HDMR, High Dimensional Model Representation is one of the recently developed tool in the approximation of multivaniate functions. It is based on multivariate integration over orthogonal geometries under product type weight functions and uses a divide-and-conquer algorithm such that the target function is separated into components in ascending multivariance. In almost all applications HDMR is desired to be truncated at constant, univariate, or at most, bivariate components as approximations. The approximating quality of these components increases as the target functions additive nature dominates. Here, our purpose is to investigate the role of the geometric scale of HDMR be anticipating to get efficiency in the small scale geometries. The ultimate goal of this approach is to develop a new HDMR like finite elements.