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 regression model based on the values of the dependent variable. Currently, only continuous data is supported, and the parameter is hidden in the Geoprocessing pane. Do not use categorical, count, or binary dependent variables. - Continuous—The dependent variable represents continuous values. This is the default.
| String |

Explanatory Variables | A list of fields that will be used as independent explanatory variables in the regression model. | Field |

Output Features | The new feature class containing the coefficients, residuals, and significance levels of the MGWR model. The feature class will be added to the Contents pane in a group layer. | Feature Class |

Neighborhood Type
| Specifies whether the neighborhood will be a fixed distance or allowed to vary spatially depending on the density of the features. - Number of Neighbors— The neighborhood size will be a specified number of closest neighbors for each feature. Where features are dense, the spatial extent of the neighborhood will be smaller; where features are sparse, the spatial extent of the neighborhood will be larger.
- Distance Band—The neighborhood size will be a constant or fixed distance for each feature.
| String |

Neighborhood Selection Method
| Specifies how the neighborhood size will be determined. - Golden Search—An optimal distance or number of neighbors will be identified by minimizing the AICc value using the Golden Search algorithm. This option takes the longest time to calculate, especially for large or high-dimensional datasets.
- Gradient Search—An optimal distance or number of neighbors will be identified by minimizing the AICc value using the gradient-based optimization algorithm. This option runs the fastest and requires significantly less memory usage than Golden Search.
- Manual Intervals— A distance or number of neighbors will be identified by testing a range of values and determining the value with the smallest AICc. If the Neighborhood Type parameter is set to Distance Band, the minimum value of this range is provided by the Minimum search distance parameter. The minimum value is then incremented by the value specified in the Search Distance Increment parameter. This is repeated the number of times specified by the Number of Increments parameter. If the Neighborhood Type parameter is set to Number of Neighbors, the minimum value, increment size, and number of increments are provided in the Minimum Number of Neighbors, Number of Neighbors Increment, and Number of Increments parameters, respectively.
- User Defined—The neighborhood size will be specified by either the Number of Neighbors parameter value or the Distance Band parameter value.
| String |

Minimum Number of Neighbors
(Optional) | The minimum number of neighbors that each feature will include in its calculation. It is recommended that you use at least 30 neighbors. | Long |

Maximum Number of Neighbors
(Optional) | The maximum number of neighbors that each feature will include in its calculations. | Long |

Distance Unit
(Optional) | Specifies the unit of distance that will be used to measure the distances between features. - International Feet—Distances will be measured in international feet.
- Statute Miles—Distances will be measured in statute miles.
- US Survey Feet—Distances will be measured in US survey feet.
- Meters—Distances will be measured in meters.
- Kilometers—Distances will be measured in kilometers.
- US Survey Miles—Distances will be measured in US survey miles.
| String |

Minimum Search Distance
(Optional) | The minimum search distance that will be applied to every explanatory variable. It is recommended that you provide a minimum distance that includes at least 30 neighbors for each feature. | Double |

Maximum Search Distance
(Optional) | The maximum neighborhood search distance that will be applied to all variables. | Double |

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. | Double |

Number of Increments
(Optional) | The number of neighborhood sizes to test when using manual intervals. The first neighborhood size is the value of the Minimum Number of Neighbors or Minimum Search Distance parameter. | Long |

Number of Neighbors
(Optional) | The number of neighbors that will be used for the user-defined neighborhood type. | Long |

Distance Band
(Optional) | The size of the distance band that will be used for the user-defined neighborhood type. All features within this distance will be included as neighbors in the local models. | Double |

Number of Neighbors for Golden Search
(Optional) | The customized Golden Search options for individual explanatory variables. For each explanatory variable to be customized, provide the variable, the minimum number of neighbors, and the maximum number of neighbors in the columns. | Value Table |

Number of Neighbors for Manual Intervals
(Optional) | The customized manual intervals options for individual explanatory variables. For each explanatory variable to be customized, provide the minimum number of neighbors, number of neighbors increment, and number of increments in the columns. | Value Table |

User Defined Number of Neighbors
(Optional) | The customized user-defined options for individual explanatory variables. For each explanatory variable to be customized, provide the number of neighbors. | Value Table |

Search Distance for Golden Search
(Optional) | The customized Golden Search options for individual explanatory variables. For each explanatory variable to be customized, provide the variable, the minimum search distance, and the maximum search distance in the columns. | Value Table |

Search Distance for Manual Intervals
(Optional) | The customized manual intervals options for individual explanatory variables. For each variable to be customized, provide the variable, the minimum search distance, search distance increments, and number of increments in the columns. | Value Table |

User Defined Search Distance
(Optional) | The customized user-defined options for individual explanatory variables. For each variable to be customized, provide the variable and the distance band in the columns. | Value Table |

Prediction Locations
(Optional) | A feature class with the locations where estimates will be computed. Each feature in this dataset should contain a value for every explanatory variables specified. The dependent variable for these features will be estimated using the model calibrated for the input feature class data. These feature locations should be close to (within 115 percent of the extent) or within the same study area as the input features. | Feature Layer |

Explanatory Variables to Match
(Optional) | The explanatory variables from the prediction locations that match corresponding explanatory variables from the input features. | Value Table |

Output Predicted Features
(Optional) | The output feature class that will receive dependent variable estimates for every prediction location. | Feature Class |

Robust Prediction
(Optional) | Specifies the features that will be used in the prediction calculations. - Checked—Features with values greater than three standard deviations from the mean (value outliers) and features with weights of 0 (spatial outliers) will be excluded from the prediction calculations but will receive predictions in the output feature class. This is the default.
- Unchecked—Every feature will be used in the 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. - Bisquare—A weight of zero will be assigned to any feature outside the neighborhood specified. This is the default.
- Gaussian—All features will receive weights, but weights become exponentially smaller the farther away they are from the target feature.
| String |

Output Neighborhood Table
(Optional) | A table containing the output statistics of the MGWR model. A bar chart of estimated bandwidths or numbers of neighbors will be included with the output. | Table |

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. If a directory is provided, the rasters will be TIFF (.tif) raster type. | Workspace |

Scale Data
(Optional) | Specifies whether the values of the explanatory and dependent variables will be standardized (also called Z-score standardization) to have a mean zero and a standard deviation one prior to fitting the model. - Checked—The values of the variables will be scaled. The results will contain scaled and unscaled versions of the explanatory variable coefficients.
- Unchecked—The values of the variables will not be scaled. All coefficients will be unscaled and in original data units.
| Boolean |

Number of Neighbors for Gradient Search
(Optional) | The customized Gradient Search options for individual explanatory variables. For each explanatory variable to be customized, provide the variable, the minimum number of neighbors, and the maximum number of neighbors in the columns. | Value Table |

Search Distance for Gradient Search
(Optional) | The customized Gradient Search options for individual explanatory variables. For each explanatory variable to be customized, provide the variable, the minimum search distance, and the maximum search distance in the columns. | Value Table |

### Derived Output

Label | Explanation | Data Type |

Coefficient Raster Layers | The output rasters of explanatory variable coefficients. | Raster |

Output Layer Group | A group layer of the outputs. The group layer name is the Output Features parameter value with _MGWR_Results appended to the end. It contains the standardized residual and a separate sub-group layer for each explanatory variable. Each sub-group layer includes a Coefficient layer and a Significance layer. The group layer will be added to the Contents pane. | Group Layer |