  Measuring socioeconomic deprivation of cities in an accurate and timely
fashion has become a priority for governments around the world, as the massive
urbanization process we are witnessing is causing high levels of inequalities
which require intervention. Traditionally, deprivation indexes have been
derived from census data, which is however very expensive to obtain, and thus
acquired only every few years. Alternative computational methods have been
proposed in recent years to automatically extract proxies of deprivation at a
fine spatio-temporal level of granularity; however, they usually require access
to datasets (e.g., call details records) that are not publicly available to
governments and agencies.
  To remedy this, we propose a new method to automatically mine deprivation at
a fine level of spatio-temporal granularity that only requires access to freely
available user-generated content. More precisely, the method needs access to
datasets describing what urban elements are present in the physical
environment; examples of such datasets are Foursquare and OpenStreetMap. Using
these datasets, we quantitatively describe neighborhoods by means of a metric,
called {\em Offering Advantage}, that reflects which urban elements are
distinctive features of each neighborhood. We then use that metric to {\em (i)}
build accurate classifiers of urban deprivation and {\em (ii)} interpret the
outcomes through thematic analysis. We apply the method to three UK urban areas
of different scale and elaborate on the results in terms of precision and
recall.
