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We explore methodologies that allow conclusions to be drawn from the large Poverty Environment Network (PEN) dataset. First, we characterize the diverse parts of the tropics in terms of factors that influence forest resources, access and livelihoods. Secondly, for the conclusions drawn from the site-based analysis to be useful for roader policy recommendations, we need to know the extrapolation domains. We compared the characteristics of landscapes where PEN studies took place with overall tropical landscapes, and those of PEN villages with 'random' villages. Both methods rely on variables derived from global data sets using spatial analysis. Thirdly, we study the relationships of livelihoods and forests using multilevel regression analysis. Our study suggests that for global comparative analysis, it is necessary to identify the overall variation of the system of interest, to define the extrapolation domain of the samples/study sites, and to address relationships that by nature involve multiple scale processes. Available global data set, advances in spatial techniques and relatively cheap computer storage and computational power allow such analysis to be done, adding value through global comparative analysis of the interesting site-level findings.