The point is, I wonder if the machine learning approach used here is overly complex. After all, the set of environmental factors affecting crime is so well understood and thoroughly researched that you could focus on detecting tried-and-true things such as sidewalks. This would entail applying a clear set of rules instead of using the relatively unsupervised approach with training data. To be fair, ML is a complicated subject and I'm not an expert; maybe their approach draws heavily on these things.
EDIT: I understand that perception, rather than the actual crime rate, is the focus of this research. Still, there seems to be a tight correlation between the features that are known to be dangerous and those that appear sketchy. The major ones - an absence of lights, few walkways, etc. - are obvious to most pedestrians.