The document discusses predicting crime rates and optimizing police force allocation in Chicago. It describes how in 2009 Chicago had high homicide rates, so police turned to technology funding to test experimental methods. Now Chicago police fully embrace algorithms to inform decisions, like predicting crime hotspots. The document then outlines a study analyzing narcotics crime data from 2001-2015 to define a Bayesian model, fit parameters using MCMC and optimization, and use clustering algorithms to identify optimal police station locations and force allocation to minimize average distance to crimes.