Context: Knowledge about spatial patterns of human dimensions data within landscape ecology is nascent despite its importance in natural resources management decision-making. We explored this topic within the context of utility roadside forest vegetation management, a complex situation involving ecological, cultural, and aesthetic aspects of forests and reliable power.Objectives We applied spatial interpolation to investigate patterns of human attitudes toward exurban roadside vegetation management data across an exurban landscape.Methods Mail surveys (n = 1962) were used to collect social science data from residents in four areas of Connecticut, USA. For each area, three attitudes variables were evaluated for spatial autocorrelation using Moran's I statistic. Based on identi ed autocorrelation distance or scale, attitudes were interpolated using inverse distance weighting. Model validation of interpolated surfaces was completed using root mean square error.Results: Statistically signi cant spatial autocorrelation was present for ve of 12 study area-attitude pairings at variable distances. Accuracy of interpolations also varied among study areas, suggesting that the choice of spatial scale of analysis in uenced model results.Conclusions: Social processes within the exurban landscape were spatially heterogeneous and multiscalar for the same variables in different locations, exemplifying the complexity of social processes within exurban land use. Interpolation assumptions often applied toward ecological studies did not work well for social processes studied in this analysis. Results demonstrated the importance of understanding spatial dimensions at which social processes operate and, therefore, may in uence ecological outcomes of the roadside forest within the context of state-level natural resources management and policy.