Differences in spatial units among spatial data often complicate analyses. Spatial unit conversion, called areal interpolation, is often applied to address this problem. Of the many proposed areal interpolation methods, few consider spatial autocorrelation, which is the general property of spatial data. In this paper an areal interpolation method is constructed by combining a spatial process model, a primal model in spatial statistics, and the linear-regression-based areal interpolation method. The primal advantages of our methods are twofold: It considers both spatial autocorrelation and the volume-preserving property; it is more practical than other spatial-statistics-based areal interpolation methods. A case study on the areal interpolation of the density of employee numbers is provided to check the properties of our method. This case study shows that our method succeeds in improving predictive accuracy. Furthermore, the areal interpolation result indicates that our method, which provides a smooth interpolation map, is appropriate to model the underlying process of spatially aggregated data. These results indicate that the consideration of spatial autocorrelation is important for areal interpolation.