3D Analyst Tools ArcToolbox

Generate Spatial Weights Matrix, Geographically Weighted Regression and Ordinary Least Squares Tools

Generate Spatial Weights Matrix

How to use Generate Spatial Weights Matrix Tool in Arc Toolbox??

Generate Spatial Weights Matrix

Path to access the tool

:

Generate Spatial Weights Matrix Tool, Modeling Spatial Relationships Toolset, Spatial Statistics Tools Toolbox

 

Generate Spatial Weights Matrix

Constructs a spatial weights matrix (.swm) file to represent the spatial relationships among features in a dataset.

1.    Input Feature Class

The feature class for which spatial relationships of features will be assessed.

2.    Unique ID Field

An integer field containing a different value for every feature in the input feature class. If you don't have a Unique ID field, you can create one by adding an integer field to your feature class table and calculating the field values to equal the FID or OBJECTID field.

3.    Output Spatial Weights Matrix File

The full path for the spatial weights matrix file (.swm) you want to create.

4.    Conceptualization of Spatial Relationships

Specifies how spatial relationships among features are conceptualized.

1. INVERSE_DISTANCE—The impact of one feature on another feature decreases with distance.
2. FIXED_DISTANCE—Everything within a specified critical distance of each feature is included in the analysis; everything outside the critical distance is excluded.
3. K_NEAREST_NEIGHBORS—The closest k features are included in the analysis; k is a specified numeric parameter.
4. CONTIGUITY_EDGES_ONLY—Polygon features that share a boundary are neighbors.
5. CONTIGUITY_EDGES_CORNERS—Polygon features that share a boundary and/or share a node are neighbors.
6. DELAUNAY_TRIANGULATION—A mesh of nonoverlapping triangles is created from feature centroids; features associated with triangle nodes that share edges are neighbors.
7. SPACE_TIME_WINDOW—Features within a specified critical distance and specified time interval of each other are neighbors.
8. CONVERT_TABLE—Spatial relationships are defined in a table.

5.    Distance Method (optional)

Specifies how distances are calculated from each feature to neighboring features.

1. EUCLIDEAN—The straight-line distance between two points (as the crow flies)
2. MANHATTAN—The distance between two points measured along axes at right angles (city block); calculated by summing the (absolute) difference between the x- and y-coordinates

6.    Exponent (optional)

Parameter for inverse distance calculation. Typical values are 1 or 2.

7.    Threshold Distance (optional)

Specifies a cutoff distance for Inverse Distance and Fixed Distance conceptualizations of spatial relationships. Enter this value using the units specified in the environment output coordinate system. Defines the size of the Space window for the Space Time Window conceptualization of spatial relationships.

A value of zero indicates that no threshold distance is applied. When this parameter is left blank, a default threshold value is computed based on output feature class extent and the number of features.

8.    Number of Neighbors (optional)

An integer reflecting either the minimum or the exact number of neighbors. For K_NEAREST_NEIGHBORS, each feature will have exactly this specified number of neighbors. For INVERSE_DISTANCE or FIXED_DISTANCE each feature will have at least this many neighbors (the threshold distance will be temporarily extended to ensure this many neighbors, if necessary). When one of the contiguity Conceptualizations of Spatial Relationships is selected, then each polygon will be assigned this minimum number of neighbors. For polygons with fewer than this number of contiguous neighbors, additional neighbors will be based on feature centroid proximity.

9.    Row Standardization (optional)

Row standardization is recommended whenever feature distribution is potentially biased due to sampling design or to an imposed aggregation scheme.

1. Checked—Spatial weights are standardized by row. Each weight is divided by its row sum.
2. Unchecked—No standardization of spatial weights is applied.

10. Input Table (optional)

A table containing numeric weights relating every feature to every other feature in the input feature class. Required fields are the Input Feature Class Unique ID field, NID (neighbor ID), and WEIGHT.

11. Date/Time Field (optional)

A date field with a timestamp for each feature.

12. Date/Time Interval Type (optional)

The units to use for measuring time.

1. SECONDS—Seconds
2. MINUTES—Minutes
3. HOURS—Hours
4. DAYS—Days
5. WEEKS—Weeks
6. MONTHS—30 Days
7. YEARS—Years

13. Date/Time Interval Value (optional)

An Integer reflecting the number of time units comprising the time window.

For example, if you select HOURS for the Date/Time Interval Type and 3 for the Date/Time Interval Value, the time window would be 3 hours; features within the specified space window and within the specified time window would be neighbors.

Geographically Weighted Regression

How to use Geographically Weighted Regression Tool in Arc Toolbox??

Geographically Weighted Regression

Path to access the tool

:

Geographically Weighted Regression Tool, Modeling Spatial Relationships Toolset, Spatial Statistics Tools Toolbox

 

Geographically Weighted Regression

Performs Geographically Weighted Regression (GWR), a local form of linear regression used to model spatially varying relationships.

An enhanced version of this tool has been added to ArcGIS Pro 2.3. This is the tool documentation for the deprecated tool. It is recommended that you upgrade and use the new Geographically Weighted Regression tool in ArcGIS Pro or later.

1.    Input features

The feature class containing the dependent and independent variables.

2.    Dependent variable

The numeric field containing the values that will be modeled.

3.    Explanatory variable(s)

A list of fields representing independent explanatory variables in the regression model.

4.    Output feature class

The output feature class that will receive dependent variable estimates and residuals.

5.    Kernel type

Specifies whether the kernel is constructed as a fixed distance, or if it is allowed to vary in extent as a function of feature density.

1. FIXED—The spatial context (the Gaussian kernel) used to solve each local regression analysis is a fixed distance.
2. ADAPTIVE —The spatial context (the Gaussian kernel) is a function of a specified number of neighbors. Where feature distribution is dense, the spatial context is smaller; where feature distribution is sparse, the spatial context is larger.

6.    Bandwidth method

Specifies how the extent of the kernel will be determined. When AICc (corrected Akaike Information Criterion) or CV (cross validation) is selected, the tool will find the optimal distance or number of neighbors for you. Typically, you will select either AICc or CV when you aren't sure what to use for the distance or number_of_neighbors parameter. Once the tool determines the optimal distance or number of neighbors, however, you'll use the BANDWIDTH_PARAMETER option.

1. AICc—The extent of the kernel is determined using the Akaike Information Criterion.
2. CV—The extent of the kernel is determined using cross validation.
3. BANDWIDTH_PARAMETER—The extent of the kernel is determined by a fixed distance or a fixed number of neighbors. You must specify a value for either the distance or number_of_neighbors parameters.

7.    Distance (optional)

The distance to use when the Kernel type parameter is set to FIXED and the Bandwidth method parameter is set to BANDWIDTH_PARAMETER.

8.    Number of neighbors (optional)

The exact number of neighbors to include in the local bandwidth of the Gaussian kernel when the Kernel type parameter is set to ADAPTIVE and the Bandwidth method parameter is set to BANDWIDTH_PARAMETER.

9.    Weights (optional)

The 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. This is 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, for example, you can use the number of samples as your weight field so that locations with more samples have a larger influence on model calibration than locations with few samples.

10. Coefficient raster workspace (optional)

The full path to the workspace where the coefficient rasters will be created. When this workspace is provided, rasters are created for the intercept and every explanatory variable.

11. Output cell size (optional)

The cell size (a number) or reference to the cell size (a path to a raster dataset) to use when creating the coefficient rasters.

The default cell size is the shortest of the width or height of the extent specified in the geoprocessing environment output coordinate system, divided by 250.

12. Prediction locations (optional)

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.

13. Prediction explanatory variable(s) (optional)

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 correspondence) 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.

14. Output prediction feature class (optional)

The output feature class to receive dependent variable estimates for each feature in the Prediction locations feature class.

Ordinary Least Squares

How to use Ordinary Least Squares Tool in Arc Toolbox??

Ordinary Least Squares

Path to access the tool

:

Ordinary Least Squares Tool, Modeling Spatial Relationships Toolset, Spatial Statistics Tools Toolbox

 

Ordinary Least Squares

Performs global Ordinary Least Squares (OLS) linear regression to generate predictions or to model a dependent variable in terms of its relationships to a set of explanatory variables.

You can access the results of this tool (including the optional report file) from the Results window. If you disable background processing, results will also be written to the Progress dialog box.

1.    Input Feature Class

The feature class containing the dependent and independent variables for analysis.

2.    Unique ID Field

An integer field containing a different value for every feature in the Input Feature Class.

3.    Output Feature Class

The output feature class to receive dependent variable estimates and residuals.

4.    Dependent Variable

The numeric field containing values for what you are trying to model.

5.    Explanatory Variables

A list of fields representing explanatory variables in your regression model.

6.    Output Report File (optional)

The path to the optional PDF file you want the tool to create. This report file includes model diagnostics, graphs, and notes to help you interpret the OLS results.

7.    Coefficient Output Table (optional)

The full path to an optional table that will receive model coefficients, standardized coefficients, standard errors, and probabilities for each explanatory variable.

8.    Diagnostic Output Table (optional)

The full path to an optional table that will receive model summary diagnostics.