Vector geometries are traditionally represented as entities of sequences of coordinate structures. With advancements in hardware and software for data analytics, a preference for columnar data layouts arose. This paper examines array programming for spatial operations to evaluate the potential performance benefits of modern computing architectures and emerging spatial data encodings. Evaluating selected operations, such as distance calculation, extent extraction, and affine transformations, indicates similar or improved performance for geometries with columnar coordinate layouts. By leveraging modern compiler infrastructure, we further demonstrate that advanced hardware features in commodity hardware, such as vector instructions, are becoming accessible without specialized code. The performance comparison with established, widely used geospatial and computational libraries reveals significant untapped potential for increasing the efficiency of spatial computing.