Search Distance Increment
(Optional) | The distance by which manual intervals will increase for each neighborhood test. | Linear Unit |
Number of Increments
(Optional) | The number of neighborhood sizes that will be tested starting with the Minimum Number of Neighbors or Minimum Search Distance parameter value. | Long |
Number of Neighbors
(Optional) | The closest number of neighbors (up to 1000) that will be considered for each feature. The number must be an integer between 2 and 1000. | Long |
Distance Band
(Optional) | The spatial extent of the neighborhood. | Linear Unit |
Prediction Locations (Optional) | A feature class containing features representing locations where estimates will be computed. Each feature in this dataset should contain values for all the explanatory variables specified. The dependent variable for these features will be estimated using the model calibrated for the input feature class data. To be predicted, these feature locations should be within the same study area as the Input Features parameter value or be close (within the extent plus 15 percent). | Feature Layer |
Explanatory Variables to Match
(Optional) | The explanatory variables from the Prediction Locations parameter that match corresponding explanatory variables from the Input Features parameter. | Value Table |
Output Predicted Features
(Optional) | The output feature class that will receive dependent variable estimates for each Prediction Location value. | Feature Class |
Robust Prediction
(Optional) | Specifies the features that will be used in prediction calculations.
| Boolean |
Local Weighting Scheme
(Optional) | Specifies the kernel type that will be used to provide the spatial weighting in the model. The kernel defines how each feature is related to other features within its neighborhood.
| String |
Coefficient Raster Workspace (Optional) | The workspace where the coefficient rasters will be created. When this workspace is provided, rasters are created for the intercept and every explanatory variable. This parameter is only available with a Desktop Advanced license. | Workspace |
Scale Data
(Optional) | Specifies whether the values of the explanatory and dependent variables will be scaled to have mean zero and standard deviation one before fitting the model.
| Boolean |
Derived Output
Label | Explanation | Data Type |
Coefficient Raster Layers | The output coefficient rasters. | Raster Layer |