Deep Learning for Public Safety – H2O blog

We've seen some incredible applications of Deep Learning with respect to image recognition and machine translation but this particular use case has to do with public safety; in particular, how Deep Learning can be used to fight crime in the forward-thinking cities of San Francisco and Chicago. The cool thing about these two cities (and many others!) is that they are both open data cities, which means anybody can access city data ranging from transportation information to building maintenance records. So, if you are a data scientist or thinking about becoming a data scientist, there are publicly available city-specific datasets you can play with. For this example, we looked at the historical crime data from both Chicago and San Francisco and joined this data with other external data, such as weather and socioeconomic factors, using Spark's SQL context. We do the data import, ad-hoc data munging (parsing the date column, for example), and joining of tables by leveraging the power of Spark and then publish the Spark RDD as an H2O Frame (Figure 1).