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Visualizing Changes in the Age-Distributions of Homicides in the United States: 1964–2019

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Abstract

Objective

To demonstrate how visualization can aid in understanding crime rate data and provide new insights and hypotheses for some central criminological questions about homicide offending over time.

Methods

The research uses arrest data that is based on a mixture of single year age data and other age groupings to produce single years of age estimates of homicide offending for those 15–64 from 1964 to 2019. This data is then presented in surface plots, contour plots, and with graphs based on simple statistics to address four areas of research: the increase in homicide rates from the mid-1960s to the mid-1970s, the drop in homicide offending from the early 1990s to 2000, the epidemic of youth homicide, and the invariance of the homicide age-curve.

Results

The epidemic of youth homicide (ages 15–24) lasts well into the period when homicide rates dropped in the 1990s. For most of the population (excluding those 15–24) the homicide drop is initiated around 1980 rather than the early 1990s. However, the homicide increase of mid-1960s to mid-1970s included increases in the homicide rate for both those 15–24 and those not in the 15–24 age group.

Conclusions

Researchers can learn much about important areas in criminology by examining the relationship between age and homicide offending using simple visualizations based on raw data and pursue what they learn using line graphs based on elementary statistics and simple statistical methods. Of course, complicated statistical methods are called for in many situations.

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Notes

  1. Quetelet (2003 [1831]) wrote about the age-crime curve and its relative invariance in 1831.

  2. When I refer to youth, I will use the United Nations (United Nations 2021) and World Health Organization (WHO 2021) definitions of youth as those 15–24 years of age. This age range includes those with the highest homicide rates from 1964 to 2019 in the United States. Some literature define youth as juveniles (less than 18 years of age). In any case, during the epidemic of youth homicides, youth had a striking increase in homicide rates in comparison to those of older ages.

  3. Importantly the number of homicides known to the police each year is quite close to the number of homicides that are reported in the Vital Statistics based on death certificates (see, for example, O’Brien 2019).

  4. Supplementary Homicide Report data by age that is representative of the population of the United States is available from the mid-1970s. It is also based on arrest data, but has some additional details on the victim as well as the offender in particular incidents.

  5. I use the same method with the Center for Disease Control (CDC 2021) data on the United States resident population for the years 1964–1989, which is categorized in five-year age groups. This data is not truncated at age 65 plus, so that I was able to use five five-year age groups in all of my estimates using the Sprague formula.

  6. This coding indicates the identification problem in APCMC models in that being a specific age (\(i\)) and year (\(j\)) determine an observation’s birth cohort (\(I-i+j\)).

  7. The rates per 100,000 for 18 year olds were 57 in 1991, 52 in 1992, 55 in 1993 and 52 in 1994.

  8. See McDowall (2019) and Donohue (1998) on fluctuations in homicide rates versus longer term trends.

  9. The rate is calculated as the estimated number of homicide offenses for all age groups except for those 15–24 divided by the number of residents in the US not in the 15–24 age category times 100,000.

  10. For reviews of approaches to estimating APCMC models see Fosse and Winship (2019), Bell (2020), and O’Brien (2019).

  11. The deviations from their linear trend for periods and ages are also estimable functions.

  12. It is the linear trends for ages, periods, and cohorts that create the identification problem in APCMC models.

  13. These are residuals are from an APCMC model in which the homicide rate variable has been logged, which is common in most regression models that analyze homicide rates (because these rates are based on counts and they are skewed to the right). The proportion of the variance accounted for in the APCMC homicide model is .977.

  14. The General Social Survey (GSS) is a project of the independent research organization NORC at the University of Chicago, with principal funding from the National Science Foundation.

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  1. Department of Sociology, 1291 University of Oregon, Eugene, OR, 97403, USA

    Robert M. O’Brien

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Correspondence to Robert M. O’Brien.

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O’Brien, R.M. Visualizing Changes in the Age-Distributions of Homicides in the United States: 1964–2019. J Quant Criminol 39, 495–518 (2023). https://doi.org/10.1007/s10940-022-09543-y

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