  We describe a (nonparametric) prediction algorithm for spatial data, based on
a canonical factorization of the spectral density function. We provide
theoretical results showing that the predictor has desirable asymptotic
properties. Finite sample performance is assessed in a Monte Carlo study that
also compares our algorithm to a rival nonparametric method based on the
infinite AR representation of the dynamics of the data. Finally, we apply our
methodology to predict house prices in Los Angeles.
