  Police departments around the world have been experimenting with forms of
place-based data-driven proactive policing for over two decades. Modern
incarnations of such systems are commonly known as hot spot predictive
policing. These systems predict where future crime is likely to concentrate
such that police can allocate patrols to these areas and deter crime before it
occurs. Previous research on fairness in predictive policing has concentrated
on the feedback loops which occur when models are trained on discovered crime
data, but has limited implications for models trained on victim crime reporting
data. We demonstrate how differential victim crime reporting rates across
geographical areas can lead to outcome disparities in common crime hot spot
prediction models. Our analysis is based on a simulation patterned after
district-level victimization and crime reporting survey data for Bogot\'a,
Colombia. Our results suggest that differential crime reporting rates can lead
to a displacement of predicted hotspots from high crime but low reporting areas
to high or medium crime and high reporting areas. This may lead to
misallocations both in the form of over-policing and under-policing.
