[hawkharris]

It's actually well understood which landscape features contribute to or prevent crime. Things like sidewalks and bright street lamps have a positive effect (no surprise there). Bushes, certain fences and other objects that obstruct the view from the road can have a negative effect. As a reporter I once did a ride-along with a police sergeant who took it upon himself to cut a set of hedges (with the property owner's permission, of course) that helped facilitate drug deals and prostitution.

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.

[jamesbritt]

Things like sidewalks and bright street lamps have a positive effect (no surprise there).

There may be some surprise there.

The correlation between street lighting and safety is not obvious.

"In 2008, PG&E Corp., the San Francisco-based energy company, reviewed the research and found 'either that there is no link between lighting and crime, or that any link is too subtle or complex to have been evident in the data.'"

http://www.bloombergview.com/articles/2013-02-24/turn-down-t...

http://www.popcenter.org/responses/street_lighting/2