用社会经济政治数据预测美国犯罪趋势，助力政策精准施策。
Time-series Crime Prediction Across the United States Based on Socioeconomic and Political Factors
- 融合性别比、失业率等多维因素，构建时序预测模型
- 平均误差9.74%，总损失70.79，受极端值影响待优化
- 适合关注公共安全与政策制定的研究者参考
传统犯罪预测方法在犯罪快速上升时效率低下。为改进这一问题，本文基于各州多年来的性别比、高中毕业率、政治状况、失业率及中位收入等数据，构建了结合长短期记忆网络（LSTM）与门控循环单元（GRU）的时序预测模型。尽管存在其他犯罪预测工具，但通过人工筛选关键因素，使本模型具备独特性。该模型平均总损失为70.792.30，平均百分比误差为9.74%，但两项指标均受极端异常值影响，经适当优化后有望改善。有效模型可帮助决策者在高犯罪地区科学配置资源与立法，推动刑事司法研究发展。
原文摘要 · Abstract (English)
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.
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