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| Format: | Preprint |
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2024
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| Online Access: | https://arxiv.org/abs/2410.00394 |
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| _version_ | 1866913622378151936 |
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| author | Dai, Wei Zhang, Rui Kafle, Diya |
| author_facet | Dai, Wei Zhang, Rui Kafle, Diya |
| contents | Public safety is vital to every country, especially school safety. In the United States, students and educators are concerned about school shootings. There are critical needs to understand the patterns of school shootings. Without this understanding, we cannot take action to prevent school shootings. Existing research that includes statistical analysis usually focuses on public mass shootings or just shooting incidents that have occurred in the past and there are hardly any articles focusing on mass school shootings. Here we firstly define mathematic models through gam theory. Then, we evaluate shootings events in schools for recently 26-year (1999-2024). Compared with the number of mass school shootings in COVID-19 period, we predict the number of mass school shooting events in the US will be reduced through four machine learning models. We also identify that mass school shootings usually take average 31 minutes with four periods. The annual probability of mass school shootings is 1.23 E-5 (or one in 81,604) per school. The shootings mostly occur inside buildings, especially classrooms and hallways. By interpreting these data and conducting various statistical analysis, this will ultimately help the law enforcement and schools to reduce the future school shootings. The research data sets could be downloaded via the website: https://publicsafetyinfo.com |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_00394 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Analyzing Mass School Shootings in the United States from 1999 to 2024 with Game Theory, Probability Analysis, and Machine Learning Dai, Wei Zhang, Rui Kafle, Diya Computers and Society Public safety is vital to every country, especially school safety. In the United States, students and educators are concerned about school shootings. There are critical needs to understand the patterns of school shootings. Without this understanding, we cannot take action to prevent school shootings. Existing research that includes statistical analysis usually focuses on public mass shootings or just shooting incidents that have occurred in the past and there are hardly any articles focusing on mass school shootings. Here we firstly define mathematic models through gam theory. Then, we evaluate shootings events in schools for recently 26-year (1999-2024). Compared with the number of mass school shootings in COVID-19 period, we predict the number of mass school shooting events in the US will be reduced through four machine learning models. We also identify that mass school shootings usually take average 31 minutes with four periods. The annual probability of mass school shootings is 1.23 E-5 (or one in 81,604) per school. The shootings mostly occur inside buildings, especially classrooms and hallways. By interpreting these data and conducting various statistical analysis, this will ultimately help the law enforcement and schools to reduce the future school shootings. The research data sets could be downloaded via the website: https://publicsafetyinfo.com |
| title | Analyzing Mass School Shootings in the United States from 1999 to 2024 with Game Theory, Probability Analysis, and Machine Learning |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2410.00394 |