  For centuries quality of life was a subject of studies across different
disciplines. However, only with the emergence of a digital era, it became
possible to investigate this topic on a larger scale. Over time it became clear
that quality of life not only depends on one, but on three relatively different
parameters: social, economic and well-being measures. In this study we focus
only on the first two, since the last one is often very subjective and
consequently hard to measure. Using a complete set of bank card transactions
recorded by Banco Bilbao Vizcaya Argentaria (BBVA) during 2011 in Spain, we
first create a feature space by defining various meaningful characteristics of
a particular area performance through activity of its businesses, residents and
visitors. We then evaluate those quantities by considering available official
statistics for Spanish provinces (e.g., housing prices, unemployment rate, life
expectancy) and investigate whether they can be predicted based on our feature
space. For the purpose of prediction, our study proposes a supervised machine
learning approach. Our finding is that there is a clear correlation between
individual spending behavior and official socioeconomic indexes denoting
quality of life. Moreover, we believe that this modus operandi is useful to
understand, predict and analyze the impact of human activity on the wellness of
our society on scales for which there is no consistent official statistics
available (e.g., cities and towns, districts or smaller neighborhoods).
