Document Type
Student Coursework
Publication Date
Spring 3-1-2025
Abstract
This paper presents the design and implementation of a scalable data pipeline and interactive dashboards for analyzing crime patterns in Los Angeles using data from the Los Angeles Police Department (LAPD). The pipeline was built on Amazon Web Services (AWS), where data was ingested through Python scripts, stored in Amazon S3, processed with AWS Glue, and loaded into Amazon Redshift. Tableau was integrated for real-time visualization, enabling exploration of geographic, temporal, and demographic trends. Analysis revealed that the Central, 77th Street, and Pacific divisions consistently report the highest crime rates, with vehicle theft, battery/simple assault, and burglary as the most frequent offenses. Crimes peak in the afternoon and on Fridays, while victims are most commonly between 30 and 40 years old. The system provides residents and law enforcement with actionable insights, supporting data-driven decision-making, efficient resource allocation, and community awareness for improved public safety.
Program or Discipline Name
Computer and Information Sciences
Recommended Citation
Cha, S., Patra, D., Jiang, L., Manvi, S., Shi, W., & Chao, Y. (2025). A Scalable Architecture for Visualizing and Analyzing Crime Data in LA. A Scalable Architecture for Visualizing and Analyzing Crime Data in LA, 1-5. Retrieved from https://digitalcommons.harrisburgu.edu/other-works/17
Publication Title
A Scalable Architecture for Visualizing and Analyzing Crime Data in LA
Start Page No.
1
End Page No.
5
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.