The use of big data and data mining in the investigation of criminal offences
Keywords:
criminal analytics, criminal justice, criminal offenses, investigation, working with data.
Abstract
The aim of this study was to determine the features and prospects of using Big Data and Data Mining in criminal proceedings. The research involved the methods of a systematic approach, descriptive analysis, systematic sampling, formal legal approach and forecasting. The object of using Big Data and Data Mining are various crimes, the common features of which are the seriousness and complexity of the investigation. The common tools of Big Data and Data Mining in crime investigation and crime forecasting as interrelated tasks were identified. The creation of databases is the result of the processing of data sources by Data Mining methods, each being distinguished by the specifics of use. The main risks of implementing Big Data and Data Mining are violations of human rights and freedoms. Improving the use of Big Data and Data Mining requires standardization of procedures with strict adherence to the fundamental ethical, organizational and procedural rules. The use of Big Data and Data Mining is a forensic innovation in the investigation of serious crimes and the creation of an evidence base for criminal justice. The prospects for widespread use of these methods involve the standardization of procedures based on ethical, organizational and procedural principles. It is appropriate to outline these procedures in framework practical recommendations, emphasizing the responsibility of officials in case of violation of the specified principles. The area of further research is the improvement of innovative technologies and legal regulation of their application.
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References
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Belesiotis, A., Papadakis, G., & Skoutas, D. (2018). Analyzing and predicting spatial crime distribution using crowdsourced and open data. ACM Transactions on Spatial Algorithms and Systems, 3(4), 1-31. https://doi.org/10.1145/3190345
Blahuta, R., & Movchan, A. (2020). The latest technologies in the investigation of crimes: The current state and problems of use. Lviv: Lviv State University of Internal Affairs.
Butt, U. M., Letchmunan, S., Hassan, F. H., Ali, M., Baqir, A., & Sherazi, H. H. R. (2020). Spatio-Temporal Crime HotSpot detection and prediction: А systematic literature review. IEEE Access, 8, 166553-166574.
Chaudhary, M., & Bansal, D. (2022). Open source intelligence extraction for terrorism-related information: A review. WIREs. Data Mining and Knowledge Discovery, Online version, e1473. https://doi.org/10.1002/widm.1473
Das, P., Das, A. K., Nayak, J., Pelusi, D., & Ding, W. (2021). Incremental classifier in crime prediction using bi-objective Particle Swarm Optimization. Information Sciences, 562, 279-303. https://doi.org/10.1016/j.ins.2021.02.002
Dehtiarovai, Y. V., & Yevdokimov, Y. (2018). Data mining methods and models for social and economic processes forecasting. Mechanism of Economic Regulation, 2, 34-44. https://doi.org/10.21272/mer.2018.80.03
Dupont, B., Stevens, Y., Westermann, H., & Joyce, M. (2018). Artificial intelligence in the context of crime and criminal justice. Montreal university [Université de Montréal]. http://dx.doi.org/10.2139/ssrn.3857367
European Parliament and the Council of the European Union. (2018). Regulation (EU) 2018/1727 of the European parliament and of the council of 14 November 2018 on the European Union Agency for Criminal Justice Cooperation (Eurojust), and replacing and repealing Council Decision 2002/187/JHA. Retrieved from https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32018R1727&from=IT
European Parliament and the Council of the European Union. (2019). Regulation (EU) 2019/817 of the European parliament and of the council of 20 May 2019 on establishing a framework for interoperability between EU information systems in the field of borders and visa and amending Regulations (EC) No 767/2008, (EU) 2016/399, (EU) 2017/2226, (EU) 2018/1240, (EU) 2018/1726 and (EU) 2018/1861 of the European Parliament and of the Council and Council Decisions 2004/512/EC and 2008/633/JHA. Retrieved from https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:32019R0817
European Union. (2018). The General Data Protection Regulation: Regulation (EU) 2016/679. Retrieved from https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32016R0679
Europol. (2022). Secure Information Exchange Network Application: Ensuring the secure exchange of sensitive and restricted information. Retrieved from https://www.europol.europa.eu/operations-services-and-innovation/services-support/information-exchange/secure-information-exchange-network-application-siena
Grechkina, O., Kornyushkina, A., Naruzhnaya, E., Tonkov, E., & Turanin, V. (2019). El lenguaje jurídico como medio de comunicación intelectual y jurídico. Revista Científica Del Amazonas, 2(3), 32-38. Recuperado a partir de https://revistadelamazonas.info/index.php/amazonas/article/view/15
Guariglia, M. (2020). Technology can’t predict crime, it can only weaponized proximity to policing. Electronic Frontier Foundation. Retrieved from https://www.eff.org/deeplinks/2020/09/technology-cant-predict-crime-it-can-only-weaponize-proximity-policing
Hajela, G., Chawla, M., & Rasool, A. (2021). A multi-dimensional crime spatial pattern analysis and prediction model based on classification. ETRI Journal, 43(2), 272-287. https://doi.org/10.4218/etrij.2019-0306
Hassani, H., Huang, X., Silva, E. S., &mGhods, M. (2016). A review of data mining applications in crime. Statistical Analysis and Data Mining, 9(3), 139-154. https://doi.org/10.1002/sam.11312
Hou, M., Hu, X., Cai, J., Han, X., & Yuan, S. (2022). An integrated graph model for spatial–temporal urban crime prediction based on attention mechanism. ISPRS International Journal of Geo-Information, 11(5), 294. https://doi.org/10.3390/ijgi11050294
Hussain, F. S., & Aljuboori, A. F. (2022). A crime data analysis of prediction based on classification approaches. Baghdad Science Journal, 5, 1073-1077. http://dx.doi.org/10.21123/bsj.2022.6310
Interpol. (n.d.) Our 19 databases. Recovered from https://www.interpol.int/How-we-work/Databases
Jha, S., Yang, E., Almagrabi, A. O., Bashir, A. K., & Joshi, G. P. (2021). Comparative analysis of time series model and machine testing systems for crime forecasting. Neural Computing and Applications, 33, 10621-0636. https://doi.org/10.1007/s00521-020-04998-1
Kadar, C., Maculan, R., & Feuerriegel, S. (2019). Public decision support for low population density areas: An imbalance-aware hyper-ensemble for spatio-temporal crime prediction. Decision Support Systems, 119, 107-117. https://doi.org/10.1016/j.dss.2019.03.001
Norouzi, N., & Ataei, E. (2021). Application of data mining in identifying and discovering hidden patterns of theft. International Journal of Innovative Research in the Humanities, 1(1), 29–42.
Oatley, G. C. (2022). Themes in data mining, big data, and crime analytics. WIREs Data Mining and Knowledge Discovery, 12(2), e1432. https://doi.org/10.1002/widm.1432
Oatley, G., Chapman, B., & Speers, J. (2020). Forensic intelligence and the analytical process. WIREs Data Mining and Knowledge Discovery, 10(3), e1354. https://doi.org/10.1002/widm.1354
Pokhriyal, N., Kumar, N., Verma, R., & Semwal, A. (2020). Survey on crime data analysis using a different approach of K-Means clustering. International Journal of Advanced Science and Technology, 29(5), 13839-13854.
Pramanik, M. I., Lau, R. Y. K., Yue, Wei T., Ye, Y., & Li, C. (2017). Big data analytics for security and criminal investigations. WIREs Data Mining and Knowledge Discovery, 7(4), e1208. https://doi.org/10.1002/widm.1208
Soni, S., Shankar, V. G., Chaurasia, C. (2019). Route-the safe: A robust model for safest route prediction using crime and accidental data. International Journal of Advanced Science and Technology, 28(16), 1415-1428.
Usha, D., Niveditha, V. R., Kirubadevi, T., & Thamizhikkavi, P. (2020). Use of predictive analytical algorithm by crime investigation team – An Analysis. International Journal of Advanced Science and Technology, 29(9s), 2986-2992.
Wang, J., Hu, J., Shen, S., Zhuang, J., & Ni, S. (2020). Crime risk analysis through big data algorithm with urban metrics. Physica A: Statistical Mechanics and its Applications, 545, 123627. https://doi.org/10.1016/j.physa.2019.123627
Zhao, X., & Tang, J. (2017). Modeling temporal-spatial correlations for crime prediction. In: E.-P. Lim, & M. Winslett (Eds.), Proceeding of the 2017 ACM on Conference on Information and Knowledge Management, (pp. 497-506). New York, NY: Association for Computing Machinery. https://doi.org/10.1145/3132847.3133024
Zhou, B., Chen, L., Zhao, S., Zhou, F., Li, S., & Pan, G. (2021). Spatio-temporal analysis of urban crime leveraging multisource crowd sensed data. Personal and Ubiquitous Computing. Retrieved from https://doi.org/10.1007/s00779-020-01456-6
Published
2022-10-18
How to Cite
Tymchyshyn, A., Semeniaka, A., Bondar, S., Akhtyrska, N., & Kostiuchenko, O. (2022). The use of big data and data mining in the investigation of criminal offences. Amazonia Investiga, 11(56), 278-290. https://doi.org/10.34069/AI/2022.56.08.27
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