Soil spatial distribution, i.e. the spatial distribution of soils within landscapes, is difficult to predict because numerous processes operate simultaneously, but variably, over time. Quantifications of large areas with an acceptable degree of precision and low in cost require the development of specific methods making the best possible use of existing soil data and auxiliary information such as soil-forming factors. The quantification of the influence of soil-forming factors on soil spatial distribution is seldom performed over large areas such as regions. This study aimed to quantify the relationship between soil spatial distribution and the soil-forming factors of geology, topography, climate, and tectonic regime in order to predict soil spatial distribution over a wide region (30,000 km2). The Armorican Massif (western France), a complex basement of Proterozoic and Paleozoic rocks affected by recent tectonic activity and characterized by variations in topography and climate, was chosen as the study site. Detailed soil maps (1:25,000) were used to describe soil spatial distribution along transects. An ANOVA performed on 314 transects showed a high correlation between the occurrence of soils with particular features (namely redoximorphic, leached, glossic, and albic) and geological substrate, uplift ratio, mean slope gradient, and net rainfall. No such correlation was found with fluvic soils. These soil-forming factors seem to act through saprolite quality and erosion processes, which in turn control the development of soil features. A quantification of the relationship between soil features and soil-forming factors was performed by regression analysis in order to allow further prediction of the soil spatial distribution over the entire Armorican Massif. These results revealed and quantified the hitherto unrecognized role of tectonism on soil distribution and its relative importance in respect to other soil-forming factors. Finally, such an analysis, which is based on existing maps, can help to describe, quantify, and predict detailed soil spatial distribution at smaller scales.