Testing local versions of correlation coefficients

The aim of this paper is to define and test local versions of standard correlation coefficients in statistical analysis. This research is motivated by the increasing number of applications using local versions of exploratory and explanatory spatial data analysis methods. A common example of the latter is local regression. Methods such as the Geographically Weighted Regression argue that it is necessary to check spatial non-stationarity in the relationships between a geographic phenomenon and its determinants. This is because the response to a stimulus could vary across space. For example the relationship between education level and unemployment could vary across the EU regions. Local regression claims to account for local relationships that may be hidden or missed when a global regression is applied. However, the statistical inference in local regression methods is still an open field for basic research. In this paper a local version of Pearson correlation coefficient is defined and tested in spatial data. By doing this a simple tool for statistical inference is provided assisting a more careful interpretation of the results of a local regression model. Furthermore, this could be a technique for testing the existence of local correlation among two variables representing spatial data in the absence of a global correlation and vice versa. The application of this technique includes pairs of usually correlated variables, such as income and high levels of education as well as not correlated variables.