The polyhedral model is powerful for analyzing and transforming static control programs, hence its intensive use for the optimization of data locality and automatic paralleliza-tion. Affine transformations excel at modeling control flow, to promote data reuse and to expose parallelism. The approach has also successfully been applied to the optimization of memory accesses (array expansion and contraction), although the available tools in the area are not as mature. Yet data locality also depends on other parameters such as data layout and data placement relatively to the memory hierarchy; these include spatial locality in cache lines and scalability on NUMA systems. This paper presents Ivie, a parallel intermediate language which complements affine transformations implemented in state-of-the-art polyhedral compilers and supports spatial and NUMA-aware data locality optimizations. We validate the design of the intermediate language on representative benchmarks.