Creating a Website for Chicago City’s Crimes Using MongoDB

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