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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Accesso online: | https://arxiv.org/abs/2506.04240 |
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| _version_ | 1866913876742766592 |
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| author | Anuyah, Sydney |
| author_facet | Anuyah, Sydney |
| contents | In this short article, I leverage the National Crime Victimization Survey from 1992 to 2022 to examine how income, education, employment, and key demographic factors shape the type of crime victims experience (violent vs property). Using balanced classification splits and logistic regression models evaluated by F1-score, there is an isolation of the socioeconomic drivers of victimization "Group A" models and then an introduction of demographic factors such as age, gender, race, and marital status controls called "Group B" models. The results consistently proves that higher income and education lower the odds of violent relative to property crime, while men younger individuals and racial minorities face disproportionately higher violentcrime risks. On the geographic spectrum, the suburban models achieve the strongest predictive performance with an accuracy of 0.607 and F1 of 0.590, urban areas benefit from adding education and employment predictors and crime in rural areas are still unpredictable using these current factors. The patterns found in this study shows the need for specific interventions like educational investments in metropolitan settings economic support in rural communities and demographicaware prevention strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_04240 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | What does making money have to do with crime?: A dive into the National Crime Victimization survey Anuyah, Sydney Physics and Society Machine Learning In this short article, I leverage the National Crime Victimization Survey from 1992 to 2022 to examine how income, education, employment, and key demographic factors shape the type of crime victims experience (violent vs property). Using balanced classification splits and logistic regression models evaluated by F1-score, there is an isolation of the socioeconomic drivers of victimization "Group A" models and then an introduction of demographic factors such as age, gender, race, and marital status controls called "Group B" models. The results consistently proves that higher income and education lower the odds of violent relative to property crime, while men younger individuals and racial minorities face disproportionately higher violentcrime risks. On the geographic spectrum, the suburban models achieve the strongest predictive performance with an accuracy of 0.607 and F1 of 0.590, urban areas benefit from adding education and employment predictors and crime in rural areas are still unpredictable using these current factors. The patterns found in this study shows the need for specific interventions like educational investments in metropolitan settings economic support in rural communities and demographicaware prevention strategies. |
| title | What does making money have to do with crime?: A dive into the National Crime Victimization survey |
| topic | Physics and Society Machine Learning |
| url | https://arxiv.org/abs/2506.04240 |