In the empirical analysis it was concluded, that some variables tends to spatially cluster arround historical city center. To investigate, whether this clustering is statistically significant and how strong it is, spatial autocorrelation analysis is performed in this chapter.
Lets start with brief explanation of the spatial autocorrelation concept. Autocorrelation (whether spatial or not) is a measure of similarity (correlation) between nearby observations. A spatial autocorrelation measures how distance influences a particular variable. It quantifies the degree of which objects are similar to nearby objects. Variables are said to have a positive spatial autocorrelation when similar values tend to be nearer than dissimilar values. Spatial autocorrelation in a variable can be exogenous (it is caused by another spatially autocorrelated variable, e.g. rainfall) or endogenous (it is caused by the process at play, e.g. the spread of a disease) 1. There are two types of spatial autocorrelation – global and local. If the data are globally autocorrelated, the test statistics can tell us whether values in our map cluster together (or disperse) overall, but it won’t inform us about where specific clusters (or outliers) are. Local spatial autocorrelation investigates the relationships between each observation and its surroundings, rather than providing a numerical summary of these relationships across space. Both statistical models were applied and results were analysed in dedicated chapters.