Time series analysis for crime forecasting

Technological development in every aspect of human life has formed a wider analytical approach to crime. The genesis and structure of crime, its intensity, and dynamics are subjects of intense scientific studies carried out by researchers in various fields of science. Increasing possibilities to track crime events give public organizations and police departments the opportunity to collect and store detailed data, including spatial and temporal information. At the same time, the crowd-sourced open datasets as social media and Internet datasets can be a valuable source of knowledge about various behavior patterns and social phenomena, including those of criminal nature. Thus, exploratory analysis and data mining become an important part of the current methodology for the detection and forecasting of crime development. The ability to use data analysis tools to extract useful information related to criminal events enables law enforcement agencies to more efficiently allocate their resources to specific crime areas. It allows the effective deployment of officers to high-risk crime areas and elimination from areas with a decreasing crime trend as well as developing effective crime prevention strategies. The purpose of this paper is to show the usefulness of analytic algorithms in predicting crimes, however, there are other applications of such analyzes in the area of law enforcement, such as defining criminal hot spots, creating criminal profiles, and detecting crime trends. The most important factor is the accuracy with which one can infer and create new knowledge based on observations from the past that will be useful in the process of reducing the number of crimes (predictive policing) and ensure the security of citizens.

I. Introduction

Technological development in every aspect of human life has formed a wider analytical approach to crime. The genesis and structure of crime, its intensity, and dynamics are subjects of intense scientific studies carried out by researchers in various fields of science [4], [5], [6], [7], [8], [10], [12], [13], [14], [17], [18], [24], [25], [26], [29], [31], [32], [35], [36], [40].