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One of the fundamental problems in the area of large-scale optimization is to study locality features of spatially distributed optimization problems in which the variables are coupled in the cost function as well as constraints. Such problems can motivate the development of fast and well-conditioned distributed algorithms. In this paper, we study spatial locality features of large-scale multi-parametric quadratic programming (MPQP) problems with linear inequality constraints. Our main application focus is receding horizon control of spatially distributed linear systems with input and state constraints. We propose a new approach for analysis of large-scale MPQP problems by blending tools from duality theory with operator theory. The class of spatially decaying matrices is introduced to capture couplings between optimization variables in the cost function and the constraints. We show that the optimal solution of a convex MPQP is piecewise affine- represented as convolution sums. More importantly, we prove that the kernel of each convolution sum decays in the spatial domain at a rate proportional to the inverse of the corresponding coupling function of the optimization problem.