The Analyze Patterns toolset contains tools for identifying, quantifying, and visualizing spatial patterns in feature data.
GeoAnalytics Desktop tools provide a parallel processing framework for analysis on a desktop machine using Apache Spark. Through aggregation, regression, detection, and clustering, you can visualize, understand, and interact with big data. These tools work with big datasets and allow you to gain insight into your data through patterns, trends, and anomalies. The tools are integrated and run in AllSource in the same way as other desktop geoprocessing tools.
| Tool | Description |
|---|---|
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Calculates a magnitude-per-unit area from point features that fall within a neighborhood around each cell. | |
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 the random forest algorithm, which is a supervised machine learning method developed by Leo Breiman and Adele Cutler. 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. |