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Large-scale trends in urban crime and global terrorism are well-predicted bysocio-economic drivers, but focused, event-level predictions have had limitedsuccess. Standard machine learning approaches are promising, but lackinterpretability, are generally interpolative, and ineffective for precisefuture interventions with costly and wasteful false positives. Here, we areintroducing Granger Network inference as a new forecasting approach forindividual infractions with demonstrated performance far surpassing pastresults, yet transparent enough to validate and extend social theory.Considering the problem of predicting crime in the City of Chicago, we achievean average AUC of ~90\% for events predicted a week in advance within spatialtiles approximately $1000$ ft across. Instead of pre-supposing that crimesunfold across contiguous spaces akin to diffusive systems, we learn the localtransport rules from data. As our key insights, we uncover indications ofsuburban bias -- how law-enforcement response is modulated by socio-economiccontexts with disproportionately negative impacts in the inner city -- and howthe 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 analyzeterror 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 measuringapproximately 120 miles across. We conclude that while crime operates near anequilibrium quickly dissipating perturbations, terrorism does not. Indeedterrorism aims to destabilize social order, as shown by its dynamics beingsusceptible to run-away increases in event rates under small perturbations.

Long-range Event-level Prediction and Response Simulation for Urban Crime and Global Terrorism with Granger Networks