Data collected by space probes and Earth-based observations allow to
investigate multiple aspects of planetary bodies in the Solar System.
Typical spatial data utilized for geological and geophysical global
scale investigations include imagery basemaps, digital elevation models,
feature density maps, geochemical, magnetic and gravity anomaly maps.
Different datasets often present local correlations, and their
simultaneous use can help unveil the wide diversity of geological and
geophysical processes that contribute to the planetary evolution. A
geostatistical approach can be applied to quantify the level of
correlation and anticorrelation between the different datasets. We
developed a method based on a linear regression analysis of multiple
planetary data, producing georeferenced maps that can be imported into a
Geographic Information System (GIS) environment. We show how this
geostatistical method can be used to quantify the level of correlation
between different spatial data, and to investigate the geological and
geophysical processes of planetary bodies. We present the results of the
geostatistical method for the Mercury science case, using datasets
returned by the MESSENGER mission.