The spatial dependence of located health and/or genetic data can be used to detect clusters likely to reveal disease prevalence or signatures of adaptation associated with characteristics of the local environment (high temperatures, air or water pollution), be it in humans or animals. Measures of spatial dependence are key to detect and visualize spatial patterns in health and/or genetic data because spatial statistics can reveal signals that remain often hidden using thematic mapping. On the basis of the clusters highlighted by these exploratory methods, it is possible to formulate hypotheses about possible environmental or socio-economic causes and to test them with the help of confirmatory statistics.