Journal of Information Systems Engineering and Management

Journal of Information Systems Engineering and Management, 2020 - Volume 5 Issue 4, Article No: em0126
https://doi.org/10.29333/jisem/8492

Published Online: 30 Aug 2020

Views: 1478 | Downloads: 1045

Crime analysis represents a great challenge to law enforcement considering that the sources to use for generating intelligence are diverse in content and/or structure. However, in recent years, techniques such as natural language processing, a field of computing, artificial intelligence and linguistics have been developed that allow to study the interactions between computers and human language, and that in turn can be used effectively in the analysis of large amounts of texts and in the subsequent derivation of interesting analytical results. This paper presents a model for analysis of criminal events from online newspapers, identifying the areas with the highest crime rates through the detection and geographical mapping of critical points and the analysis of the nature of the criminal event. The evaluation of the proposed model to estimate the perception of crime in the domain of the proposed communes indicates that it is efficient in categorizing the news and the nature of these (validated by the performance indicators).

criminal analysis geographical mapping perception of criminality web mining

• Abdul Jalil, M. @ M., Ling, C. P., Mohamad Noor, N. M. and Mohd., F. (2017). Knowledge Representation Model for Crime Analysis. Procedia Comput. Sci., 116, 484-491. https://doi.org/10.1016/j.procs.2017.10.067
• Achsan, H. T. Y. and Wibowo, W. C. (2014). A fast distributed focused-web crawling. Procedia Eng., 69, 492-499. https://doi.org/10.1016/j.proeng.2014.03.017
• Adnan, M., Nagi, M., Kianmehr, K., Tahboub, R., Ridley, M. and Rokne, J. (2011). Promoting where, when and what? An analysis of web logs by integrating data mining and social network techniques to guide ecommerce business promotions. Soc. Netw. Anal. Min., 1(3), 173-185. https://doi.org/10.1007/s13278-010-0015-3
• Alami, S. and Elbeqqali, O. (2015). Cybercrime profiling: Text mining techniques to detect and predict criminal activities in microblog posts. 2015 10th Int. Conf. Intell. Syst. Theor. Appl. SITA 2015. https://doi.org/10.1109/SITA.2015.7358435
• Alhajj, R. and Rokne, J. (2018). Encyclopedia of Social Network Analysis and Mining. New York, NY: Springer New York.
• Andrienko, G., Gunopulos, D., Ioannidis, Y., Kalogeraki, V., Katakis, I., Morik, K. and Verscheure, O. (2016). Mining Urban Data (Part B). Inf. Syst., 57, 75-76. https://doi.org/10.1016/j.is.2016.01.001
• Armando, H., Eraso, O., Alberto, C. and Lozada, C. (2011). Stemming in the Spanish Language for Documents Recovered from the Web. (58), 107-114.
• Arulanandam, R., Savarimuthu, B. T. R. and Purvis, M. A. (2014). Extracting Crime Information from Online Newspaper Articles. In Proceedings of the Second Australasian Web Conference (AWC 2014), Auckland, New Zealand, Awc, 31-38.
• Badal-Valero, E., Alvarez-Jareño, J. A. and Pavía, J. M. (2018). Combining Benford’s Law and machine learning to detect money laundering. An actual Spanish court case. Forensic Sci. Int., 282, 24–34. https://doi.org/10.1016/j.forsciint.2017.11.008
• Belur, J. and Johnson, S. (2018). Is crime analysis at the heart of policing practice? A case study. Polic. Soc., 28(7), 768-786. https://doi.org/10.1080/10439463.2016.1262364
• Boba, R. (2001). Introductory Guide to Crime Analysis and Mapping. Available at: http://www.ncjrs.gov/App/abstractdb/AbstractDBDetails.aspx?id=194685
• Boba, R. (2016). Crime Analysis with Crime Mapping (4th Ed.). London, UK: SAGE Publications.
• Cesur, R., Ceyhan, E. B., Kermen, A. and Sağiroğlu, Ş. (2017). Determination of potential criminals in social network. Gazi Univ. J. Sci., 30(1), 121–131.
• Chen, H., Chung, W., Xu, J. J., Wang, G., Qin, Y. and Chau, M. (2004). Crime data mining: A general framework and some examples. Computer (Long. Beach. Calif)., 37(4), 50-56. https://doi.org/10.1109/MC.2004.1297301
• Çinar, M. S., Genç, B. and Sever, H. (2019). Identifying criminal organizations from their social network structures. Turkish J. Electr. Eng. Comput. Sci., 27(1), 421–436. https://doi.org/10.3906/elk-1806-52
• Dammert, L. and Lunecke, A. (2002). Victimización y Temor en Chile: Revisión Teórico Empírica en Doce Comunas del País. Santiago, Chile. Available at: https://www.cesc.uchile.cl/publicaciones/se_01_victimizacion.pdf
• Daniel, J. and Martin, J. H. (2018). N-gram Language Models. In Speech and Language Processing (3rd Ed.), pp. 28.
• Darwiche, A. (2009). Modeling and Reasoning with Bayesian Networks (1st Ed.). Los Angeles, USA: Cambridge University Press.
• De Farias, A. M. G., Cintra, M. E., Felix, A. C. and Cavalcante, D. L. (2018). Definition of Strategies for Crime Prevention and Combat Using Fuzzy Clustering and Formal Concept Analysis. Int. J. Uncertainty, Fuzziness Knowlege-Based Syst., 26(3), 429–452. https://doi.org/10.1142/S0218488518500216
• de Mendonça, R. R., de Brito, D. F., de Franco Rosa, F., dos Reis, J. C. and Bonacin, R. (2020). A framework for detecting intentions of criminal acts in social media: A case study on twitter. Inf., 11(3), 1-40. https://doi.org/10.3390/info11030154
• De Sousa Netto, M. C., Pinto, A. L. and Semeler, A. R. (2019). Man and machines against crime: An approach based on visual learning. Educ. Inf., 35(3), 251–262. https://doi.org/10.3233/EFI-190280
• Dowerah Baruah, T. (2012). Effectiveness of Social Media as a tool of communication and its potential for technology enabled connections: A micro-level study. Int. J. Sci. Res. Publ., 2(5), 1-10.
• Dressel, J. and Farid, H. (2018). The accuracy, fairness, and limits of predicting recidivism. Sci. Adv., 4(1), 1–6. https://doi.org/10.1126/sciadv.aao5580
• European Commission (2011). Crime and deviance in the EU. Brussels, Belgium. Available at: https://ec.europa.eu/research/social-sciences/pdf/policy_reviews/crime-and-deviance_en.pdf
• Evans, J. M. and Kebbell, M. R. (2012). The effective analyst: A study of what makes an effective crime and intelligence analyst. Polic. Soc., 22(2), 204–219. https://doi.org/10.1080/10439463.2011.605130
• Fundación Paz Ciudadana (2020). Índice Paz Ciudadana. Santiago, Chile. Available at: https://pazciudadana.cl/wp-content/uploads/2019/10/IPC-2019-Conferencia.pdf
• Gerber, M. S. (2014). Predicting crime using Twitter and kernel density estimation. Decis. Support Syst., 61, 115-125. https://doi.org/10.1016/j.dss.2014.02.003
• Goldstein Braunfeld, E. (2003). Los Robos con Violencia en el Gran Santiago: Magnitudes y Características. Santiago, Chile. Available at: https://www.cesc.uchile.cl/publicaciones/se_05_goldstein.pdf
• Grosan, C. and Abraham, A. (2011). Intelligent Systems (1st Ed., vol. 17). Berlin, Heidelberg, Germany: Springer Berlin Heidelberg.
• Hammouda, K. M. and Kamel, M. S. (2004) Efficient phrase-based document indexing for web document clustering. IEEE Trans. Knowl. Data Eng., 16(10), 1279-1296. https://doi.org/10.1109/TKDE.2004.58
• Hotho, A., Nürnberger, A. and Paaß, G. (2005). A Brief Survey of Text Mining. Ldv Forum, 20(1), 19-62.
• Instituto Nacional de Estadísticas (2020). Encuesta Nacional Urbana de Seguridad Ciudadana 2019. Santiago, Chile. Available at: http://www.seguridadpublica.gov.cl/enusc_2012.html
• Jayaweera, I., Sajeewa, C., Liyanage, S., Wijewardane, T., Perera, I. and Wijayasiri, A. (2015). Crime analytics: Analysis of crimes through newspaper articles. MERCon 2015 - Moratuwa Eng. Res. Conf., pp. 277-282. https://doi.org/10.1109/MERCon.2015.7112359
• Justicia de la Torre, C., Sánchez, D., Blanco, I. and Martín-Bautista, M. J. (2018). Text Mining: Techniques, Applications, and Challenges. Int. J. Uncertainty, Fuzziness Knowledge-Based Syst., 26(04), 553-582. https://doi.org/10.1142/S0218488518500265
• Kahya-Özyirmidokuz, E. (2016). Analyzing unstructured Facebook social network data through web text mining: A study of online shopping firms in Turkey. Inf. Dev., 32(1), 70-80. https://doi.org/10.1177/0266666914528523
• Kao, A. and Poteet, S. R. (2007). Natural Language Processing and Text Mining (1st Ed.). London, UK: Springer London.
• Kaushik, A. and Naithani, S. (2016). A Comprehensive Study of Text Mining Approach. IJCSNS Int. J. Comput. Sci. Netw. Secur., 16(2), 69.
• Kelleher, J., Mac Namee, B. and D’Arcy, A. (2015). Fundamentals of Machine Learning for Predictive Data Analytics (1st Ed.). London, UK: The MIT Press.
• Kumar, S. N. (2015). World towards Advance Web Mining: A Review. Am. J. Syst. Softw., 3(2), 44-61. https://doi.org/10.12691/AJSS-3-2-3
• Li, Y. and Zhong, N. (2004). Web mining model and its applications for information gathering. Knowledge-Based Syst., 17(5-6), 207-217. https://doi.org/10.1016/j.knosys.2004.05.002
• Li, Y. S. and Qi, M. L. (2019). An approach for understanding offender modus operandi to detect serial robbery crimes. J. Comput. Sci., 36, 101024. https://doi.org/10.1016/j.jocs.2019.101024
• Lim, C., Kim, K. J. and Maglio, P. P. (2018). Smart cities with big data: Reference models, challenges, and considerations. Cities, 82(February), 86–99, 2018, https://doi.org/10.1016/j.cities.2018.04.011
• Lim, M., Abdullah, A., Jhanjhi, N. Z. and Khurram Khan, M. (2020). Situation-Aware Deep Reinforcement Learning Link Prediction Model for Evolving Criminal Networks. IEEE Access, 8, 16550–16559. https://doi.org/10.1109/ACCESS.2019.2961805
• Lim, M., Abdullah, A., Jhanjhi, N. Z. and Supramaniam, M. (2019a). Hidden link prediction in criminal networks using the deep reinforcement learning technique. Computers, 8(1), 1–13. https://doi.org/10.3390/computers8010008
• Lim, M., Abdullah, A., Jhanjhi, N., Khurram Khan, M. and Supramaniam, M. (2019b). Link prediction in time-evolving criminal network with deep reinforcement learning technique. IEEE Access, 7, 184797–184807. https://doi.org/10.1109/ACCESS.2019.2958873
• Lin, Y. L., Yen, M. F. and Yu, L. C. (2018). Grid-based crime prediction using geographical features. ISPRS Int. J. Geo-Information, 7(8), 298. https://doi.org/10.3390/ijgi7080298
• Liu, B. (2011). Web Data Mining. Berlin, Heidelberg: Springer Berlin Heidelberg.
• Majnik, M. and Bosnić, Z. (2013). ROC analysis of classifiers in machine learning: A survey. Intell. Data Anal., 17(3), 531-558. https://doi.org/10.3233/IDA-130592
• Makki, S., Assaghir, Z., Taher, Y., Haque, R., Hacid, M. S. and Zeineddine, H. (2019). An Experimental Study with Imbalanced Classification Approaches for Credit Card Fraud Detection. IEEE Access, 7, 93010–93022. https://doi.org/10.1109/ACCESS.2019.2927266
• Marinescu, V. and Balica, E. (2018). Migration and Crime Realities and Media Representations. Springer Nature Switzerland AG.
• Mawby, R. I. (2004). Crime and Disorder: Perceptions of Business People in Cornwall, England. Int. Rev. Vict., 11(2-3), 313-332. https://doi.org/10.1177/026975800401100207
• Mawby, R. I. (2015). Exploring the relationship between crime and place in the countryside *. J. Rural Stud., 39, 262-270. https://doi.org/10.1016/j.jrurstud.2014.12.003
• Meijer, A. and Wessels, M. (2019). Predictive Policing: Review of Benefits and Drawbacks. Int. J. Public Adm., 42(12), 1031-1039. https://doi.org/10.1080/01900692.2019.1575664
• Moghaddam, A. S., Hosseinkhani, J., Chuprat, S., Taherdoost, H. and Baravati, H. B. (2013). Proposing a framework for exploration of crime data using web structure and content mining. Res. J. Appl. Sci. Eng. Technol., 6(19), 3617-3624. http://doi.org/10.19026/rjaset.6.3568
• Mukhopadhyay, D. (2019). Web Searching and Mining (1st Ed.). Singapore: Springer Singapore.
• Nokhbeh Zaeem, R., Manoharan, M., Yang, Y. and Barber, K. S. (2017). Modeling and analysis of identity threat behaviors through text mining of identity theft stories. Comput. Secur., 65, 50–63. https://doi.org/10.1016/j.cose.2016.11.002
• O’Shea, T. C. and Nicholls, K. (2002). Crime Analysis in America. Massachusetts, USA. Available at: https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/812146-commercialmdhd-truckfuelefficiencytechstudy-v2.pdf
• Olavarría, M. (2006). El crimen en Chile Una mirada desde las víctimas. Santiago, Chile. Available at: http://www.cesc.uchile.cl/publicaciones/se_13_victimizacion.pdf
• Pan, Y., Tian, Y., Liu, X., Gu, D. and Hua, G. (2016). Urban Big Data and the Development of City Intelligence. Engineering, 2(2), 171–178. https://doi.org/10.1016/J.ENG.2016.02.003
• Petherick, W. (2015). Applied Crime Analysis (1st Ed.). Waltham, Massachusets, USA: Elsevier.
• Pinto, P., Theodoro, I., Arrais, M. and Oliveira, J. (2017). Data mining and social web semantics: A case study on the use of hashtags and memes in Online Social Networks. IEEE Lat. Am. Trans., 15(12), 2276-2281. https://doi.org/10.1109/TLA.2017.8071088
• Po, L. and Rollo, F. (2018). Building an Urban Theft Map by Analyzing Newspaper Crime Reports. In Proceedings - 13th International Workshop on Semantic and Social Media Adaptation and Personalization, SMAP 2018, pp. 13–18. https://doi.org/10.1109/SMAP.2018.8501866
• Python Software Foundation (2019). “Requests: HTTP for HumansTM”. Available at: https://pypi.org/project/requests/ (Accessed: 11 July 2019).
• Rapidminer.com (2018). Available at: https://rapidminer.com/products/ (Accessed: 27 June 2018).
• Richardson, L. (2019). Beautiful Soup: We called him Tortoise because he taught us. Available at: https://www.crummy.com/software/BeautifulSoup/ (Accessed: 4 March 2020).
• Rouvroy, A. (2016). Of Data and Men’ Fundamental Rights and Fredoms in a World of Data. Strasbourg, Germany. Available at: https://rm.coe.int/16806a6020
• Saldaña, M., Escobar, C., Galvez, E., Torres, D. and Toro, N. (2020). Mapping of the Perception of Theft Crimes from Analysis of Newspaper Articles Online. In 15th Iberian Conference on Information Systems and Technologies (CISTI), June, pp. 1-7. https://doi.org/10.23919/CISTI49556.2020.9141154
• Saldaña, M., Flores, V., Toro, N. and Leiva, C. (2019). Representation for a prototype of recommendation system of operation mode in copper mining. In 2019 14th Iberian Conference on Information Systems and Technologies (CISTI), June, 1-4. https://doi.org/10.23919/CISTI.2019.8760884
• Sanders, C. and Condon, C. (2017). Crime analysis and cognitive effects: the practice of policing through flows of data. Glob. Crime, 18(3), 237–255. https://doi.org/10.1080/17440572.2017.1323637
• Sathyadevan, S., Devan, M. S. and Surya Gangadharan, S. (2014). Crime analysis and prediction using data mining. 1st Int. Conf. Networks Soft Comput. ICNSC 2014 - Proc., August, 406-412. https://doi.org/10.1109/CNSC.2014.6906719
• Sidana, A. and Aggarwal, H. (2017). Review of web usage of data mining in web mining. Int. J. Adv. Res. Comput. Sci., 8(5), 1-5.
• Song, J., Song, T. M., Seo, D. C. and Jin, J. H. (2016). Data Mining of Web-Based Documents on Social Networking Sites That Included Suicide-Related Words Among Korean Adolescents. J. Adolesc. Heal., 59(6), 668-673. https://doi.org/10.1016/j.jadohealth.2016.07.025
• Spadon, G., Scabora, L. C., Oliveira, P. H., Araujo, M. V. S., et al. (2017). Behavioral Characterization of Criminality Spread in Cities. Procedia Comput. Sci., 108, 2537-2541. https://doi.org/10.1016/J.PROCS.2017.05.118
• Stenton, A. E. (2006). Crime Analysis: An Examination of Crime Prevention and Reduction Strategies. University of Ottawa.
• Talib, R., Kashif, M., Ayesha, S. and Fatima, F. (2016). Text Mining: Techniques, Applications and Issues. Int. J. Adv. Comput. Sci. Appl., 7(11), 414-418. https://doi.org/10.14569/ijacsa.2016.071153
• Thelwall, M. (2001). A web crawler design for data mining. J. Inf. Sci., 27(5), 319-325. https://doi.org/10.1177/016555150102700503
• Tollenaar, N. and van der Heijden, P. G. M. (2009). Which method predicts recidivism best?: a comparison of statistical, machine learning and data mining predictive models. Journal of the Royal Statistical Society, Statistics in Society, Series A, 176(2), 565-584. https://doi.org/10.1111/j.1467-985X.2012.01056.x
• Tollenaar, N. and Van Der Heijden, P. G. M. (2019). Optimizing predictive performance of criminal recidivism models using registration data with binary and survival outcomes. PLOS ONE, 14(3), e0213245. https://doi.org/10.1371/journal.pone.0213245
• Vellani, K. H. (2010). Crime Analysis: for Problem Solving Security Professionals in 25 Small Steps. Available at: https://popcenter.asu.edu/sites/default/files/library/reading/PDFs/crimeanalysis25steps.pdf
• Verma, T. and Gaur, D. (2014). Tokenization and Filtering Process in RapidMiner. Int. J. Appl. Inf. Syst., 7(2), 16-18. https://doi.org/10.5120/ijais14-451139
• Vishal Gupta, G. S. L. (2009). A Survey of Text Mining Techniques and Applications. J. Emerg. Technol. web Intell., 1(1), 17. https://doi.org/10.4304/jetwi.1.1.60-76
• Vishwakarma, R. K. and Shankar, R. (2014). Modeling brain and behavior of a terrorist through fuzzy logic and ontology. IEEE Int. Conf. Ind. Eng. Eng. Manag., 857-861. https://doi.org/10.1109/IEEM.2013.6962533
• Yang, X., Sun, N., Zhang, Y. and Kong, D. (2008). General Framework for Text Classification Based on Domain Ontology. Semant. Media Adapt. Pers. 2008. SMAP ‘08. Third Int. Work., pp. 147–152. https://doi.org/10.1109/SMAP.2008.17

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.