# Time-series Crime Prediction Across the United States Based on Socioeconomic and Political Factors

arXiv: https://arxiv.org/abs/2409.00640 · HTML: https://mlsanity.com/paper/2409.00640/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 19 references that are in the map.
- First posted on arXiv 1 Sep 2024.

## Builds on (most cited first, 1 of 1)

- [More crime in cities? On the scaling laws of crime and the inadequacy of per capita rankings -- a cross-country study](https://mlsanity.com/paper/2012.15368/) (2020)

## Abstract (arXiv)

Traditional crime prediction techniques are slow and inefficient when generating predictions as crime increases rapidly citer15. 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 citer2A. 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.