An overview of the Modeling Spatial Relationships toolset
Beyond analyzing spatial patterns, GIS analysis can be used to examine or quantify relationships among features. The Modeling Spatial Relationships tools construct spatial weights matrices or model spatial relationships using regression analyses.
Tools that construct spatial weights matrix files measure how features in a dataset relate to each other in space. A spatial weights matrix is a representation of the spatial structure of your data: the spatial relationships that exist among the features in your dataset.
True spatial statistics integrate information about space and spatial relationships into their mathematics. Some of the tools in the Spatial Statistics toolbox that accept a spatial weights matrix file are Spatial Autocorrelation (Global Moran's I), Cluster and Outlier Analysis (Anselin Local Moran's I), and Hot Spot Analysis (GetisOrd Gi*).
The regression tools provided in the Spatial Statistics Toolbox model relationships among data variables associated with geographic features, allowing you to make predictions for unknown values or to better understand key factors influencing a variable you are trying to model. Regression methods allow you to verify relationships and to measure how strong those relationships are.
Tool 
Description 

Constructs a spatial weights matrix file (.swm) using a Network dataset, defining feature spatial relationships in terms of the underlying network structure.


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

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


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. Results are accessible from the Results window. 