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Nowadays, 23% of the world population lives in multi-million cities. In thesemetropolises, criminal activity is much higher and violent than in either smallcities or rural areas. Thus, understanding what factors influence urban crimein big cities is a pressing need. Mainstream studies analyse crime recordsthrough historical panel data or analysis of historical patterns combined withecological factor and exploratory mapping. More recently, machine learningmethods have provided informed crime prediction over time. However, previousstudies have focused on a single city at a time, considering only a limitednumber of factors (such as socio-economical characteristics) and often at largespatial units. Hence, our understanding of the factors influencing crime acrosscultures and cities is very limited. Here we propose a Bayesian model toexplore how crime is related not only to socio-economic factors but also to thebuilt environmental (e.g. land use) and mobility characteristics ofneighbourhoods. To that end, we integrate multiple open data sources withmobile phone traces and compare how the different factors correlate with crimein diverse cities, namely Boston, Bogot\'a, Los Angeles and Chicago. We findthat the combined use of socio-economic conditions, mobility information andphysical characteristics of the neighbourhood effectively explain the emergenceof crime, and improve the performance of the traditional approaches. However,we show that the socio-ecological factors of neighbourhoods relate to crimevery differently from one city to another. Thus there is clearly no "one fitsall" model.

Socio-economic, built environment, and mobility conditions associated with crime: A study of multiple cities