A proposed Bi-layer crime prevention framework using big data analytics

Abstract

The future of science and technology sounds very promising. The need to adopt new technologies while navigating towards industry 4.0 has changed the perceptions of law enforcement agency to contend against criminal minds. It is sad but true that the conventional crime prevention system followed by government agencies is not effective for long-term implications. With advanced technologies that continuously generate and exchange data, big data analytics can be applied to predict and prevent crime from happening. However, dealing with the overwhelming amount of complex and heterogeneous crime-related data is never an easy task. There are many data analysis techniques, and each of them has its strengths and weaknesses. In order to identify the most efficient techniques, recent literature is reviewed to spotlight the trend as well as to shed light on the research gaps and challenges in various areas. The areas include crime data collection and preprocessing, crime data analysis, crime prediction and crime prevention. These techniques are further analyzed by considering the advantages and disadvantages, which then provides insight to propose a bi-layer crime prevention framework. The first layer intends to support the law enforcement agency's daily operation while the second layer serves as a countermeasure for the first layer. Both layers aim to reduce the crime rate by involving law enforcement agency through the utilization of various significant data sources and techniques effectively. The proposed crime prevention framework will progressively collect data to deter criminal behavior for the city's environmental design. Ultimately, a safe and secure city is molded shortly.