This paper shows that dividing data spatially over machines, computing local Gaussian process posteriors, and aggregating them retains optimal recovery rates and adapts to the smoothness of the true function. It proposes a Matérn process and integrated Brownian motion prior, proves contraction rates, and introduces an improved aggregation method.