Survey on Crime Analysis Using Data Mining Based on Mobile Platforms
DOI:
https://doi.org/10.29304/jqcm.2021.13.1.770Keywords:
Data mining, Crime analysis, Mobile PlatformAbstract
With a large rise in crime globally, there is a necessity to analysis crime data to bring down the rate of crime. This encourages the police and people to occupy the required measures and more effectively restricting the crimes. Crime analysis and spatial analysis using Geographic Information System(GIS) tools such as hot spot generation, zoning, navigation and crime profiling, detection of mobile locations and various web-based applications are well known and can be used scientifically for citizens advancement, while crime prediction and control can be used effectively. Crime data analysts will enable the law enforcement officials to speed up the crime resolution process. We can analyze previously unknown, helpful information from unstructured data by using the theory of data mining. The purpose of this work is to perform a survey on the crime analysis by using several data mining techniques that has been applied towards criminal activities over the years to be a valuable guide to young researchers that are interested in this field.
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References
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[27] Y.Huang, C.Te Li and S. Jeng, “Mining Location-based Social Networks for Criminal Activity Prediction” , Proceedings of 24th IEEE , Proceedings of 24th IEEE International Conference on Wireless and Optical Communication, pp. 185-190, 2015.
[2] H. B. Fredrick David and A. Suruliandi, “Survey on Crime Analysis and Prediction Using Data Mining Techniques,” ICTACT J. Soft Comput., vol. 7, no. 3, pp. 1459–1466, 2017, doi: 10.21917/ijsc.2017.0202.
[3] S. R. Bandekar and C. Vijayalakshmi, “Design and analysis of machine learning algorithms for the reduction of crime rates in India,” Procedia Comput. Sci., vol. 172, no. 2019, pp. 122–127, 2020, doi: 10.1016/j.procs.2020.05.018.
[4] A. N. Yousif and A. S. Elameer, “An expert system for the tourism destinations in Iraq based on the google maps API,” Proc. - 2018 1st Annu. Int. Conf. Inf. Sci. AiCIS 2018, pp. 1–6, 2019, doi: 10.1109/AiCIS.2018.00014.
[5] K. Strom, “The author(s) shown below used Federal funding provided by the U.S. Department of Justice to prepare the following resource: Document Title: Research on the Impact of Technology on Policing Strategy in the 21st Century, Final Report,” 2017, [Online]. Available: https://www.ncjrs.gov/pdffiles1/nij/grants/251140.pdf.
[6] C. Lum, C. S. Koper, and J. Willis, “Understanding the Limits of Technology’s Impact on Police Effectiveness,” Police Q., vol. 20, no. 2, pp. 135–163, 2017, doi: 10.1177/1098611116667279.
[7] I. Shadeed Al-Mejibli and D. Hamed Abd, “Mushroom Diagnosis Assistance System Based on Machine Learning by Using Mobile Devices,” J. Al-Qadisiyah Comput. Sci. Math., vol. 9, no. 2, pp. 103–113, 2017, doi: 10.29304/jqcm.2017.9.2.319.
[8] A. Akpan, B. Barida, and M. Shedrack, “Toward an effective crime mapping solution for Nigeria: Leveraging Emerging Mobile Platforms,” vol. 5, no. 9, pp. 108–118, 2018, [Online]. Available: https://www.researchgate.net/publication/329216903_Crime_Mapping_Solution.
[9] W. Graham, “Benefits of implementation of mobile devices with frontline police officers in Police Scotland,” no. January, 2021.
[10] Ziema Mushtaq, “Mobile Application Development Trends and Challenges,” Int. J. Eng. Technol., vol. 3, no. 8, pp. 1096–1099, 2016, doi: 10.1590/S0034-89102005000600008.
[11] A. Khandeparkar, R. Gupta, and B. S. B.Sindhya, “An Introduction to Hybrid Platform Mobile Application Development,” Int. J. Comput. Appl., vol. 118, no. 15, pp. 31–33, 2015, doi: 10.5120/20824-3463.
[12] S. Prabakaran and S. Mitra, “Survey of Analysis of Crime Detection Techniques Using Data Mining and Machine Learning,” J. Phys. Conf. Ser., vol. 1000, no. 1, 2018, doi: 10.1088/1742-6596/1000/1/012046.
[13] K. C. Lekha and S. Prakasam, “Data mining techniques in detecting and predicting cyber crimes in banking sector,” 2017 Int. Conf. Energy, Commun. Data Anal. Soft Comput. ICECDS 2017, no. August, pp. 1639–1643, 2018, doi: 10.1109/ICECDS.2017.8389725.
[14] B. Sivanagaleela and S. Rajesh, “Crime analysis and prediction using fuzzy c-means algorithm,” Proc. Int. Conf. Trends Electron. Informatics, ICOEI 2019, no. Icoei, pp. 595–599, 2019, doi: 10.1109/ICOEI.2019.8862691.
[15] F. Mata et al., “A Mobile Information System Based on Crowd-Sensed and Official Crime Data for Finding Safe Routes: A Case Study of Mexico City,” Mob. Inf. Syst., vol. 2016, 2016, doi: 10.1155/2016/8068209.
[16] M. A. Awal, J. Rabbi, S. I. Hossain, and M. M. A. Hashem, “Using linear regression to forecast future trends in crime of Bangladesh,” 2016 5th Int. Conf. Informatics, Electron. Vision, ICIEV 2016, no. June 2020, pp. 333–338, 2016, doi: 10.1109/ICIEV.2016.7760021.
[17] A. Baqir, S. U. Rehman, S. Malik, F. U. Mustafa, and U. Ahmad, “Evaluating the Performance of Hierarchical Clustering algorithms to Detect Spatio-Temporal Crime Hot-Spots,” 2020 3rd Int. Conf. Comput. Math. Eng. Technol. Idea to Innov. Build. Knowl. Econ. iCoMET 2020, 2020, doi: 10.1109/iCoMET48670.2020.9074125.
[18] H. K. R. Toppireddy, B. Saini, and G. Mahajan, “Crime Prediction & Monitoring Framework Based on Spatial Analysis,” Procedia Comput. Sci., vol. 132, no. Iccids, pp. 696–705, 2018, doi: 10.1016/j.procs.2018.05.075.
[19] A. Deshmukh, S. Banka, S. B. Dcruz, S. Shaikh, and A. K. Tripathy, “Safety App: Crime Prediction Using GIS,” 2020 3rd Int. Conf. Commun. Syst. Comput. IT Appl. CSCITA 2020 - Proc., pp. 120–124, 2020, doi: 10.1109/CSCITA47329.2020.9137772.
[20] S. Soni, V. G. Shankar, and S. Chaurasia, “Route-the safe: A robust model for safest route prediction using crime and accidental data,” Int. J. Adv. Sci. Technol., vol. 28, no. 16, pp. 1415–1428, 2019.
[21] P. Yerpude and V. Gudur, “Predictive Modelling of Crime Dataset Using Data Mining,” Int. J. Data Min. Knowl. Manag. Process, vol. 7, no. 4, pp. 43–58, 2017, doi: 10.5121/ijdkp.2017.7404.
[22] F. Ayele, “Appling Data Mining Technique for Crime Prevention: The Case of Hossaena Town Police Office,” Int. J. Adv. Eng. Res. Sci., vol. 7, no. 1, pp. 136–140, 2020, doi: 10.22161/ijaers.71.17.
[23] B. Panja, “Crime Analysis Mapping, Intrusion Detection - Using.pdf,” vol. 7, pp. 6–10.
[24] T. T. Nguyen, A. Hatua, and A. H. Sung, “Building a Learning Machine Classifier with Inadequate Data for Crime Prediction,” J. Adv. Inf. Technol., no. January, pp. 141–147, 2017, doi: 10.12720/jait.8.2.141-147.
[25] S. Kim, P. Joshi, P. S. Kalsi, and P. Taheri, “Crime Analysis Through Machine Learning,” 2018 IEEE 9th Annu. Inf. Technol. Electron. Mob. Commun. Conf. IEMCON 2018, pp. 415–420, 2019, doi: 0.1109/IEMCON.2018.8614828.
[26] S. Qayyum and H. Shareef, ” A Survey of Data Mining Techniques for Crime detection,” 2018 university of Sindh journal of information communication technology (USJICT), vol.2, pp.1-6 communication technology (USJICT), vol.2, pp.1-6
[27] Y.Huang, C.Te Li and S. Jeng, “Mining Location-based Social Networks for Criminal Activity Prediction” , Proceedings of 24th IEEE , Proceedings of 24th IEEE International Conference on Wireless and Optical Communication, pp. 185-190, 2015.
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Published
2021-03-10
How to Cite
Hussain, F. S., & Aljuboori, A. F. (2021). Survey on Crime Analysis Using Data Mining Based on Mobile Platforms. Journal of Al-Qadisiyah for Computer Science and Mathematics, 13(1), Comp Page 36 – 44. https://doi.org/10.29304/jqcm.2021.13.1.770
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Computer Articles
