This chapter deals with

• The notion of exploratory spatial data analysis

• The presentation of descriptive statistics

• Spatial statistics and their importance in analyzing spatial data

• Analyzing univariate data

• Simple exploratory spatial data analysis tools such as histograms, boxplots and other visual methods to get a better insight of spatial datasets

• Bivariate analysis

• Correlation and pairwise correlation

• Normalization, rescaling and adjustments

• An introduction of basic notions of statistical significant tests

• The importance of hypothesis setting in a spatial context

• The importance of normal distribution in classic statistics and how it is integrated into spatial analysis

After a thorough study of the theory and lab sections, you will be able to

• Have a solid knowledge of descriptive statistics

• Use descriptive statistics for univariate analysis

• Understand and use exploratory spatial data analysis techniques to map and analyze variables attached to spatial objects

• Create several plots, link them to maps and identify interesting patterns of data

• Conduct bivariate analysis, identify whether two variables are linearly related and use plots to further examine their relation

• Rescale data to make comparisons between variables easier and also allow for better data handling

• Apply ESDA tools through ArcGIS and GeoDa