Crimes are common social problems that can even affect the quality of life, even the economic growth of a country. Big Data Analytics (BDA) is used for analyzing and identifying different crime patterns, their relations, and the trends within a large amount of crime data. Here, BDA is applied to criminal data in which, data analysis is conducted for the purpose of visualization. Big data analytics and visualization techniques were utilized to analyze crime big data within the different parts of India. Here, we have taken all the states of Indian for analysis, visualization and prediction. The series of operations performed are data collection, data pre-processing, visualization and trends prediction, in which LSTM model is used. The data includes different cases of crimes with in different years and the crimes such as crime against women and children in which, kidnap, murder, rape. The predictive results show that the LSTM perform better than neural network models. Hence, the generated outcomes will benefit for police and law enforcement organizations to clearly understand crime issues and that will help them to track activities, predict the similar incidents, and optimize the decision making process.
| Published in | International Journal of Data Science and Analysis (Volume 7, Issue 3) |
| DOI | 10.11648/j.ijdsa.20210703.11 |
| Page(s) | 51-59 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Big Data Analytics, Crime Data, Crime Data Analysis, Visualization, Prediction, LSTM
| [1] | Mingchen Feng, Jiangbin Zheng, Jinchang Ren, Amir Hussain, Xiuxiu Li, Yue Xi, and Qiao yuan Liu, “Big Data Analytics and Mining for Effective Visualization and Trends Forecasting of CrimeData”, IEEE Transactions on Creative CommonsAttribution 2019. |
| [2] | A. Gandomi and M. Haider, “Beyond the hype: Big data concepts, methods, and analytics,” Int. J. Inf. Manage., vol. 35, no. 2, pp. 137–144, Apr. 2015. |
| [3] | Z. Jia, C. Shen, Y. Chen, T. Yu, X. Guan, and X. Yi, “Big-data analysis of multi-source logs for anomaly detection on network-based system,” in Proc. 13th IEEE Conf. Autom. Sci. Eng. (CASE), Xi’an, China, Aug. 2017, pp. 1136–1141. |
| [4] | M. Huda, A. Maseleno, M. Siregar, R. Ahmad, K. A. Jasmi, N. H. N. Muhamad, and P. Atmotiyoso, “Big data emerging technology: Insights into innovative environment for online learning resources,” Int. J. Emerg. Technol. Learn., vol. 13, no. 1, pp. 23–36, Jan. 2018. |
| [5] | J. Zakir and T. Seymour, “Big data analytics,” Issues Inf. Syst., vol. 16, no. 2, pp. 81–90, 2015. |
| [6] | Y. Wang, L. Kung, W. Y. C. Wang, and C. G. Cegielski, “An integrated big data analytics-enabled transformation model: Application to health care,” Inf. Manage., vol. 55, no. 1, pp. 64–79, Jan. 2018. |
| [7] | W. Raghupathi and V. Raghupathi, “Big data analytics in healthcare: Promise and potential,” Health Inf. Sci. Syst., vol. 2, no. 1, pp. 1–10, Feb. 2014. |
| [8] | J. Archenaa and E. A. M. Anita, “A survey of big data analytics in healthcare and government,” Procedia Comput. Sci., vol. 50, pp. 408–413, Apr. 2015. |
| [9] | A. Londhe and P. Rao, “Platforms for big data analytics: Trend towards hybrid era,” in Proc. Int. Conf. Energy, Commun., Data Anal. Soft Comput. (ICECDS), Chennai, 2017, pp. 3235–3238. |
| [10] | W. Grady, H. Parker, and A. Payne, “Agile big data analytics: AnalyticsOps for data science,” in Proc. IEEE Int. Conf. Big Data, Boston, MA, USA, Dec. 2017, pp. 2331–2339. |
| [11] | R. Vatrapu, R. R. Mukkamala, A. Hussain, and B. Flesch, “Social set analysis: A set theoretical approach to Big Data analytics,” IEEE Access, vol. 4, pp. 2542–2571, 2016. |
| [12] | Y. Zhang, S. Ren, Y. Liu, and S. Si, “A big data analytics architecture for cleaner manufacturing and maintenance processes of complex products,” J. Cleaner Prod., vol. 142, no. 2, pp. 626–641, Jan. 2017. |
| [13] | E. W. Ngai, A. Gunasekaran, S. F. Wamba, S. Akter, and R. Dubey, “Big data analytics in electronic markets,” Electron. Markets, vol. 27, no. 3, pp. 243–245, Aug. 2017. |
| [14] | Y.-Y. Liu, F.-M. Tseng, and Y.-H. Tseng, “Big Data analytics for forecasting tourism destination arrivals with the applied Vector Autoregression model,” Technol. Forecasting Social Change, vol. 130, pp. 123–134, May 2018. |
| [15] | D. Fisher, M. Czerwinski, S. Drucker, and R. DeLine, “Interactions with big data analytics,” Interactions, vol. 19, no. 3, pp. 50–59, Jun. 2012. |
| [16] | S. Musa, “Smart cities — A road map for development,” IEEE Potentials, vol. 37, no. 2, pp. 19–23, Mar./Apr. 2018. |
| [17] | S. Yadav, A. Yadav, R. Vishwakarma, N. Yadav and M. Timbadia, Crime pattern detection, analysis prediction,” in Proc. IEEE Int. Conf. Electron., Commun. Aerosp. Technol., Coimbatore, India, Apr. 2017, pp. 225–230. |
| [18] | N. Baloian, C. E. Bassaletti, M. Fernandez, O. Figueroa, P. Fuentes, R. Manasevich, ´ M. Orchard, S. Penafiel, J. A. Pino, and M. Vergara, “Crime prediction using patterns ˜and context,” in Proc. 21st IEEE Int. Conf. Comput. Supported Cooperat. Work Design, Wellington, New Zealand, Apr. 2017, pp. 2–9. |
| [19] | X. Zhao and J. Tang, ”Exploring transfer learning for crime prediction,”in Proc. IEEE Int. Conf. Data Mining Workshops, New Orleans, LA, USA, Nov. 2017, pp. 1158–1159. |
| [20] | S. Wu, J. Male, and E. Dragut, “Spatial-temporal campus crime pattern mining from, historical alert messages,” in Proc. Int. Conf. Comput., Netw. Commun., Santa Clara, CA, USA, 2017, pp. 778–782. |
| [21] | K. R. S. Vineeth, T. Pradhan, and A. Pandey, “A novel approach for intelligent crime pattern discovery and prediction,” in Proc. Int. Conf. Adv. Commun. Control Comput. Technol., Ramanathapuram, India, 2016, pp. 531–538. |
| [22] | C. R. Rodr´ıguez, D. M. Gomez, and M. A. M. Rey, “Forecasting time series from clustering by a memetic differential fuzzy approach: An application to crime prediction,” in Proc. IEEE Symp. Ser. Comput. Intell., Honolulu, HI, USA, Nov./Dec. 2017, pp. 1–8. |
| [23] | A. Joshi, A. S. Sabitha, and T. Choudhury, “Crime analysis using K-means clustering,” in Proc. 3rd Int. Conf. Comput. Intell. Netw., Odisha, India, 2017, pp. 33–39. |
| [24] | N. M. M. Noor, W. M. F. W. Nawawi, and A. F. Ghazali, “Supporting decision making in situational crime prevention using fuzzy association rule,” in Proc. Int. Conf. Comput., Control, Informat. Appl. (IC3INA), Jakarta, Indonesia, 2013, pp. 225–229. |
| [25] | M. Injadat, F. Salo, and A. B. Nassif, “Data mining techniques in social media: A survey,” Neurocomputing, vol. 214, pp. 654–670, Nov. 2016. |
| [26] | Z. Zhao, S. Tu, J. Shi, and R. Rao, “Time-weighted LSTM model with redefined labeling for stock trend prediction,” in Proc. IEEE 29th Int. Conf. Tools Artif. Intell. (ICTAI, Boston, MA, USA, Nov. 2017, pp. 1210–1217. |
| [27] | J. Dai, G. Sheng, X. Jiang, and H. Song, “LSTM networks for the trend prediction of gases dissolved in power transformer insulation oil,” in Proc. 12th Int. Conf. Properties Appl. Dielectr. Mater., Xi’an, China, 2018, pp. 666–669. |
| [28] | H. Kashef, M. Abdel-Nasser, and K. Mahmoud, “Power loss estimation in smart grids using a neural network model,” in Proc. Int. Conf. Innov. Trends Comput. Eng. (ITCE), Aswan, Egypt, 2018, pp. 258–263. |
| [29] | J. Peral, A. Ferrandez, D. Gil, E. Kauffmann, and H. Mora, “A review of the analytics ´ techniques for an efficient management of online forums: An architecture proposal,” IEEE Access, vol. 7, pp. 12220–12240, 2019. |
APA Style
Mufeeda Manengadan, Silpa Nandanan, Neethu Subash. (2021). Crime Data Analysis, Visualization and Prediction Using LSTM. International Journal of Data Science and Analysis, 7(3), 51-59. https://doi.org/10.11648/j.ijdsa.20210703.11
ACS Style
Mufeeda Manengadan; Silpa Nandanan; Neethu Subash. Crime Data Analysis, Visualization and Prediction Using LSTM. Int. J. Data Sci. Anal. 2021, 7(3), 51-59. doi: 10.11648/j.ijdsa.20210703.11
AMA Style
Mufeeda Manengadan, Silpa Nandanan, Neethu Subash. Crime Data Analysis, Visualization and Prediction Using LSTM. Int J Data Sci Anal. 2021;7(3):51-59. doi: 10.11648/j.ijdsa.20210703.11
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Download@article{10.11648/j.ijdsa.20210703.11,
author = {Mufeeda Manengadan and Silpa Nandanan and Neethu Subash},
title = {Crime Data Analysis, Visualization and Prediction Using LSTM},
journal = {International Journal of Data Science and Analysis},
volume = {7},
number = {3},
pages = {51-59},
doi = {10.11648/j.ijdsa.20210703.11},
url = {https://doi.org/10.11648/j.ijdsa.20210703.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20210703.11},
abstract = {Crimes are common social problems that can even affect the quality of life, even the economic growth of a country. Big Data Analytics (BDA) is used for analyzing and identifying different crime patterns, their relations, and the trends within a large amount of crime data. Here, BDA is applied to criminal data in which, data analysis is conducted for the purpose of visualization. Big data analytics and visualization techniques were utilized to analyze crime big data within the different parts of India. Here, we have taken all the states of Indian for analysis, visualization and prediction. The series of operations performed are data collection, data pre-processing, visualization and trends prediction, in which LSTM model is used. The data includes different cases of crimes with in different years and the crimes such as crime against women and children in which, kidnap, murder, rape. The predictive results show that the LSTM perform better than neural network models. Hence, the generated outcomes will benefit for police and law enforcement organizations to clearly understand crime issues and that will help them to track activities, predict the similar incidents, and optimize the decision making process.},
year = {2021}
}
TY - JOUR T1 - Crime Data Analysis, Visualization and Prediction Using LSTM AU - Mufeeda Manengadan AU - Silpa Nandanan AU - Neethu Subash Y1 - 2021/05/08 PY - 2021 N1 - https://doi.org/10.11648/j.ijdsa.20210703.11 DO - 10.11648/j.ijdsa.20210703.11 T2 - International Journal of Data Science and Analysis JF - International Journal of Data Science and Analysis JO - International Journal of Data Science and Analysis SP - 51 EP - 59 PB - Science Publishing Group SN - 2575-1891 UR - https://doi.org/10.11648/j.ijdsa.20210703.11 AB - Crimes are common social problems that can even affect the quality of life, even the economic growth of a country. Big Data Analytics (BDA) is used for analyzing and identifying different crime patterns, their relations, and the trends within a large amount of crime data. Here, BDA is applied to criminal data in which, data analysis is conducted for the purpose of visualization. Big data analytics and visualization techniques were utilized to analyze crime big data within the different parts of India. Here, we have taken all the states of Indian for analysis, visualization and prediction. The series of operations performed are data collection, data pre-processing, visualization and trends prediction, in which LSTM model is used. The data includes different cases of crimes with in different years and the crimes such as crime against women and children in which, kidnap, murder, rape. The predictive results show that the LSTM perform better than neural network models. Hence, the generated outcomes will benefit for police and law enforcement organizations to clearly understand crime issues and that will help them to track activities, predict the similar incidents, and optimize the decision making process. VL - 7 IS - 3 ER -
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