The sustainability of rural development depends on the distribution of the social and environmental resources needed to maintain and improve the vitality of rural areas. Here we examine the complexity of measuring patterns of distribution using examples of socioeconomic data on rural poverty and affluence as well as data on environmental quality and species richness. We demonstrate how changes in the base spatial units used for analysis have different effects on different measures of inequality. The effects of such changes in spatial resolution also depend on the underlying processes that generate the data. The results of our investigations into the effects of scale on the assessment of inequality suggest that, where data come from both the social and natural science sources, the most appropriate level for analysis is that of the finest common resolution. This may result in redundancy of effort for some types of data but any such disadvantage is offset by the benefits of identifying inequalities that are masked at coarser resolutions.
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