  Large-scale trends in urban crime and global terrorism are well-predicted by
socio-economic drivers, but focused, event-level predictions have had limited
success. Standard machine learning approaches are promising, but lack
interpretability, are generally interpolative, and ineffective for precise
future interventions with costly and wasteful false positives. Here, we are
introducing Granger Network inference as a new forecasting approach for
individual infractions with demonstrated performance far surpassing past
results, yet transparent enough to validate and extend social theory.
Considering the problem of predicting crime in the City of Chicago, we achieve
an average AUC of ~90\% for events predicted a week in advance within spatial
tiles approximately $1000$ ft across. Instead of pre-supposing that crimes
unfold across contiguous spaces akin to diffusive systems, we learn the local
transport rules from data. As our key insights, we uncover indications of
suburban bias -- how law-enforcement response is modulated by socio-economic
contexts with disproportionately negative impacts in the inner city -- and how
the dynamics of violent and property crimes co-evolve and constrain each other
-- lending quantitative support to controversial pro-active policing policies.
To demonstrate broad applicability to spatio-temporal phenomena, we analyze
terror attacks in the middle-east in the recent past, and achieve an AUC of
~80% for predictions made a week in advance, and within spatial tiles measuring
approximately 120 miles across. We conclude that while crime operates near an
equilibrium quickly dissipating perturbations, terrorism does not. Indeed
terrorism aims to destabilize social order, as shown by its dynamics being
susceptible to run-away increases in event rates under small perturbations.
