During the last decade various spatial data structures have been designed and compared against each other with respect to their performance. Still missing is a lower bound result, e.g. an optimal spatial data clustering, which would allow for the absolute comparison of the performance of the well-known data structures with the optimum. In this paper, we address the static situation where the data is known in beforehand. An optimal data clustering for this setting will also provide a lower bound for the dynamic situation where the input data is not known in advance and the data structure is built up by iterated insertions. Using as performance measure the expected number of data bucket accesses needed to perform a window query, the static clustering problem turns into a classical optimization problem. For the special case of bucket capacity cb = 2 the optimization problem is solvable in polynomial time, whereas for cb 3 it is NP-hard. In experiments using simulated annealing heuristics...