Even if many research projects in population genetics and conservation biology collect a quantity of spatially located biological samples, and despite its present predominance in Science and its direct application to concerns of public society (health, food), molecular data were heretofore rarely studied by the GIScience community. Biotechnologies make it possible to measure this growing amount of genetic data, and GIScience holds promise for being one of the appropriate ways to investigate this information from a complementary point of view, which is somewhat unique to the traditional field of life sciences. In this paper, I describe a novel spatial analysis method (SAM) to detect regions of the genome being shaped by natural selection. This operation is essential as it gives the possibility to understand which genes are involved in adaptation processes. SAM is the first method to tackle this issue from the environmental angle: with the contribution of GIS, environmental variables and molecular data, it applies multiple univariate logistic regressions to test for association between targeted genomic regions and environmental variables. Several applications to animals and plants demonstrated a strong correspondence between SAM results and those obtained with a standard population genetics approach. In the future, such a method may accelerate the process of hunting for functional genes at the population level. Indeed, it permits to identify ecological parameters which will help to interpret the role specific regions of the genome may play, likely to improve our understanding of the genetic mechanisms of evolution.