A Prediction Model of Recidivism of Specific Populations Based on Big Data

Abstract

This research is aimed at establishing and improving the relevant information database of “released prisoners,” integrating the behavior data of specific populations, constructing the early-warning model of recidivism, evaluating the database file information by using prediction analysis technology, and making a prediction alarm to the people who are most likely to commit the crime again on the basis of big data analysis so as to ultimately achieve the goal of reducing the recidivism rate. The research used the data exchange technology of the heterogeneous database to complete data collection and database establishment, used the feature engineering technology to analyze the big data of specific populations, obtained the multidimensional behavior trajectory data, and carried out sorting and statistics. On this basis, the linear regression algorithm was applied to make the prediction and evaluation, and the visual results were presented to assist in researching and judging the possibility of recidivism of specific personnel. Through programming realization and simulation experiments, the research obtained the tendency prediction of the people to commit crimes again by statistical analysis from multidimensional data with a long time span. In the next step, the real test will be carried out to help the public security work in China and contribute to the maintenance of national and social stability.