Factor analysis is a statistical technique for modeling multivariate data as a function of a small number of underlying factors. In standard factor analysis, this dimension reduction is performed without respect to any dependencies in the data. When the multivariate response is a spatial surface this assumption is no longer appropriate, and an adaptation is required. We introduce a Bayesian non-parametric spatial factor analysis model with spatial dependency induced through a prior on the factor loadings matrix. Spatial dependency is encoded though a Probit stick-breaking process with a multiplicative gamma process shrinkage prior, used to determine the number of latent factors. Through simulation, we show that our new method has superior prediction capability in settings of spatial dependency over standard factor analysis models, and furthermore is capable of producing a lower dimensional spatial surface through clustering. Finally, we apply the method to malaria surveillance and environmental data collected weekly from 51 districts across the Peruvian Amazon from 2000-2018.