Whether the goal is to reach decisions among multiple agents, ensure that AI systems are aligned with human preferences, or design better recommender systems, the problem of translating between (ordinal) rankings and (numerical) utilities arises naturally in many contexts. This task is commonly approached by computing embeddings, which represent both the agents doing the ranking (voters) and the items to be ranked (alternatives) in a shared metric space. Here, ordinal preferences are translated into relationships between pairwise distances. Prior work has established that any collection of rankings with n voters and m alternatives (preference profile) can be embedded into d-dimensional Euclidean space for d≥min{n,m-1} under the Euclidean norm and the Manhattan norm. We show that this holds for all p-norms and establish that any pair of rankings can be embedded into R2 under arbitrary norms, significantly expanding the reach of spatial preference models.