Visualizing Changes in the Age-Distributions of Homicides in the United States: 1964–2019
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...
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| Medienart: | Elektronisch Aufsatz |
| Sprache: | Englisch |
| Veröffentlicht: |
2023
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| In: |
Journal of quantitative criminology
Jahr: 2023, Band: 39, Heft: 2, Seiten: 495-518 |
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Volltext (lizenzpflichtig) Volltext (lizenzpflichtig) |
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| 245 | 1 | 0 | |a Visualizing Changes in the Age-Distributions of Homicides in the United States: 1964–2019 |
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| 520 | |a 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. | ||
| 650 | 4 | |a Cohort effects on the age curve of homicide offending | |
| 650 | 4 | |a The great drop in U.S. homicide rates | |
| 650 | 4 | |a Invariant age curve of homicide offending | |
| 650 | 4 | |a epidemic of youth homicide | |
| 650 | 4 | |a Visualizing homicide rates by age | |
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