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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.00640 |
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| _version_ | 1866909302348840960 |
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| author | Dao, Patricia Sappa, Jashmitha Terala, Saanvi Wong, Tyson Lam, Michael Zhu, Kevin |
| author_facet | Dao, Patricia Sappa, Jashmitha Terala, Saanvi Wong, Tyson Lam, Michael Zhu, Kevin |
| contents | Traditional crime prediction techniques are slow and inefficient when generating predictions as crime increases rapidly \cite{r15}. To enhance traditional crime prediction methods, a Long Short-Term Memory and Gated Recurrent Unit model was constructed using datasets involving gender ratios, high school graduation rates, political status, unemployment rates, and median income by state over multiple years. While there may be other crime prediction tools, personalizing the model with hand picked factors allows a unique gap for the project. Producing an effective model would allow policymakers to strategically allocate specific resources and legislation in geographic areas that are impacted by crime, contributing to the criminal justice field of research \cite{r2A}. The model has an average total loss value of 70.792.30, and a average percent error of 9.74 percent, however both of these values are impacted by extreme outliers and with the correct optimization may be corrected. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_00640 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Time-series Crime Prediction Across the United States Based on Socioeconomic and Political Factors Dao, Patricia Sappa, Jashmitha Terala, Saanvi Wong, Tyson Lam, Michael Zhu, Kevin Machine Learning Traditional crime prediction techniques are slow and inefficient when generating predictions as crime increases rapidly \cite{r15}. To enhance traditional crime prediction methods, a Long Short-Term Memory and Gated Recurrent Unit model was constructed using datasets involving gender ratios, high school graduation rates, political status, unemployment rates, and median income by state over multiple years. While there may be other crime prediction tools, personalizing the model with hand picked factors allows a unique gap for the project. Producing an effective model would allow policymakers to strategically allocate specific resources and legislation in geographic areas that are impacted by crime, contributing to the criminal justice field of research \cite{r2A}. The model has an average total loss value of 70.792.30, and a average percent error of 9.74 percent, however both of these values are impacted by extreme outliers and with the correct optimization may be corrected. |
| title | Time-series Crime Prediction Across the United States Based on Socioeconomic and Political Factors |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2409.00640 |