The Analyze Patterns toolset contains tools that identify, quantify, and visualize spatial patterns in feature data.
This toolset uses distributed processing to complete analytics on your GeoAnalytics Server.
Calculates a magnitude-per-unit area from point features that fall within a neighborhood around each cell.
Summarizes a set of points into a netCDF data structure by aggregating them into space-time bins. Within each bin, the points are counted, and specified attributes are aggregated. For all bin locations, the trend for counts and summary field values are evaluated.
Given a set of features, identifies statistically significant hot spots and cold spots using the Getis-Ord Gi* statistic.
Finds clusters of point features in surrounding noise based on their spatial or spatiotemporal distribution.
Creates models and generates predictions using an adaptation of Leo Breiman's random forest algorithm, which is a supervised machine learning method. Predictions can be performed for both categorical variables (classification) and continuous variables (regression). Explanatory variables can take the form of fields in the attribute table of the training features. In addition to validation of model performance based on the training data, predictions can be made to features.
Performs generalized linear regression (GLR) to generate predictions or to model a dependent variable in terms of its relationship to a set of explanatory variables. This tool can be used to fit continuous (OLS), binary (logistic), and count (Poisson) models.
Performs Geographically Weighted Regression (GWR), which is a local form of linear regression that is used to model spatially varying relationships.