The Polish Police is a centralised, armed and uniformed organisation. Nearly 100,000 police officers supported by almost 25,000 civilian workers are tasked with watching over the safety of Poland’s inhabitants and the maintenance of public order. The contemporary Polish Police consists of officers employed in the criminal, preventive and support services of the police in the organisational, logistic and technical areas.
Well-armed and trained anti-terrorist police are involved in detaining the most-dangerous criminals. Police officers of the Central Bureau of Investigation (CBŚP) unit break up organised crime groups, and fight against criminal terror and drug trafficking.
Assumptions: The MIM Solutions solution is based on assumptions similar to the successfully used PredPol system. The PredPol system was the first crime prediction system that achieved significant success in crime prevention. It operates in 11 major cities in the US and the UK. The main advantage of this system is the very small amount of data needed to make forecasts and thus the speed and flexibility of implementation in various centres. The basic version of the system uses solely information on the place, time and type of event. The aim of the MIM Solutions system is to support police officers in suggesting the place and time of possible future events. The system extracts various crime patterns from historical data (e.g. on Fridays in the evening there are more robberies in this district, on working days break-ins happen most often in this area, etc.) that a human may not notice.
Data used to create the model: The predictive system was based on police data from the decision support system (DSS, in Polish: SWD), which includes the place, time and type of event. The data was processed to obtain the exact coordinates of the location based on the textual description.
Creating of the system: The MIM Solutions model uses a spatial-temporal regression model based on gradient boosted decision trees (GBDT). It predicts the probability of another event based on previously observed events in the area. It allows the simple integration of external data sources such as weather and cultural and sports events. It supports predictions for any period of time, e.g. hour, day, or month. When preparing a crime forecast for a given region (e.g. city, district, county), we divide the region into small square areas called locations. The default and recommended square size is 200 by 200 metres. The development of the right features for the algorithm to use was the key challenge. We made sure that the features capture spatial-temporal information about past crimes and allow robust predictions for the forecast.