For spatial data analysis, a key insight is that observations in space cannot typically be assumed to be mutually independent and that those observations that are close to each other are likely to be similar. This sight has been applied to the modeling of spatial extreme events, which takes spatial dependence and extreme properties into account simultaneously to obtain return levels and some indices for locations with no observed data. This study's modeling is based on max-stable processes (MSP) with Schlather's characterization and aims to draw statistical inferences about spatial extreme data.