An increasing amount of location-based service data is being accumulated and helps to study urban dynamics and human mobility. Location embedding generated from human mobility trajectories has become a popular topic to understand urban functionality, and could be applied as essential resources to various downstream tasks. Existing location embedding methods are mostly tailored for specific problems that are taken place within a small group of areas. Downscaling the spatial resolution makes existing approaches suffer from extensive computational cost and significant data sparsity. We propose to learn finegrained location representations through a GCN-aided skip-gram model named GCN-L2V by considering both spatial adjacency and human mobility. With a flow graph and a spatial graph, it embeds context information into vector representations. GCN-L2V is able to capture relationships among locations and provides a better notion of similarity in spatial environment. Quantitative experiments and case studies empirically demonstrate that representations learned by GCN-L2V are effective.