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Big Crime Data Analytics and Visualization

Published: 23 May 2022 Publication History

Abstract

Crime is one of the most permanent and troubling issues for societies and law enforcement agencies, and it costs dearly in several ways. In recent years, criminal justice agencies headed for Deep Learning, Data Mining, and Machine Learning (ML) to help in the crime-fighting process by taking advantage of historical crime data in order to identify and detect crime patterns and hotspots, predict crime occurrences in the future, and apprehend criminals and suspects. A major challenge facing researchers and law enforcement agencies is the rapid volume growth of crime data and how it can be analyzed accurately and efficiently. In this paper, state-of-the-art big crime data analysis and visualization have been done on two real crime datasets of two US cities, Chicago and San-Francisco. Some interesting and hidden patterns and correlations have been detected.

References

[1]
Suad A Alasadi and Wesam S Bhaya. 2017. Review of data preprocessing techniques in data mining. J. Eng. Appl. Sci. 12, 16 (2017), 4102–4107.
[2]
Emre Cihan ATEŞ, Gazi Erkan BOSTANCI, and M S G Serdar. 2020. Big Data, Data Mining, Machine Learning, and Deep Learning Concepts in Crime Data. J. Penal Law Criminol. 8, 2 (2020), 293–319.
[3]
Alkesh Bharati and Dr Sarvanaguru RA. 2018. Crime Prediction and Analysis Using Machine Learning. Int. Res. J. Eng. Technol. 5, 09 (2018).
[4]
Paul Butke and Scott C Sheridan. 2010. An analysis of the relationship between weather and aggressive crime in Cleveland, Ohio. Weather. Clim. Soc. 2, 2 (2010), 127–139.
[5]
Pablo F Cabrera-Barona, Gualdemar Jimenez, and Pablo Melo. 2019. Types of crime, poverty, population density and presence of police in the metropolitan district of Quito. ISPRS Int. J. Geo-Information 8, 12 (2019), 558.
[6]
Janet Chan and Lyria Bennett Moses. 2016. Is big data challenging criminology? Theor. Criminol. 20, 1 (2016), 21–39.
[7]
Jongmook Choe. 2008. Income inequality and crime in the United States. Econ. Lett. 101, 1 (2008), 31–33.
[8]
Mingchen Feng, Jiangbin Zheng, Jinchang Ren, Amir Hussain, Xiuxiu Li, Yue Xi, and Qiaoyuan Liu. 2019. Big data analytics and mining for effective visualization and trends forecasting of crime data. IEEE Access 7, (2019), 106111–106123.
[9]
Lawrence J Fennelly. 2012. Handbook of loss prevention and crime prevention. Elsevier.
[10]
Gaurav Hajela, Meenu Chawla, and Akhtar Rasool. 2020. A Clustering Based Hotspot Identification Approach For Crime Prediction. Procedia Comput. Sci. 167, (2020), 1462–1470.
[11]
Suhong Kim, Param Joshi, Parminder Singh Kalsi, and Pooya Taheri. 2018. Crime analysis through machine learning. In 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), IEEE, 415–420.
[12]
Akash Kumar, Aniket Verma, Gandhali Shinde, Yash Sukhdeve, and Nidhi Lal. 2020. Crime Prediction Using K-Nearest Neighboring Algorithm. In 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), IEEE, 1–4.
[13]
Lance Lochner. 2020. Education and crime. In The Economics of Education. Elsevier, 109–117.
[14]
Wes McKinney. 2011. pandas: a foundational Python library for data analysis and statistics. Python High Perform. Sci. Comput. 14, 9 (2011), 1–9.
[15]
Jerry H Ratcliffe. 2004. The hotspot matrix: A framework for the spatio‐temporal targeting of crime reduction. Police Pract. Res. 5, 1 (2004), 5–23.
[16]
Kristina P Sinaga and Miin-Shen Yang. 2020. Unsupervised K-means clustering algorithm. IEEE Access 8, (2020), 80716–80727.
[17]
Prajakta Yerpude. 2020. Predictive Modelling of Crime Data Set Using Data Mining. Int. J. Data Min. Knowl. Manag. Process Vol 7, (2020).
[18]
Jesia Quader Yuki, Md Mahfil Quader Sakib, Zaisha Zamal, Khan Mohammad Habibullah, and Amit Kumar Das. 2019. Predicting crime using time and location data. In Proceedings of the 2019 7th International Conference on Computer and Communications Management, 124–128.
[19]
Binbin Zhou, Longbiao Chen, Sha Zhao, Fangxun Zhou, Shijian Li, and Gang Pan. 2021. Spatio-temporal analysis of urban crime leveraging multisource crowdsensed data. Pers. Ubiquitous Comput. (2021), 1–14.
[20]
Lina Zhou, Shimei Pan, Jianwu Wang, and Athanasios V Vasilakos. 2017. Machine learning on big data: Opportunities and challenges. Neurocomputing 237, (2017), 350–361.
[21]
CHICAGO DATA PORTAL. Retrieved from https://data.cityofchicago.org/Public-Safety/Crimes-2001-to-Present/ijzp-q8t2
[22]
DataSF. Retrieved from https://data.sfgov.org/Public-Safety/Police-Department-Incident-Reports-Historical-2003/tmnf-yvry

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ICCDA '22: Proceedings of the 2022 6th International Conference on Compute and Data Analysis
February 2022
131 pages
ISBN:9781450395472
DOI:10.1145/3523089
This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 May 2022

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  1. Big crime data
  2. Data analytics
  3. Exploratory data analysis

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