What are two spatial relationships in statistical terms related to spatial dependence and heterogeneity?

• A. Second-order (local or spatial autocorrelation)
• B. First-order (global)
• C. First-order spatial effects (global) and second-order (local or spatial autocorrelation)
• D. Temporal trend

Answer: (C)

Two important ways to describe spatial data are the global mean pattern and the way values relate to each other across space.

First-order spatial effects describe global patterns in the mean across the study area—how the average value changes when you move from one location to another, capturing broad, system-wide trends. Second-order effects capture spatial dependence or autocorrelation—the way observations co-vary with nearby observations, which can be assessed globally or locally. This combination directly addresses both spatial dependence and spatial heterogeneity.

Temporal trends aren’t spatial in nature, and focusing on only one aspect (either global mean or local autocorrelation) misses the other. By pairing global first-order effects with second-order spatial autocorrelation, you acknowledge both the overarching spatial pattern and the localized relationships among values.