In order to assess the full systemic impact of anthropogenic pressures on climate and land, it is crucial to account for second-order impacts mediated by bio-geophysical processes, which typically display a high spatial heterogeneity. In this study, we systematically compare different clustering approaches to capture uni- and multivariate spatial patterns in second-order impacts of human activity on climate, land, and water. In a first step, we estimate effect sizes based on simulations from a spatial global vegetation model. In a second step, we approach the question of suitable spatial clustering. Following a top-down approach first, we map the global pattern of second-order impacts on common natural partitions of the Earth, like climate- or vegetation-zones. Cluster validity indices reveal a close alignment between the second-order impacts of land use change on climate and a biogeographic classification. In contrast, the second-order impacts of climate- and land use change on surface water runoff are best captured by the Köppen-Geiger climate zones. Following a bottom-up approach, we employ multivariate spatially constrained clustering to derive an integrative global partition. Several patches of tropical rainforest on the Indomalayan islands as well as large areas of warm grasslands in Australia are identified as high-impact clusters. The results of this study should be considered illustrative as they are based on only one dynamical vegetation model. Nevertheless, they emphasize the local nature of second-order impacts and elucidate both the potential and risks of spatial aggregation.