收入教育越高，越少遭遇暴力犯罪
What does making money have to do with crime?: A dive into the National Crime Victimization survey
- 分组建模分析收入、教育等对犯罪类型的影响
- 高收入高学历者暴力犯罪风险显著降低
- 郊区预测效果最好，农村仍难精准预判
本文利用1992至2022年的国家犯罪受害调查数据，研究收入、教育、就业及关键人口统计因素如何影响受害者所经历的犯罪类型（暴力或财产类）。通过平衡分类划分与逻辑回归模型，并以F1分数评估，构建了包含社会经济因素的“组A”模型，以及加入年龄、性别、种族和婚姻状况等控制变量的“组B”模型。结果一致表明：收入与教育水平越高，遭遇暴力犯罪相对于财产犯罪的概率越低；而男性、年轻群体及少数族裔面临更高的暴力犯罪风险。在地理维度上，郊区模型预测表现最优，准确率为0.607，F1值为0.590；城市地区通过引入教育与就业变量提升预测能力；而农村地区当前因素仍难以预测犯罪。研究揭示需针对不同区域实施差异化干预，如在大都市加强教育投入、在农村提供经济支持，并制定基于人口特征的预防策略。
原文摘要 · Abstract (English)
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.
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