An overview of the Analyzing Patterns toolset

Identifying geographic patterns is important for understanding how geographic phenomena behave.

Although you can get a sense of the overall pattern of features and their associated values by mapping them, calculating a statistic quantifies the pattern. This makes it easier to compare patterns for different distributions or different time periods. Often the tools in the Analyzing Patterns toolset are a starting point for more in-depth analyses. Using the Spatial Autocorrelation tool to identify distances where the processes promoting spatial clustering are most pronounced, for example, might help you select an appropriate distance (scale of analysis) to use for investigating hot spots (Hot Spot Analysis).

The tools in the Analyzing Patterns toolset are inferential statistics; they start with the null hypothesis that your features, or the values associated with your features, exhibit a spatially random pattern. They then compute a p-value representing the probability that the null hypothesis is correct (that the observed pattern is simply one of many possible versions of complete spatial randomness). Calculating a probability may be important if you need to have a high level of confidence in a particular decision. If there are public safety or legal implications associated with your decision, for example, you may need to justify your decision using statistical evidence.

The Analyzing Patterns tools provide statistics that quantify broad spatial patterns. These tools answer questions such as, "Are the features in the dataset, or the values associated with the features in the dataset, spatially clustered?". The following table lists the tools available and provides a brief description of each.

Tool	Description
Average Nearest Neighbor	Berechnet einen Index für den nächsten Nachbarn basierend auf der durchschnittlichen Entfernung jedes Features zum nächstgelegenen Nachbar-Feature.
High/Low Clustering	Misst den Grad der Cluster-Bildung von hohen oder niedrigen Werten mit der Getis-Ord General G-Statistik.
Spatial Autocorrelation	Misst die räumliche Autokorrelation basierend auf Feature-Speicherorten und Attributwerten unter Verwendung von Global Moran's I-Statistikwerten.
Multi-Distance Spatial Cluster Analysis (Ripley's k-function)	Bestimmt, ob Features oder die mit Features verknüpften Werte eine statistisch signifikante Cluster-Bildung oder Streuung über einen Entfernungsbereich aufweisen.

Analyzing patterns tools