Safera uses an initial data set that typically incorporates crime prediction factors – such as time period, weather, traffic events, unemployment rate, or crime type – in order to construct an initial prediction model. If any of the fields change the value, then the model is re-trained. Some fields change infrequently while others change daily (such as weather and traffic events). The model is continuously updated/ upgraded with new data. The system periodically pulls in the latest fields (automatically) from appropriate sources. Then the model runs against the new data to predict what kind of crime is likely to occur in each precinct.
There are many ways in which law enforcement agencies can use Safera.
The system predicts the probability of crime happening at particular locations based on historical data, census data, any new traffic, or weather events. Departments can also run the system once a day, and based on the predictions, decide how to deploy resources in each precinct.
For example, thanks to instantaneous information on-site and according to the type of need, Safera can accelerate deployment of police, firefighting, and emergency services