Exploratory data analysis (EDA) tries to summarize datasets main characteristics such as nearest neighborhood indexes, standard deviation, scatterplots or quadrat analysis. This EDA chapter is divided into several sections to cover myGeoffice© options not forgetting the graphical mode when facing outputs: file data input (after all, any analysis demands data); Descriptive study of the variable (mean, kurtosis, distribution plot, etc.); 2D-3D data posting (spatial location of the data samples); Cutoff layout map (a spatial colorful plot according to the data samples values that are higher and lower against any particular threshold); G and Kipley's K Index (to disclose clustered, uniform and random space sampling); Kernel Gaussian density (a non-parametric way to estimate the probability space density function of a variable); T-Student and F-tests (a parametric approach to check statistical differences between two sub-regions), including a brief section regarding the two-way ANOVA technique; Quadrat analysis (comparison of the statistically expected and actual counts of objects within spatial sampling areas to test randomness and clustering); XX profile scatterplot (silhouette view of the data along XX axis); and YY profile scatterplot (silhouette view of the data along YY axis).
Parallel isosurface extraction for 3D data analysis workflows in distributed environments