  Nowadays, 23% of the world population lives in multi-million cities. In these
metropolises, criminal activity is much higher and violent than in either small
cities or rural areas. Thus, understanding what factors influence urban crime
in big cities is a pressing need. Mainstream studies analyse crime records
through historical panel data or analysis of historical patterns combined with
ecological factor and exploratory mapping. More recently, machine learning
methods have provided informed crime prediction over time. However, previous
studies have focused on a single city at a time, considering only a limited
number of factors (such as socio-economical characteristics) and often at large
spatial units. Hence, our understanding of the factors influencing crime across
cultures and cities is very limited. Here we propose a Bayesian model to
explore how crime is related not only to socio-economic factors but also to the
built environmental (e.g. land use) and mobility characteristics of
neighbourhoods. To that end, we integrate multiple open data sources with
mobile phone traces and compare how the different factors correlate with crime
in diverse cities, namely Boston, Bogot\'a, Los Angeles and Chicago. We find
that the combined use of socio-economic conditions, mobility information and
physical characteristics of the neighbourhood effectively explain the emergence
of crime, and improve the performance of the traditional approaches. However,
we show that the socio-ecological factors of neighbourhoods relate to crime
very differently from one city to another. Thus there is clearly no "one fits
all" model.
