Distance preserving visualization techniques have emerged as one of the fundamental tools for data analysis. One example are the techniques that arrange data instances into two-dimensional grids so that the pairwise distances among the instances are preserved into the produced layouts. Currently, the state-of-the-art approaches produce such grids by solving assignment problems or using permutations to optimize cost functions. Although precise, such strategies are computationally expensive, limited to small datasets or being dependent on specialized hardware to speed up the process. In this paper, we present a new technique, called Distance-preserving Grid (DGrid), that employs a binary space partitioning process in combination with multidimensional projections to create orthogonal regular grid layouts. Our results show that DGrid is as precise as the existing state-of-the-art techniques whereas requiring only a fraction of the running time and computational resources.
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