From Exploratory Data Analysis to Exploratory Spatial Data Analysis

The Exploratory Data Analysis raised by Tuckey [19] has been used in multiple research in many areas but, especially, in the area of the social sciences. This technique searches behavioral patterns of the variables of the study, establishing a hypothesis with the least possible structure. However, in recent times, the inclusion of the spatial perspective in this type of analysis has been revealed as essential because, in many analyses, the observations are spatially autocorrelated and/or they present spatial heterogeneity. The presence of these spatial effects makes necessary to include spatial statistics and spatial tools in the Exploratory Data Analysis. Exploratory Spatial Data Analysis includes a set of techniques that describe and visualize those spatial effects: spatial dependence and spatial heterogeneity. It describes and visualizes spatial distributions, identifies outliers, finds distribution patterns, clusters and hot spots and suggests spatial regimes or other forms of spatial heterogeneity and, it is being increasingly used. With the objective of reviewing the last applications of this technique, this paper, firstly, shows the tools used in Exploratory Spatial Data Analysis and, secondly, reviews the latest Exploratory Spatial Data Analysis applications focused on different areas in the social sciences particularly. As conclusion, it should be noted the growing interest in the use of this spatial technique to analyze different aspects of the social sciences including the spatial dimension.