What does Moran's Index measure?

Moran's Index is a statistical measure used to evaluate spatial autocorrelation, which refers to the degree to which a set of spatial data points correlate with each other. Specifically, it assesses whether similar values are clustered in space or are dispersed. A positive Moran's Index indicates that high values are located near high values, and low values are located near low values, suggesting a clustering effect. Conversely, a negative index suggests a tendency for dissimilar values to be near each other, indicating dispersion.

In the context of GIS and spatial analysis, understanding spatial autocorrelation is crucial, as it helps analysts identify patterns and relationships in geographic data. This can inform decision-making, such as in urban planning or resource allocation, where the spatial arrangement of data points can significantly influence outcomes.

The other options do not appropriately describe Moran's Index's function. Data centrality relates to the middle point of a dataset and not its spatial relationships. Temporal trends refer to changes over time rather than spatial position. Data frequency denotes how often data occurs within a dataset, which is unrelated to the spatial correlations that Moran's Index addresses.