Hostname: page-component-669899f699-7xsfk Total loading time: 0 Render date: 2025-04-25T03:52:28.659Z Has data issue: false hasContentIssue false

Opportunities and Challenges of Big Data Analytics in Crime Investigation

Published online by Cambridge University Press:  21 April 2025

Anthony Tik Tsuen Wong*
Affiliation:
School of Computing and Information Sciences, Saint Francis University, Hong Kong

Abstract

The integration of big data into criminal investigations is advancing significantly. Big data fundamentally involves the utilization of artificial intelligence technologies to analyse vast quantities of electronic information. The inherent features of big data contribute to minimizing subjectivity in investigative procedures and facilitate the evolution of criminal investigation methodologies and incident identification. However, challenges persist regarding the protection of rights and potential biases in data collection, as well as issues of subjectivity and the “black box effect” in data processing, alongside security concerns related to data storage. To address these challenges, it is essential to implement strategies such as enhancing the quality of big data, restricting the transparency of data processing methods and establishing a tiered protection framework for personal information.

Abstracto

Abstracto

La integración de big data en las investigaciones criminales está avanzando significativamente. Big data implica fundamentalmente la utilización de tecnologías de inteligencia artificial para analizar grandes cantidades de información electrónica. Las características inherentes de big data contribuyen a minimizar la subjetividad en los procedimientos de investigación y facilitan la evolución de las metodologías de investigación criminal y la identificación de incidentes. Sin embargo, persisten desafíos relacionados con la protección de los derechos y los posibles sesgos en la recopilación de datos, así como cuestiones de subjetividad y el “efecto caja negra” en el procesamiento de datos, junto con preocupaciones de seguridad relacionadas con el almacenamiento de datos. Para abordar estos desafíos, es esencial implementar estrategias como mejorar la calidad de big data, restringir la transparencia de los métodos de procesamiento de datos y establecer un marco de protección escalonado para la información personal.

Abstrait

Abstrait

L’intégration du big data dans les enquêtes criminelles progresse de manière significative. Le big data implique fondamentalement l’utilisation de technologies d’intelligence artificielle pour analyser de vastes quantités d’informations électroniques. Les caractéristiques inhérentes au big data contribuent à minimiser la subjectivité dans les procédures d’enquête et facilitent l’évolution des méthodologies d’enquête criminelle et l’identification des incidents. Cependant, des défis persistent concernant la protection des droits et les biais potentiels dans la collecte de données, ainsi que les problèmes de subjectivité et d’« effet boîte noire » dans le traitement des données, ainsi que les problèmes de sécurité liés au stockage des données. Pour relever ces défis, il est essentiel de mettre en oeuvre des stratégies telles que l’amélioration de la qualité du big data, la restriction de la transparence des méthodes de traitement des données et l’établissement d’un cadre de protection à plusieurs niveaux pour les informations personnelles.

摘要

摘要

大数据与刑事侦查的结合正在取得重大进展。大数据从根本上涉及利用人工智能技术分析大量电子信息。大数据的固有特性有助于最大限度地减少侦查程序中的主观性,促进刑事侦查方法和事件识别的发展。然而,在数据收集方面,权利保护和潜在偏见、数据处理中的主观性和“黑箱效应”问题以及与数据存储相关的安全问题仍然存在挑战。为了应对这些挑战,必须实施提高大数据质量、限制数据处理方法的透明度以及建立个人信息分级保护框架等战略。

الملخص

الملخص

يشهد دمج البيانات الضخمة في التحقيقات الجنائية تقدمًا كبيرًا. تتضمن البيانات الضخمة بشكل أساسي الاستفادة من تقنيات الذكاء الاصطناعي لتحليل كميات هائلة من المعلومات الإلكترونية. تساهم السمات المتأصلة للبيانات الضخمة في تقليل الذاتية في إجراءات التحقيق وتسهيل تطور منهجيات التحقيق الجنائي وتحديد الحوادث. ومع ذلك، لا تزال هناك تحديات فيما يتعلق بحماية الحقوق والتحيزات المحتملة في جمع البيانات، فضلاً عن قضايا الذاتية و”تأثير الصندوق الأسود”” في معالجة البيانات، إلى جانب المخاوف الأمنية المتعلقة بتخزين البيانات. ولمعالجة هذه التحديات، من الضروري تنفيذ استراتيجيات مثل تحسين جودة البيانات الضخمة، وتقييد شفافية أساليب معالجة البيانات، وإنشاء إطار حماية متعدد المستويات للمعلومات الشخصية.

Type
Article
Copyright
© International Society of Criminology, 2025

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Adam, I. and Fazekas, M.. 2021. “Are Emerging Technologies Helping Win the Fight against Corruption? A Review of the State of Evidence.” Information Economics and Policy 57:100950.CrossRefGoogle Scholar
Akinbowale, O. E., Mashigo, P., and Zerihun, M. F.. 2023. “The Integration of Forensic Accounting and Big Data Technology Frameworks for Internal Fraud Mitigation in the Banking Industry.” Cogent Business and Management 10(1):2163560.CrossRefGoogle Scholar
Barbaglia, L., Frattarolo, L., Onorante, L., Pericoli, F. M., Ratto, M., and Pezzoli, L. T.. 2023. “Testing Big Data in a Big Crisis: Nowcasting under COVID-19.” International Journal of Forecasting 39(4):1548–63.CrossRefGoogle Scholar
Bhardwaj, A., Mangat, V., Vig, R., Halder, S., and Conti, M.. 2021. “Distributed Denial of Service Attacks in Cloud: State-of-the-Art of Scientific and Commercial Solutions.” Computer Science Review 39:100332.CrossRefGoogle Scholar
Brayne, S. and Christin, A.. 2021. “Technologies of Crime Prediction: The Reception of Algorithms in Policing and Criminal Courts.” Social Problems 68(3):608–24.CrossRefGoogle ScholarPubMed
Chan, W. C. (editor). 2008. Support for Victims of Crime in Asia. Abingdon, Oxon: Routledge.Google Scholar
Chen, G. and Zheng, W.. 2007. “On the Reform of China’s Criminal Procedures for Trial Supervision.” Frontiers of Law in China 2:255–80.CrossRefGoogle Scholar
Dai, Y. 2020. “Personal Data Protection and Related Legislation in China: The Implications for International Trade Law.” Doctoral dissertation, University of Macau.Google Scholar
Dau, P. M., Dewinter, M., Witlox, F., Beken, T. V., and Vandeviver, C.. 2023. “Simple Indicators of Crime and Police: How Big Data Can Be Used to Reveal Temporal Patterns.” European Journal of Criminology 20(3):1146–63.CrossRefGoogle Scholar
Dijesh, P., Babu, S., and Vijayalakshmi, Y.. 2020. “Enhancement of E-Commerce Security through Asymmetric Key Algorithm.” Computer Communications 153:125–34.Google Scholar
Evans, S. W., Leese, M., and Rychnovská, D.. 2021. “Science, Technology, Security: Towards Critical Collaboration.” Social Studies of Science 51(2):189213.CrossRefGoogle ScholarPubMed
Ferrara, E. 2024. “The Butterfly Effect in Artificial Intelligence Systems: Implications for AI Bias and Fairness.” Machine Learning with Applications 15:100525.CrossRefGoogle Scholar
Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., Scardapane, S., Spinelli, I., Mahmud, M., and Hussain, A.. 2024. “Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence.” Cognitive Computation 16(1):4574.CrossRefGoogle Scholar
Janssen, M., Brous, P., Estevez, E., Barbosa, L. S., and Janowski, T.. 2020. “Data Governance: Organizing Data for Trustworthy Artificial Intelligence.” Government Information Quarterly 37(3):101493.CrossRefGoogle Scholar
Jiang, Q. 2023. “Big Data, Big Opportunity, and Big Future.” Pp. 123–37 in Digital China: Big Data and Government Managerial Decision, edited by Q. Jiang. Singapore: Springer Nature Singapore.CrossRefGoogle Scholar
Kounadi, O., Ristea, A., Araujo, A., and Leitner, M.. 2020. “A Systematic Review on Spatial Crime Forecasting.” Crime Science 9:7.CrossRefGoogle ScholarPubMed
Kronkvist, K., Borg, A., Boldt, M., and Gerell, M.. 2025. “Predicting Public Violent Crime Using Register and OpenStreetMap Data: A Risk Terrain Modeling Approach Across Three Cities of Varying Size.” Applied Spatial Analysis and Policy 18(1):9.CrossRefGoogle Scholar
Lauritsen, J. L. 2023. “The Future of Crime Data.” Criminology 61(2):187203.CrossRefGoogle Scholar
Mahendhiran, M., Apsara, A., Vasudevan, H., and Nithish, N.. 2024. “Certain Investigation on Crime Trend Prediction through Big Data Analytics and Mining.” AIP Conference Proceedings 2742(1):020027.CrossRefGoogle Scholar
Mandalapu, V., Elluri, L., Vyas, P., and Roy, N.. 2023. “Crime Prediction using Machine Learning and Deep Learning: A Systematic Review And Future Directions.” IEEE Access 11:60153–70.CrossRefGoogle Scholar
Mazeika, D. 2023. “The Effect of Unreported Gun-Related Violent Crime on Crime Hot Spots.” Security Journal 36(1):101–17.CrossRefGoogle Scholar
Mydlowski, L. and Turner-Moore, R.. 2025. “Tensions between Police Training and Practice for the Risk Assessment of Registered Sex Offenders in England and Wales.” Journal of Sexual Aggression 31(1):5366.CrossRefGoogle Scholar
Oatley, G. C. 2022. “Themes in Data Mining, Big Data, and Crime Analytics.” WIREs: Data Mining and Knowledge Discovery 12(2):e1432.Google Scholar
Okafor, R. C. 2023. “Exploring Cybercrime Victimization of Social Media Users: A Comparative Review of Routine Activities Theory and Low Self-Control.” SSRN, 23 May 2023, retrieved 28 February 2025 (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4451761).CrossRefGoogle Scholar
Page, J. and Soss, J.. 2021. “The Predatory Dimensions of Criminal Justice.” Science 374(6565):291–4.CrossRefGoogle ScholarPubMed
Peng, H. 2020. Law and Social Solidarity in Contemporary China: A Durkheimian Analysis. Abingdon, Oxon: Routledge.CrossRefGoogle Scholar
Phillips, C. and Bowling, B.. 2020. “Racism, Ethnicity, Crime and Criminal Justice.” Pp. 377–92 in Crime, Inequality and the State, edited by Vogel, M.. Abingdon, Oxon: Routledge.CrossRefGoogle Scholar
Qian, F., Cheng, J., Wang, X., Yang, Y., and Li, C.. 2021. “Design of In-Depth Security Protection System of Integrated Intelligent Police Cloud.” Pp. 356–65 in Advances on P2P, Parallel, Grid, Cloud and Internet Computing: Proceedings of the 15th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC-2020), edited by L. Barolli, M. Takizawa, T. Yoshihisa, F. Amato, and M. Ikeda. Cham: Springer International Publishing.CrossRefGoogle Scholar
Roy, S. and Chowdhury, I. R.. 2023. “Three Decades of GIS Application in Spatial Crime Analysis: Present Global Status and Emerging Trends.” The Professional Geographer 75(6):882904.CrossRefGoogle Scholar
Rudin, C. 2022. “Why Black Box Machine Learning Should Be Avoided for High-Stakes Decisions, In Brief.” Nature Reviews Methods Primers 2(1):81.CrossRefGoogle Scholar
Şengönül, E., Samet, R., Abu Al-Haija, Q., Alqahtani, A., Alturki, B., and Alsulami, A. A.. 2023. “An Analysis of Artificial Intelligence Techniques in Surveillance Video Anomaly Detection: A Comprehensive Survey.” Applied Sciences 13(8):4956.CrossRefGoogle Scholar
Shafik, W., Kalinaki, K., Fahim, K. E., and Adam, M.. 2025. “Safeguarding Data Privacy and Security in Federated Learning Systems.” Pp. 170–90 in Federated Deep Learning for Healthcare: A Practical Guide with Challenges and Opportunities, edited by A. Kaur, C. Kaushal, Md. M. Hassan, and S. T. Aung. London: Routledge.CrossRefGoogle Scholar
Simmons, R. 2021. “Race and Reasonable Suspicion.” Florida Law Review 73(2):413–71.Google Scholar
Smiley-McDonald, H. M., Attaway, P. R., Wenger, L. D., Greenwell, K., Lambdin, B. H., and Kral, A. H.. 2023. “‘All Carrots and No Stick’: Perceived Impacts, Changes in Practices, and Attitudes among Law Enforcement following Drug Decriminalization in Oregon State, USA.” International Journal of Drug Policy 118:104100.CrossRefGoogle Scholar
Taniguchi, Y. 2000. “Good Faith and Abuse of Procedural Rights in Japanese Civil Procedure.” Tulsa Journal of Comparative & International Law 8:164–82.Google Scholar
Verhelst, H. M., Stannat, A. W., and Mecacci, G.. 2020. “Machine Learning against Terrorism: How Big Data Collection and Analysis Influences the Privacy–Security Dilemma.” Science and Engineering Ethics 26(6):2975–84.CrossRefGoogle ScholarPubMed
Vu, M. L. 2023. “Guardianship against Crime: Exploring the Individual Factors on Exhibiting Guardianship.” Bachelor’s thesis, University of Twente.Google Scholar
Wu, T. and Wang, J.. 2020. “Deep Mining Stable and Nontoxic Hybrid Organic–Inorganic Perovskites for Photovoltaics via Progressive Machine Learning.” ACS Applied Materials and Interfaces 12(52):57821–31.CrossRefGoogle ScholarPubMed
Zhang, C. 2023. “Case Filing and Determination of the Adjudicatory Personnel.” Pp. 39–50 in Win in Chinese Courts: Practice Guide to Civil Litigation in China. Singapore: Springer Nature Singapore.CrossRefGoogle Scholar