Refine your search
Collections
Co-Authors
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Gelbolingo, Mitziel C.
- Inflation-Crime Nexus: A Predictive Analysis of Crime Rate Using Inflationary Indicator in Municipalities of North Cotabato, Philippines
Abstract Views :50 |
PDF Views:25
Authors
Michaelangelo R. Serrano
1,
Nero L. Hontiveros
1,
EJ Ryle C. Mosquera
1,
Aura Mae M. Celestino
1,
John Louie D. Vilar
1,
Junuel S. Baroy
1,
Mitziel C. Gelbolingo
1
Affiliations
1 College of Information Technology and Engineering, Notre Dame of Midsayap College, Midsayap, Cotabato, PH
1 College of Information Technology and Engineering, Notre Dame of Midsayap College, Midsayap, Cotabato, PH
Source
AIRCC's International Journal of Computer Science and Information Technology, Vol 15, No 4 (2023), Pagination: 97-106Abstract
Predicting the occurrence of robberies poses significant challenges, requiring valuable data to assist law enforcement personnel in crime prevention and solving efforts. This research paper introduces Crata, a tool that leverages linear regression to predict the robbery frequency in North Cotabato, Philippines. The development of Crata was driven by the analysis of historical data encompassing robbery incidents and inflation rates from 2010 to 2022. The research emphasizes the importance of utilizing the inflation rate as a crucial predictor in determining the robbery frequency. By incorporating this variable into the predictive model, Crata enables more accurate predictions, thus empowering law enforcement agencies with vital insights to address and mitigate crime problems proactively. The robustness and reliability of the tool were assessed through comprehensive evaluations, demonstrating its practical utility and effectiveness. This paper significantly contributes to crime prevention by providing a reliable and data-driven approach to anticipating robberies, enhancing proactive efforts to safeguard the community, and combat crimes. Further research and development are warranted to refine the tool and expand its applicability to other regions for strengthening crime prevention strategies on a broader scale.Keywords
crata; linear regression; predictors; crime analysis; probabilityFull Text
References
- H. Wang, D. Kifer, C. Graif, and Z. Li, “Crime rate inference with big data,” Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016. doi:10.1145/2939672.2939736
- K. Kelley, “What is data analysis?: Process, types, methods, and Techniques,” Simplilearn.com,https://www.simplilearn.com/data-analysis-methods-process-types-article (accessed March 17, 2023).
- H. Wang and S. Ma, “Preventing crimes against public health with artificial intelligence and machine learning capabilities,” Socio-Economic Planning Sciences, vol. 80, p. 101043, 2022. doi:10.1016/j.seps.2021.101043
- A. Ristea, M. Al Boni, B. Resch, M. S. Gerber, and M. Leitner, “Spatial Crime Distribution and prediction for sporting events using social media,” International Journal of Geographical Information Science, vol. 34, no. 9, pp. 1708–1739, 2020. doi:10.1080/13658816.2020.1719495
- M. Kocher and M. Leitner, “Forecasting of crime events applying risk terrain modeling,” GI_Forum, vol. 1, pp. 30–40, 2015. doi:10.1553/giscience2015s30
- J. R. Asor, “Implementation of predictive crime analytics in municipal crime management system in Calauan, Laguna, Philippines,” International Journal of Advanced Trends in Computer Science and Engineering, vol. 9, no. 1.3, pp. 150–157, 2020. doi:10.30534/ijatcse/2020/2291.32020
- F. R. Sumangil, “Crime rate down in Cotabato,” The Manila Times, https://www.manilatimes.net/2022/01/22/news/regions/crime-rate-down-in-cotabato/1830142 (accessed August. 03, 2022).
- E. Fernandez, “Security beefed up in NOCOT town due to rash of killings,” Philippine News Agency, https://www.pna.gov.ph/articles/1156349 (accessed Jun. 10, 2022).
- A. M. Tamayo, C. Chavez, and N. Nabe, “Crime and Inflation Rates in the Philippines: A Cointegration Analysis,” International Journal of Economics, Finance and Management, vol. 2, no. 5, pp. 380–385, Aug. 2013.
- X. Yan and X. Su, Linear Regression Analysis Theory and Computing. Singapore: World Scientific, 2009.
- M. R. Serrano, N. L. Hontiveros, E. R. Mosquera, R. L. Cariaga, and N. B. Catulong, “Dalan: A course recommender for freshmen students using a multiple regression model,” International Journal of Computer Science & Engineering Survey, vol. 13, no. 5/6, pp. 09–21, 2022. doi:10.5121/ijcses.2022.13602
- A. Abu, “Educational Data Mining & Students’ performance prediction,” International Journal of Advanced Computer Science and Applications, vol. 7, no. 5, pp. 212–220, 2016. doi:10.14569/ijacsa.2016.070531
- M. Tsiakmaki, G. Kostopoulos, S. Kotsiantis, and O. Ragos, “Implementing automl in educational data mining for prediction tasks,” Applied Sciences, vol. 10, no. 1, p. 90, 2019. doi:10.3390/app10010090
- Dr. S. V., “Data mining based prediction of demand in Indian market for refurbished electronics,” Journal of Soft Computing Paradigm, vol. 2, no. 2, pp. 101–110, 2020. doi:10.36548/jscp.2020.2.007
- R. Bellazzi and B. Zupan, “Predictive data mining in clinical medicine: Current issues and guidelines,” International Journal of Medical Informatics, vol. 77, no. 2, pp. 81–97, 2008. doi:10.1016/j.ijmedinf.2006.11.006
- M. Abdar, “Using Decision Trees in Data Mining for Predicting Factors Influencing Heart Disease,” Carpathian Journal of Electronic and Computer Engineering, vol. 8, no. 2, pp. 31–36, 2015.
- H. Entorf and H. Spengler, “Socioeconomic and demographic factors of crime in Germany,” International Review of Law and Economics, vol. 20, no. 1, pp. 75–106, 2000. doi:10.1016/s0144- 8188(00)00022-3
- H. E. Barbaree and W. L. Marshall, “Deviant sexual arousal, offense history, and demographic variables as predictors of reoffense among child molesters,” Behavioral Sciences & the Law, vol. 6, no. 2, pp. 267–280, 1988. doi:10.1002/bsl.2370060209
- J. A. Arthur, “Socioeconomic predictors of crime in rural Georgia,” Criminal Justice Review, vol. 16, no. 1, pp. 29–41, 1991. doi:10.1177/073401689101600106
- J. Q. Yuki, Md. M. Sakib, Z. Zamal, K. M. Habibullah, and A. K. Das, “Predicting crime using time and location data,” Proceedings of the 2019 7th International Conference on Computer and Communications Management, 2019. doi:10.1145/3348445.3348483
- M. Sharma, “Z - crime: A data mining tool for the detection of suspicious criminal activities based on decision tree,” 2014 International Conference on Data Mining and Intelligent Computing (ICDMIC), 2014. doi:10.1109/icdmic.2014.6954268
- B. Panja, P. Meharia, and K. Mannem, “Crime analysis mapping, intrusion detection - using data mining,” 2020 IEEE Technology & Engineering Management Conference (TEMSCON), 2020. doi:10.1109/temscon47658.2020.9140074
- “Philippine National Police,” eFOI - Electronic Freedom of Information, https://www.foi.gov.ph/requests/aglzfmVmb2ktcGhyHQsSB0NvbnRlbnQiEFBOUC04ODA1MDg wOTc2ODYM (accessed Jan. 11, 2022).
- “Philippines inflation rate 1960-2023,” MacroTrends, https://www.macrotrends.net/countries/PHL/philippines/inflation-rate-cpi (accessed Jan. 2022).