Machine Learning helps to predict new violence hot spots

I think that this is an important point, and regret not discussing this more in the paper.

Predictive policing can absolutely be bad, and can be especially problematic when the algorithms are designed (or used) to target individuals or groups of people for investigations.

They can be racist, biased, and are open to misinterpretation by those who end up using them. They can also lack transparency, and are often trained on skewed datasets that can result in self-perpetuation of reported crime patterns.

I don't mean to suggest that this work is entirely without bias. However, in this particular paper we describe a method that uses point of interest data exclusively as predictor of violent crime levels. We don't consider income, education, age, race or any other socio-economic factors in the model. We simply determine the linear combination of different point of interest densities which most closely reproduces the crime density map for a city -- i.e. we don't weight one venue more strongly than another because of its location.

Police departments and governments have a lot to do when it comes ensuring fair, equal policing. Academics/researchers need to be mindful that the work and analyses they do in a lab can have significant impacts on people's lives. However, we tried to be sympathetic to these issues.

The paper mentions interventions around alcohol-serving venues -- these are things like staggered closing times for pubs (avoiding everyone in a city being kicked out from their pub at the same time), restrictions around using glass bottles/glasses, and security requirements at certain times of the day. These interventions have been shown to work in pubs/clubs, and have measurably reduced violence related hospital admissions. Our hope was that this analysis might encourage councils to consider spreading such interventions/policies to other locations, beyond those that serve alcohol, as doing so may have similarly beneficial effects.