Spatial and statistical GIS Applications for geological and environmental courses

Building student's career through undergraduate and graduate courses integrated with modern statistical and GIS software foster a competitive curriculum for their future employment. We present examples that may be introduced in geological courses (e.g. mineralogy, geomorphology, geochronology, structural geology, tectonics, stratigraphy) and environmental courses (natural hazards, hydrology, atmospheric science). Univariate and multivariate statistical models can be used for the interpretation and mapping of the geological and environmental problems. Some of the main statistical univariate models such as the normal distribution as well as the multivariate methods such as the principal component analysis, cluster analysis and factor analysis are the basic methods for understanding the variables of the environmental and geological problems. Examples are presented describing the basic steps for the solution of the problems. Some of the geological problems in different scales are the interpretation of 3D structural data, identification of suitable outcrops for mapping shear sense kinematic indicators. categorical or cluster analysis on lineations depending on their origin, topology of mineral assemblages and spatial distribution of their c-axis, distinguishing paleo-elevations using cluster analysis in geomorphological structures using LiDAR intensity and elevation data for determination of meander evolution patterns and prediction of vulnerable sites for flooding or landsliding. Other applications in atmospheric and hydrology science are the prediction of ground level ozone and the decomposition of water use time series. Those fundamental statistical and spatial concepts may be used in the field or in the lab. In the lab, modern computers and friendly interface user software allow students to process data using advanced statistical methods and GIS techniques. Modern applications in tablets or smart phones may complement field work. Teaching those methods can facilitate advanced mapping, optimize sample collection distribution, field decisions, and later lab data processing.