Landscape connectivity promotes dispersal and other types of movement, including foraging activity; consequently, the inclusion of connectivity concept is a priority in conservation and landscape planning in response to fragmentation. Urban planners expect the scientific community to provide them with an easy, but scientifically rigorous, method to identify highly connecting contexts in landscapes. The least-cost paths (LCP) method is one of the simplest resistance-based models that could be a good candidate to spatially identify areas where movement is potentially favored in a given landscape. We tested the efficiency of LCP predictions to detect highly connecting landscape contexts facilitating individual movements compared to those performed in un-connecting landscape contexts. We used a landscape-level behavioral experiment based on a translocation protocol and individual repeated measures. In the city of Rennes (France), 30 male hedgehogs (Erinaceus europaeus) were translocated and radio-tracked in both highly connecting and un-connecting contexts, respectively, which were determined by the presence and absence of modelled LCPs. Individual movement patterns were compared between the two predicted contexts. Individuals travelled longer distances, moved faster, and were more active in the highly connecting contexts compared to the un-connecting contexts. Moreover, in highly connecting contexts, hedgehog movement followed LCP orientation, with individuals using more wooded habitats than other land cover class. By using a rigorous experimental design, this study validated the ecological relevance of LCP analysis to identify highly connecting areas, and could be easily implemented by urban landscape planners.