Which term describes local spatial patterns of association such as clustering of similar values?

• A. Second-order (local or spatial autocorrelation)
• B. First-order (global)
• C. Spatial lag
• D. Spatial heterogeneity

Answer: (A)

Local spatial autocorrelation describes how nearby locations tend to have similar values, creating clusters where high values cluster with high values and low values cluster with low values. This is a second-order measure because it considers the relationship between observations across space, not just the overall distribution of values. By examining the local neighborhood around each location, you can map where these high–high or low–low clusters occur, revealing localized patterns of association.

This differs from first-order or global approaches, which summarize the overall variability without regard to where values occur. Spatial lag refers to the idea that a value at one location may be influenced by neighboring values in a modeling sense, rather than describing a pattern of local clustering itself. Spatial heterogeneity speaks to differences in relationships or processes across space (non-stationarity), rather than a single localized pattern of similar values.