Abstract
L-1-norm approach is used to construct the local linear estimator of the spatial regression quantile for functional regressors. Under mixing spatial condition, we establish the almost complete convergence of the constructed approach. The applicability of the constructed estimator is examined by a Monte-Carlo study. The finite sample performance of the proposed estimator is compared to the classical kernel estimator of the functional spatial quantile regression. The result indicates that our new approach is more accurate than the classical one.