This paper demonstrates methods that may be combined to characterize otherwise undetectable spatial heterogeneity in stated preference willingness to pay (WTP) estimates that may occur at multiple geospatial scales. These include methods applicable to large-scale analysis with diffuse policy impacts and uncertainty regarding the appropriate scales over which spatial patterns should be evaluated. Illustrated methods include spatial interpolation and multiscale analysis of hot/cold spots using local indicators of spatial association. An application to threatened and endangered marine species illustrates the empirical findings that emerge. Findings include large-scale clustering of nonuse WTP estimates at multiple scales of analysis.