Spatio-temporal Prediction of Crimes Using Predictive Justice Algorithms

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Abstract

A statistical/machine learning technique known as Risk Terrain Modelling (RTM) is currently being used as a software solution to diagnose the socio-environmental conditions that lead to crime in a specific area's geography (the Study Area) by geospatially and temporally analysing crimes by linking them to hotspots and analysing them into big data (criminal data). As a result, new predicting patterns of risks in the geographical area under survey (the Study Area) appear in the future, all for the sake of a prompt and efficient response by the Predictive Police. Prioritising the use of precautionary resources is necessary in two ways: first, to prevent crime and lessen potential dangers in the event that one does occur; second, to determine what must be done as soon as possible as a preventive measure to control the crime with the least amount of harm to the police forces. Leslie W. Kennedy and Joel M. Caplan founded the Risk Terrain Modelling at Rutgers University, and it has been systematically investing in the field of criminal investigation for over ten years. Currently, the model is being tested in more than 45 nations worldwide.

Author

• Fahil Abdulkareem, Duhok Polytechnic University