Abstract Number:
3239
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Speed
Participants:
Haiyan Wang (1), Erick Jiang (1), Ziyao Cui (1), Nicholas Sortisio (1), Cynthia Rudin (1), Eric Chen (1)
Institutions:
(1) Duke University, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
Recently, there has been a trend towards integrating advanced technologies into law enforcement strategies, which has dramatically improved police departments' efficacies. Our study aims to build upon this trend, using machine learning to analyze national and local datasets in hopes of exploring factors that influence public perceptions of crime, attitudes towards law enforcement and the criminal justice system, and average crime rates. On the national scale, we intend to use datasets obtained from the FBI's Crime Data Explorer, the General Social Survey, and polling results from sources including Gallup and the Pew Research Center to explore the relationship between variables of interest and crime. Then, we will focus on several large cities with high crime rates, using geospatial crime data to begin trying to determine key factors in crime rates and perceived crime. We hope that our analysis will shed light onto previously unconsidered variables, such as the presence of public parks. Our findings will provide insights that researchers and policymakers can utilize to create informed public safety legislation.
Keywords:
| | | | |
Sponsors:
Section on Statistical Computing
Tracks:
Machine Learning Algorithms
Can this be considered for alternate subtype?
Yes
Are you interested in volunteering to serve as a session chair?
No
I have read and understand that JSM participants must abide by the Participant Guidelines.
Yes
I understand that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is non-refundable.
I understand