Exploratory Data Analysis

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).

Event Analysis

The goal of event analysis is to understand (and perhaps to predict) spatial occurrences that happened in a particular time-frame. Thus, the following five aspects will be covered in this chapter: Binomial and geometric for two outcome type of events (any phenomenon that can be observed such as rain or not rain), Knox and Mantel indicators, moving averages, point patterns, and Poisson distribution.

Education

Given the present plug-in society of on-line services, today's youngsters became owners of digital handheld devices where Wikipedia, Twitter, Facebook, Google, Yahoo, Flickr, Viadeo, Amazon, Alibaba, WeChat, Line, Blogger, What'sApp, Instagram, Vine and Dropbox are regular daily services used as a common practice. Connectivity is oxygen nowadays. People spend hours engaging with social media, highlighting that this activity is playing a huge part of the growth and evolution of the online landscape (Kemp, 2014). Thirty years after the dawn of the Net, social media have become the first activity on the Web where everything is connected in real-time and are more personalized than ever in a universal cross-platform (the cloud concept). Definitely, real social life, based on activities such as going to a movie with your friends or children playing with toys is fading away.

Geography

The primary goal of myGeoffice© is to empower Internet users with some geographical quantitative power in a direct and goal oriented way (particularly high-school and university students whose curriculum covers spatial topics). With the spread of smartphones, apps, laptops, tablets, e-learning, m-learning, 4G wireless connectivity, free wi-fi hot spots, apps, Web 2.0 tools and adaptive learning/progress tracking, it is hoped that myGeoffice© can be associated with a teaching strategy and incorporate these trends. This would assist with the basic understanding of spatial inferential and mathematical methods in the classroom and encourage geography as a career path.

Deterministic Interpolation

The review of deterministic interpolation approaches of myGeoffice© is the main goal of this chapter. The first two sections focus on first and second order polynomial while sections three and four present multiquadratic techniques and their inverse. Inverse distance weight (IDW) and moving average interpolators are addressed in sections five and six. Nearest neighbor and triangulated irregular network (Voronoy and Delaunay) conclude this chapter.

Help

An overview of the aids given by myGeoffice© is described in this chapter. As a rule of thumb, the philosophy of myGeoffice© towards help consists of a set of definitions, notes, links, examples and suggestions to help the regular researcher fulfill his/her duty on each function.

Sequential Simulation

In this chapter, the first main section analyzed the Conditional Cumulative Gaussian Simulation (CCGS) of myGeoffice©. Section 2 examines the Conditional Indicator Simulation (CIS).

Stochastic Interpolation

Spatial analysis includes an expanding array of methods which address different spatial issues, ranging from remote sensing to spatial error uncertainty. Each of these methods focuses on geographically raw data correlated by statistical methods. In general, spatial interpolation and stochastic Kriging, in particular, will be addressed in this chapter. Ordinary Kriging (OK) foundations are presented in the first section which encompasses eight sub-sections (in accordance with the eight myGeoffice© options). Section two introduces Kriging with Trend (KT but sometimes known as Universal Kriging) including five sub-sections: Geocomputation of KT, estimation mapping, the cross-validation procedure, validation using an extra dataset and KT versus OK comparison. Finally, Indicator Kriging (IK) is explored in section three together with nine sub-sections: First and second cutoff definition, first and second probabilistic interpolation maps, construction of the conditional cumulative distribution function, entropy of Shannon, E-type spatial estimation (including misclassification risks and economic classification), morphologic geostatistics and probabilistic interval mapping.

Spatial Autocorrelation

This chapter respects the six procedures of spatial autocorrelation covered by myGeoffice©, including variogram setup and fitness, Moran I correlograms, an innovative version of the conventional Moran scatterplot and the recent Moran variance scatterplot.

(A)Spatial Regression

The global spatial lag model for stationary processes (Y=?WY+Xß+e, where W represents the neighborhood matrix while ? is the spatial autoregressive coefficient) and the spatial regimes (individual regression for each sub-region), developed within SpaceStat®, are two possibilities. An extra option is the local continuous variation framework computed by the Geographically Weighted Regression (GWR). The idea here is to adjust a regression model (the ßs of the regression model are not the same everywhere) by weighting the neighborhood observations. In this way, the estimation computation will reflect automatic adjustments, according to the distance and values of the available samples. Section “Geographically Weighted Regression” introduces this matter while the next two sections review the conventional bivariate and multivariate OLS regression, respectively. As an extension of the OLS family, non-linear relationships, regression with dummy variables, path analysis and logistic regression are briefly explained under the following section three although myGeoffice© does not cover these practices. Spearman Rank Correlation for ordinal data is presented in the last section.