In this paper the indicator approach in spatial data analysis is presented for the determination of probability distributions to characterize the uncertainty about any unknown value. Such an analysis is non-parametric and is done independently of the estimate retained. These distributions are given through a series of quantile estimates and are not related to any particular prior model or shape. Moreover, determination of these distributions accounts for the data configuration and data values. An application is discussed. Moreover, some properties related to the Gaussian model are presented.