Course: IDS-702 - Modelling and Representation of Data
Term: Fall 2023
Team: 13
Members: Revanth Chowdary Ganga(rg361), Titus Robin (tra29), Meixiang Du (md480), Suim Park (sp699)
This project delves into the intricate dynamics of crime in Los Angeles, employing an extensive dataset from the
Los Angeles Police Department (LAPD), covering incidents from 2020 to present day. Utilizing Logistic
and Poisson
regression models, our research aims to unearth the pivotal factors influencing the seriousness of crime commited
and predict the number crime occurrences in a given area and time respectively. Key findings from the first research
objective indicate a relationship between factors like victim demorgraphic and time of day on the seriousness of crime
committed. Findings from the second research objective indicate significant temporal and spatial variations in crime
rates, offering valuable insights for law enforcement and public safety strategies. This study aims to not only enhances the
understanding of crime patterns but also aids in resource allocation and preventive measures.
- The crime data used for this analysis is sourced from Los Angeles Open Data, a platform that provides access to the city's public data. The Los Angeles Crime Data is updated on a weekly basis and for this analysis, the data as of
13-Oct-2023
was used. - The geolocations of the police precincts were obtained from online searchs and the file is available in the
data
folder.
All the codes used during the different stages of this project are available in the code
folder.
The 'Final Report' code file contains all the codes required for running the analysis end to end (including EDA) and generating the PDF report.
Note: Additional codes and temporary code files are present in the 'Other' subfolder.
The reports submitted at different stages of the project are available in the report
folder. along with the reports, the presentation deck used is also available in the same folder.