Geospatial Correlation Models
Definition
Geospatial correlation models quantify relationships between variables that vary by place—e.g., air quality and asthma rates or tree canopy and heat. They accommodate spatial autocorrelation and non‑stationarity using techniques such as spatial lag/error models, geographically weighted regression, and multiscale geographically weighted models. The aim is to infer plausible relationships without mistaking proximity effects for causality.
Application
Public health agencies explore environmental justice, utilities model outage susceptibility, and retailers correlate store performance with neighborhood attributes. Proper diagnostics guard against overclaiming in policy contexts.
FAQ
Why is ordinary regression inadequate for mapped data?
Residuals are spatially dependent, violating independence assumptions. Spatial models absorb neighborhood influence, yielding unbiased estimates and realistic uncertainties.
What’s the danger of correlated covariates like income and education?
Multicollinearity inflates variance. Use variance inflation factors, dimensionality reduction, or domain‑guided selection to stabilize estimates.
How do we communicate limits to decision makers?
Report effect sizes with uncertainty ranges, map residuals, and avoid causal language unless supported by experiments or strong identification strategies.
Can models vary across neighborhoods?
Yes—GWR allows coefficients to change in space. It reveals where relationships strengthen or reverse, guiding localized interventions.