Heavy metal contamination of soil poses a threat to food chains and human health, particularly in mining areas. This study aims to advance the accuracy of mapping heavy metals in soil by incorporating auxiliary information and to assess the soil contamination risk to local residents in a mining area. Pearson correlation coefficients of environmental factors containing each of the eight heavy metals (Zn, As, Cd, Cr, Cu, Ni, Pb, Hg) are utilized to quantitatively assign the auxiliary variables for each heavy metal to reduce false interference and advance prediction accuracy. Ordinary kriging and cokriging are compared to estimate the heavy metal distributions and thus confirm whether and what auxiliary variables work on the distributions of heavy metals. Distribution maps of heavy metals are ramped based on the contamination levels of the soil quality standards of China，and then overlapped with the land-cover types of the study areas. The results show: (1) the distributions of Zn, Cd, Cu, Ni, Pb, and Hg are related to the soil's properties, the distribution of As appears to be associated to Pb and Cu, and Cd prediction accuracy is unaffected by the corporation of auxiliary variables in this study. (2) As, Cd, Hg, Pb, and Zn contaminates most soils in the areas studied, reaching at least Grade I levels near the residential field and Hg reaching the highest level of pollution based on the soil quality standards of China.