Spatial networks describe relations among agents that live in a metric space and whose locations affect the probability of connections. Recently, nonpara- metric Bayesian statistics (BNP) proved itself to be a powerful tool to provide random graph models that mimic real world networks, but no proposals have been made so far to include spatial covariates. I will show how some available models fail in recovering spatial information and conjecture a way to solve the problem.