Estimating geographical ranges of intra-specific evolutionary lineages is crucial to the fields of biogeography, evolution, and biodiversity conservation. Models of isolation mechanisms often consider multiple distances in order to explain genetic divergence. Yet, the available methods to estimate the geographical ranges of lineages are based on direct geographical distances, neglecting other distance metrics that can better explain the spatial genetic structure. We extended the phylogeographical interpolation method (phylin) in order to accommodate user-defined distance metrics and to incorporate the uncertainty associated with genetic distance calculation. These new features were tested with simulated and empirical data sets. Multiple distance matrices were generated including geographical, resistance, and environmental distances to derive maps of lineage occurrence. The new additions to this method improved the ability to predict lineage occurrence, even with low sample size. We used a regression framework to quantify the relationship between the genetic divergence and competing distance matrices representing potential isolation processes that are subsequently used in the interpolation process. Including uncertainty in tree topology and the different distance matrices improved the robustness of the variograms, allowing a better fit of the theoretical model of spatial dependence. The improvements to the method increase its potential application in other fields. Accurately mapping genetic divergence can help to locate potential contact zones between lineages as well as barriers to gene flow, which has a broad interest in biogeographical and evolutionary studies. Additionally, conservation efforts could benefit from the integration of genetic variation and landscape features in a spatially explicit framework.