Summary of Time-series Crime Prediction Across the United States Based on Socioeconomic and Political Factors, by Patricia Dao et al.
Time-series Crime Prediction Across the United States Based on Socioeconomic and Political Factors
by Patricia Dao, Jashmitha Sappa, Saanvi Terala, Tyson Wong, Michael Lam, Kevin Zhu
First submitted to arxiv on: 1 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed crime prediction model combines Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures to improve traditional methods. By incorporating datasets featuring gender ratios, high school graduation rates, political status, unemployment rates, and median income by state over multiple years, the personalized model aims to strategically allocate resources and legislation in crime-impacted areas. The model’s performance is measured using average total loss value (70.792.30) and average percent error (9.74%), although these values are influenced by extreme outliers that can be corrected with optimization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to predict crimes is being developed, which uses a special kind of artificial intelligence called Long Short-Term Memory and Gated Recurrent Unit. This model looks at data about things like the number of girls in school, how many people have jobs, and how much money people make to try to guess where crimes will happen. The idea is that by using this information, police can target specific areas with more resources and laws to reduce crime. The model’s predictions are somewhat accurate, but there are some outliers that need to be fixed. |
Keywords
* Artificial intelligence * Lstm * Optimization
