Supervised Learning could be biased if we use obsolete data
One approach to using knowledge from the example you gave above is to use that information as a prior for your current model. While it is unlikely that the exact trends observed a while ago will predict future trends, some general observations/correlations will likely still be relevant for the present day. 

Concerning your example of criminal profiling. Making predictive models in such cases can be highly biased and is controversial. I invite you to read the Outlook section XVIII.D of this ML review on the social implications of machine learning and the references mentioned.