Advances in Economics, Management and Political Sciences
- The Open Access Proceedings Series for Conferences
Proceedings of the 2nd International Conference on Business and Policy Studies
Series Vol. 17 , 13 September 2023
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Access | Article
Whether a Criminal is Likely to Re-offend? A Statistical Analysis Using Crimes Data from Broward County Florida
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Abstract
Security issues have always been a significant threat to the safety of citizens in every country, and many of these re-arrested criminals have negatively impacted social security. Therefore, predicting and studying the factors of a criminal's re-entry to prison will significantly help maintain social order and improve the civil society happiness index. This study, it will show what elements are predicted to influence a criminal's return to prison and what aspects will have a higher proportion and weight based on the collected data set. In the dataset, each re-admission inmate is categorized according to gender, age range, race, records, etc. Use the Logistics model and OLS model to build a model to predict what factors most directly lead to a criminal being arrested and imprisoned again. Data research has proved that the "number of priors" is the factor that most affects the recidivism rate of criminals.
Keywords
statistics, prediction of recidivism rate, ordinary least-squared model, logistic regression model, social inequality
References
1. Bruce, Fredrick. (1999). Factors contributing to recidivism among youth placed with the New York State Division for Youth.https://www.criminaljustice.ny.gov/crimnet/ojsa/dfy/dfy_research_report.pdf
2. Dressel, J., & Farid, H. (2021). The Dangers of Risk Prediction in the Criminal Justice System. MIT Case Studies in Social and Ethical Responsibilities of Computing. https://doi.org/10.21428/2c646de5.f5896f9f
3. Roberts, J. V. (1997). The role of criminal record in the sentencing process. Crime and Justice, 22, 303-362.
4. Latessa, E. J., Johnson, S. L., & Koetzle, D. (2020). What works (and doesn’t) in reducing recidivism. Routledge.
5. “Sigmoi.” Sigmoi__Sumor, https://blog.csdn.net/su_mo/article/details/79281623.
6. Oscar Torres-Reyna. (2007). Linear Regression using Stata https://www.princeton.edu/~otorres/Regression-101.pdf
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8. Maltz, M. D. (1984). Recidivism. Michael Maltz.
9. Dressel, J., & Farid, H. (2018). The accuracy, fairness, and limits of predicting recidivism. Science advances, 4(1), eaao5580.
10. AmiyaRanjanRout. (2022). Advantages and Disadvantages of Logistic Regression. https://www.geeksforgee-ks.org/advantages-and-disadvantages-of-logistic-regression/
Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2nd International Conference on Business and Policy Studies
- ISBN (Print)
- 978-1-915371-77-5
- ISBN (Online)
- 978-1-915371-78-2
- Published Date
- 13 September 2023
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/17/20231066
- Copyright
- 13 September 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited