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Crime Data Analysis, Visualization and Prediction Using LSTM

Received: 25 January 2021     Accepted: 19 April 2021     Published: 8 May 2021
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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.

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), 2021. Published by Science Publishing Group

Keywords

Big Data Analytics, Crime Data, Crime Data Analysis, Visualization, Prediction, LSTM

References
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Cite This Article
  • 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

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    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

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    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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  • @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}
    }
    

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  • 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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Author Information
  • Department of Computer Science and Engineering, APJ Abdul Kalam Technological University, Kerala, India

  • Department of Computer Science and Engineering, APJ Abdul Kalam Technological University, Kerala, India

  • Department of Computer Science and Engineering, APJ Abdul Kalam Technological University, Kerala, India

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