Skyline queries have gained attention lately for supporting effective retrieval over massive spatial data. While efficient algorithms have been studied for spatial skyline queries using Euclidean distance, or, L 2 norm, these algorithms are (1) still quite computationally intensive and (2) unaware of the road constraints. Our goal is to develop a more efficient algorithm for L 1 norm, also known as Manhattan distance, which closely reflects road network distance for metro areas with well-connected road networks. Towards this goal, we present a simple and efficient algorithm which, given a set P of data points and a set Q of query points in the plane, returns the set of spatial skyline points in just O(|P|log|P|) time, assuming that |Q|≤|P|. This is significantly lower in complexity than the best known method. In addition to efficiency and applicability, our proposed algorithm has another desirable property of independent computation and extensibility to L ∞ norm, which naturally invites parallelism and widens applicability. Our extensive empirical results suggest that our algorithm outperforms the state-of-the-art approaches by orders of magnitude.