  We present a data-driven machine-learning approach for modeling space-time
socioeconomic dynamics. Through coarse-graining fine-scale observations, our
modeling framework simplifies these complex systems to a set of tractable
mechanistic relationships -- in the form of ordinary differential equations --
while preserving critical system behaviors. This approach allows for expedited
'what if' studies and sensitivity analyses, essential for informed
policy-making. Our findings, from a case study of Baltimore, MD, indicate that
this machine learning-augmented coarse-grained model serves as a powerful
instrument for deciphering the complex interactions between social factors,
geography, and exogenous stressors, offering a valuable asset for system
forecasting and resilience planning.
