Program Type
Undergraduate
Faculty Advisor
Dr. Robin Ghosh
Document Type
Presentation
Location
Face-to-face
Start Date
25-4-2023 3:55 PM
Abstract
Crime is a severe problem in the city of Little Rock, Arkansas. In this study, we aim to develop a machine-learning model to predict criminal activities in the city and provide insights into crime patterns. We will analyze publicly available crime datasets from Little Rock Police Department from January 2017 to March 2023 to identify trends and patterns in crime occurrence. We used data cleaning and exploratory data analysis techniques, such as figured-based visualizations, to prepare the data for machine learning. We will employ the Neural Prophet, a time-series machine learning model, to predict daily crime counts. The model will train data from January 2017 to November 2021 and be tested on data from November 2021 to March 2023. The model will be evaluated using Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Mean Absolute Error (MAE), and R2 score with results that represent a model that can accurately forecast the crime occurrence in Little Rock. The research associated with this study was funded by the Undergraduate Research Scholarship Award provided by Arkansas Tech University to eligible undergraduate students pursuing research.
This study will contribute the following:
- Cleaning and analyzing publicly available crime data to identify trends and patterns.
- Developing a Time Series machine learning model to predict daily crime occurrence.
- Evaluating a model’s performance using standard metrics to determine promising results.
This study’s findings can be used to inform law enforcement agencies, policymakers, other research groups, and third parties, such as business owners, to develop effective crime prevention and detection strategies.
Recommended Citation
Sabakhtarishvili, Zurab; Panday, Sijan; and Jensen, Clayton, "Crime Prediction Using Machine Learning: The Case of the City of Little Rock" (2023). ATU Research Symposium. 74.
https://orc.library.atu.edu/atu_rs/2023/2023/74
Included in
Crime Prediction Using Machine Learning: The Case of the City of Little Rock
Face-to-face
Crime is a severe problem in the city of Little Rock, Arkansas. In this study, we aim to develop a machine-learning model to predict criminal activities in the city and provide insights into crime patterns. We will analyze publicly available crime datasets from Little Rock Police Department from January 2017 to March 2023 to identify trends and patterns in crime occurrence. We used data cleaning and exploratory data analysis techniques, such as figured-based visualizations, to prepare the data for machine learning. We will employ the Neural Prophet, a time-series machine learning model, to predict daily crime counts. The model will train data from January 2017 to November 2021 and be tested on data from November 2021 to March 2023. The model will be evaluated using Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Mean Absolute Error (MAE), and R2 score with results that represent a model that can accurately forecast the crime occurrence in Little Rock. The research associated with this study was funded by the Undergraduate Research Scholarship Award provided by Arkansas Tech University to eligible undergraduate students pursuing research.
This study will contribute the following:
- Cleaning and analyzing publicly available crime data to identify trends and patterns.
- Developing a Time Series machine learning model to predict daily crime occurrence.
- Evaluating a model’s performance using standard metrics to determine promising results.
This study’s findings can be used to inform law enforcement agencies, policymakers, other research groups, and third parties, such as business owners, to develop effective crime prevention and detection strategies.