Abstract:
General Background: Crime data analysis plays a vital role in enhancing public safety, particularly in densely populated urban areas such as Chicago. Specific Background: The increasing complexity of socio-economic environments necessitates scalable tools for real-time data handling and visualization. MongoDB, a NoSQL database, offers advantages in managing large unstructured datasets for dynamic web applications. Knowledge Gap: Despite comparative studies between NoSQL and relational databases, there remains a lack of practical implementations integrating real-time visualization of crime data via web interfaces. Aims: This study aims to design and develop a prototype website utilizing MongoDB and PyMongo to manage and visualize Chicago crime data from 2001 to the present. Results: The system supports seven query operations, including insert, update, delete, and statistical queries by year and arrest status, optimized through indexing on a 6-million-record dataset. It enables CRUD operations and presents interactive visualizations such as bar and stacked charts. Novelty: Unlike previous works, this research integrates a full-stack solution combining efficient NoSQL querying with user-friendly visual analytics in a single platform. Implications: The prototype can be adapted for broader urban analytics applications, including demographic tracking and population census, offering a scalable framework for real-time data management and decision-making.
Highlights:
Full-stack crime data system using MongoDB and PyMongo
Efficient queries with indexing on large datasets
Interactive visualizations for real-time urban insights
Keywords: Crime data visualization, MongoDB, NoSQL database, urban analytics, real-time web application
References
K. Santhiya and V. Bhuvaneswari, "An Automated MapReduce Framework for Crime Classification of News Articles Using MongoDB," Int. J. Appl. Eng. Res., vol. 13, no. 1, pp. 131–136, 2018.
V. Jain, A. K. Dubey, A. Jain, M. Malhotra, and S. Rastogi, "Crime Pattern Recognition in Chicago City Using Hadoop Multinode Cluster," J. Inf. Optim. Sci., vol. 40, no. 2, pp. 587–601, 2019.
N. Baviskar, Smart City Development Using Data Analytics, Doctoral dissertation, California State Univ., Sacramento, 2017. [Online]. Available: https://spring.io/understanding/NoSQL
K. Banker, D. Garrett, P. Bakkum, and S. Verch, MongoDB in Action: Covers MongoDB Version 3.0. New York, NY, USA: Simon and Schuster, 2016.
N. E. Stone, Social Media Canvassing Using Twitter and Web GIS to Aid in Solving Crime, Master’s thesis, Univ. of Southern California, 2017.
G. Chang, G. Juhasz, and L. Stephan, "Creating Your Personal Website," Mac Edition, 2006. [Online]. Available: http://andrew.cmu.edu/70-271-htmlman
Webware Staff, "11 Steps to Create a Successful Website," StartupNation/CNET, 2007.
M. Garcia, "Creating a Webpage Using HTML & CSS," ULN Internship Program, PCL Media Lab, 2015.
S. Kanoje, V. Powar, and D. Mukhopadhyay, "Using MongoDB for Social Networking Website," in Proc. IEEE 2nd Int. Conf. Innovations Inf. Embedded Commun. Syst. (ICIIECS), 2015.
W.-P. Zhu, M.-X. Li, and H. Chen, "Using MongoDB to Implement Textbook Management System Instead of MySQL," in Proc. 2011 IEEE 3rd Int. Conf. Commun. Softw. Netw., pp. 497–501, 2011.
D. D. B. Dipina, S. Salim, and S. M. Vargese, "MongoDB vs. MySQL: A Comparative Study of Performance in Super Market Management System," Int. J. Comput. Sci. Inf. Technol. (IJCSITY), vol. 4, no. 2, pp. 1–10, 2016.
C. Gyorodi, R. Gyorodi, G. Pecherle, and A. Olah, "A Comparative Study: MongoDB vs. MySQL," in Proc. 2015 13th Int. Conf. Eng. Mod. Electr. Syst. (EMES), pp. 89–94, 2015.
S. Soni, M. Ambavane, S. Ambre, and S. Maitra, "A Comparative Study: MongoDB vs. MySQL," Int. J. Sci. Eng. Res., vol. 8, no. 5, pp. 1701–1705, 2017.
S. M. Kiio, Apache Spark Based Big Data Analytics for Social Network Cybercrime Forensics, Doctoral dissertation, Univ. of Nairobi, 2017.
A. A. Shaker, N. Mandela, and A. K. Agrawal, "Review on Analyzing and Detecting Crimes," in Proc. Int. Conf. Commun. Netw. Comput., Cham, Switzerland: Springer Nature, pp. 116–127, Dec. 2022.