A Crime Data Analysis of Prediction Based on Classification Approaches
Main Article Content
Abstract
Crime is considered as an unlawful activity of all kinds and it is punished by law. Crimes have an impact on a society's quality of life and economic development. With a large rise in crime globally, there is a necessity to analyze 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. The purpose of this research is to develop predictive models that can aid in crime pattern analysis and thus support the Boston department's crime prevention efforts. The geographical location factor has been adopted in our model, and this is due to its being an influential factor in several situations, whether it is traveling to a specific area or living in it to assist people in recognizing between a secured and an unsecured environment. Geo-location, combined with new approaches and techniques, can be extremely useful in crime investigation. The aim is focused on comparative study between three supervised learning algorithms. Where learning used data sets to train and test it to get desired results on them. Various machine learning algorithms on the dataset of Boston city crime are Decision Tree, Naïve Bayes and Logistic Regression classifiers have been used here to predict the type of crime that happens in the area. The outputs of these methods are compared to each other to find the one model best fits this type of data with the best performance. From the results obtained, the Decision Tree demonstrated the highest result compared to Naïve Bayes and Logistic Regression.
Received 12/5/2021
Accepted 1/8/2021
Published Online First 20/3/2022
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
References
Yerpude P, Gudur V. Predictive Modelling of Crime Dataset Using Data Mining. IJDKP. 2017;7(4):43–58.
Chutia D, Santra M. Mapping of crime incidences and hotspot analysis through incremental auto correlation -A case study of Shillong city , Meghalaya , India. ISG. 2020;14(April). 61-70 .
Hassani H, Huang X, Silva ES, Ghodsi M. A review of data mining applications in crime. Stat Anal Data Min. 2016;9(3): 139–154 .
Tasnim S, Sarker P, Hossain A. A Classification Approach to Predict Severity of Crime on Boston City Crime Data. 7th Int Conf Data Sci SDGs. 2019;(December).405- 412 p.
Alkhatib B, Eddin MMK. Voice identification using MFCC and vector quantization. Baghdad Sci J. 2020;17(3):1019–28.
Ali BA, Gorgees HM, Kathum RI. Modeling human capital impact on the development of the iraqi oil industry. Baghdad Sci J. 2019;16(4):1080–6.
Patwary MKH, Haque MM. A semi-supervised machine learning approach using K-means algorithm to prevent burst header packet flooding attack in optical burst switching network. Baghdad Sci J. 2019;16(3):804–15.
Alzubaidi L, Fadhel MA, Al-Shamma O, Zhang J, Santamaría J, Duan Y, et al. Towards a better understanding of transfer learning for medical imaging: A case study. Appl Sci. 2020;10(13).
Yin J, Michael IA, Afa IJ. Machine Learning Algorithms for Visualization and Prediction Modeling of Boston Crime Data. 2020;1(February):1–15.
Almanie T, Mirza R, Lor E. Crime Prediction Based on Crime Types and Using Spatial and Temporal Criminal Hotspots. Int J Data Min Knowl Manag Process(IJDKP). 2015;5(4):01–19.
Mata F, Torres-Ruiz M, Guzman G, Quintero R, Zagal-Flores R, Moreno-Ibarra M, 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. 2016;2016:11.
Awal MA, Rabbi J, Hossain SI, Hashem MMA. Using linear regression to forecast future trends in crime of Bangladesh. 2016 5th Int Conf Informatics, Electron Vision, (ICIEV) 2016. 2016;(June 2020):333–8.
Toppireddy HKR, Saini B, Mahajan G. Crime Prediction & Monitoring Framework Based on Spatial Analysis. Procedia Comput Sci [Internet]. 2018;132(Iccids):696–705. Available from: https://doi.org/10.1016/j.procs.2018.05.075
Deshmukh A, Banka S, Dcruz SB, Shaikh S, Tripathy AK. Safety App: Crime Prediction Using GIS. 3rd Int Conf Commun Syst, Computing and IT Applications; 2020:120–124.
Crimes in Boston | Kaggle [Internet]. Available from: https://www.kaggle.com/ankkur13/boston-crime-data
Soni S, Shankar VG, Chaurasia S. Route-the safe: A robust model for safest route prediction using crime and accidental data. Int J Adv Sci Technol(IJAST). 2019;28(16):1415–28.
Ridzuan Khairuddin A, Alwee R, Haron H. A Comparative Analysis of Artificial Intelligence Techniques in Forecasting Violent Crime Rate. IOP Conf Ser Mater Sci Eng. 2020;864(1).
Ray S. A Quick Review of Machine Learning Algorithms. Proc Int Conf Mach Learn Big Data, Cloud Parallel Comput Trends, Prespectives Prospect Com 2019((Com-IT-Con),). 2019;35–9.
Razzaq Abdul Hussein R, Sadik Croock DM, Mahdi Al-Qaraawi DS. Improvement of Criminal Identification by Smart Optimization Method. MATEC Web Conf. 2019;281:05003.
Kim S, Joshi P, Kalsi PS, Taheri P. Crime Analysis Through Machine Learning. 2018 IEEE 9th Annu Inf Technol Electron Mob Commun Conf (IEMCON) 2018. 2019;415–20.
Sivanagaleela B, Rajesh S. Crime analysis and prediction using fuzzy c-means algorithm. Proc Int Conf Trends Electron Informatics, ICOEI 2019. 2019;(Icoei):595–9.