Outliers present in the dataset is harmful to the information quality contained in the map and may lead to wrong interpretations, even if the number of outliers to the total data collected is small. Thus, before any analysis, it is extremely important to remove these errors. This work proposes a sequential process model capable of identifying outlier data when compared their neighbors using statistical parameters. First, limits are determined based on the median range of the values of all the points contained in the dataset. Second, the neighbors are located within the range of the point under analysis. In the anisotropic process, neighbors are defined in a single direction, and then the calculation of median is with the values of the neighboring points located within the radius range next to the point under analysis. Finally, an isotropic process is conducted, where the neighbors are defined and located within the radius range, and the median value is identified. Outliers are data that deviate above or below a given percentage of a set median value. Statistical and geostatistical analysis of the data before and after this process was performed, indicating it was effective in eliminating outliers in the spatial datasets evaluated. The median limits eliminated most of the points with discrepant values from the processed datasets. The anisotropic and isotropic processes eliminated outliers in relation to their neighbors at small distances, reducing the previous nugget values and improving the characterization of the spatial dependence of the datasets.