The Spatial Component Utilities (Moran Eigenvectors) toolset contains tools related to creating and using spatial components (called Moran eigenvectors). The tools are typically run before running analysis tools in the Spatial Statistics toolbox, for example, to create explanatory variables that can be used by various tools in the Modeling Spatial Relationships toolset or to create spatial weights matrix files that can be used in many spatial statistics tools to define the neighborhoods and weights between features.
The Spatial Component Utilities toolset contains the following tools:
| Tool | Description |
|---|---|
|
Selects the spatial weights matrix (SWM) from a set of candidate SWMs that best represents the spatial patterns (such as trends or clusters) of one or more numeric fields. | |
Creates a set of spatial component fields that best describe the spatial patterns of one or more numeric fields and serve as useful explanatory variables in a prediction or regression model. | |
Decomposes a feature class and neighborhood into a set of spatial components. The components represent potential spatial patterns among the features, such as clusters or trends. | |
Creates a spatially filtered version of an input field. The filtered variable will have no statistically significant spatial clustering but will maintain the core statistical properties of the field. The spatially filtered version of the field can then be used in analytical workflows (such as correlation or regression analysis) that assume the values at each location are spatially independent (not spatially clustered). |