Certain types of crime fluctuate in prevalence over time, while others remain relatively constant. By analyzing statistical data from the sources discussed in the previous unit, we can identify these patterns in criminal behavior. However, analyzing statistical data alone is insufficient for obtaining a comprehensive understanding of crime trends. Qualitative and contextual factors such as the implementation or removal of laws, changes in population density among criminogenic age groups, and even weather conditions can significantly influence contemporary crime trends. Considering these additional factors is imperative for obtaining more accurate interpretations of crime statistics that curtail misleading conclusions. The following section provides a general overview of current nationwide crime trends, highlighting recent changes in the landscape of criminal activity and some of the factors underpinning these shifts.

UCR and NIBRS Crime Trends

The UCR and NIBRS provide valuable data for identifying short- and long-term crime trends in America. According to UCR and NIBRS data, U.S. crime rates prior to the 1960s, particularly violent crime rates, remained fairly consistent (Blumstein & Wallman, 2006). However, crime rates in most categories began to increase throughout the 1960s, 1970s, and 1980s. By contrast, both violent and property crime rates steadily decreased between the early 1990s and the 2010s. During the early 2020s, certain types of crime, such as homicide and auto theft, experienced slight increases, while other forms of criminal activity remained relatively constant over this same time period.

Violent Crime

This section will discuss violent crime trends in the U.S. using UCR and NIBRS data, focusing on homicide, rape, robbery, and aggravated assault. By examining these data sources, we can identify patterns and shifts in violent crime rates over time. Historical data reveals a significant rise in violent crime from the 1960s through the 1980s, followed by a notable decline starting in the early 1990s and continuing into the 2010s (Blumstein & Wallman, 2006). Despite this overall downward trend, recent years have seen slight increases in certain violent crimes, such as homicide and aggravated assault, while burglary has steadily decreased over the past several decades.

Figure 2.9 illustrates the per capita homicide rate in the U.S. between 1985 and 2022 (FBI, 2022d). Data from the UCR indicate that prior to 1965, homicide rates stayed below 5 homicides per 100,000 people. In the 1970s, the rates oscillated between 8 and 10 homicides per 100,000 people (Blumstein & Wallman, 2006). In 1980, the homicide rate reached an all-time high of 10.2 per 100,000 people before dropping over the subsequent five years, only to rise again to the second-highest rate of 9.8 homicides per 100,000 people in 1991. Following a sharp decline in the 1990s, the homicide rate remained below 6 per 100,000 people throughout most of the 2000s. Despite a short-lived spike in 2020 and 2021, the rate began to decline in 2022 (FBI, 2022d).

Figure 2.10 illustrates rape rates per capita in the U.S. between 1985 and 2022. Overall, there has been a gradual decline in rape rates since the early 1990s (FBI, 2022g). While UCR and NIBRS data are beneficial for evaluating rape rates, it is essential to consider legal and cultural changes. For example, as discussed in Drawbacks of the Uniform Crime Report from Crime Statistic Data Sources, the UCR changed the definition of rape in 2013, leading to a sharp increase in recorded incidents. Additionally, providing more resources and support to victims may have increased the willingness of victims to report rape. Consequently, it is difficult to discern if the number of actual rapes has changed over this time period.

Because homicide is often considered the “ultimate violent act,” police tend to allocate more resources and effort to investigating and recording this crime. Similarly, robbery is another offense that is well-defined in law and receives intensive police attention (Blumstein & Wallman, 2006). Like homicide, the rates of robbery have decreased since the peak in 1991, when there were over 250 robberies per 100,000 people. The robbery rates in 2022 were less than 66 per 100,000 people, which is lower than the rates during the 1960s (Blumstein & Wallman, 2006; FBI, 2011, 2022h).

Between 1972 and 1992, aggravated assault rates grew by 132%, from just under 200 per 100,000 people to close to 450 per 100,000 people (Blumstein & Wallman, 2006). According to UCR and NIBRS data demonstrated in Figure 2.12, the rate of aggravated assault crime decreased since the early 1990s, similar to homicide and robbery rates during this period (FBI, 2022a). Data from the NIBRS indicates that aggravated assault rates dropped to less than 250 per 100,000 people in 2013 but rose to almost 280 per 100,000 people in 2020. However, it is essential to acknowledge the subjectivity inherent in how police officers and departments categorize aggravated and simple assault when analyzing aggravated assault data (Blumstein & Wallman, 2006). Understanding this subjectivity is crucial as it can impact the comparability and accuracy of crime statistics over time and across different jurisdictions, influencing our interpretation of trends and the development of effective policies.

Careers in Criminal Justice: Crime Analyst Analyst

A career as a crime analyst offers an opportunity to play a vital role in law enforcement and public safety by utilizing data and analytical tools to understand and address criminal activity. Crime analysts are responsible for collecting, analyzing, and interpreting data related to crime patterns, trends, and hotspots. By identifying emerging crime trends and patterns, crime analysts help law enforcement agencies allocate resources effectively, develop crime prevention strategies, and enhance public safety initiatives.

To excel in this role, individuals need strong analytical and critical thinking skills, as well as proficiency in data analysis techniques and statistical software. Effective communication skills are also essential, as crime analysts often present their findings to law enforcement officials, policymakers, and other stakeholders. Additionally, attention to detail and the ability to work under pressure are crucial for success in this fast-paced environment.

According to data from Jobdescription.org, the annual salary for crime analysts varies depending on factors such as experience, location, and the employing organization (2023). On average, crime analysts can expect to earn between $59,000 and $84,000 per year, with opportunities for advancement and higher earning potential with additional experience and specialized skills.

Education requirements for crime analysts typically include a bachelor’s degree in criminal justice, criminology, sociology, statistics, or a related field. Some positions may require a master’s degree or specialized training in data analysis or crime mapping techniques. Additionally, obtaining certifications such as the Certified Crime Analyst (CCA) credential from the International Association of Crime Analysts (IACA) can enhance job prospects and credibility in the field.

Crime analysts may work in a variety of settings, including law enforcement agencies, government agencies, consulting firms, research institutions, or nonprofit organizations. They may work standard office hours, but their schedules may vary depending on the demands of their role and the needs of the organization. The work environment can be dynamic and challenging, with opportunities to make meaningful contributions to public safety efforts and the criminal justice system.

Property Crime Trends

Similar to violent crimes, data from the UCR and NIBRS indicate that property crime rates have declined over the past 40 years. The following sections will examine four types of property crimes—arson, burglary, larceny, and auto theft—to illustrate trends in property crime in the U.S.

As illustrated in Figure 2.15, arson crime trends have decreased considerably over the past 35 years (FBI, 2022b). The motivations for arson can vary greatly, including covering up evidence for other crimes, committing insurance fraud, protesting, or stemming from mental health issues such as pyromania. Additionally, arson is difficult to investigate as the evidence is often destroyed in the fire. Even when investigators determine that a fire was caused by arson, only a small percentage (21%) of offenders are apprehended (Brett, 2004). Increased surveillance, such as home security cameras, and improved mental health practices may help explain the national decrease in arson rates. Research has also identified a pattern linking arson to motor vehicle theft, showing an observed increase in vehicle arson in areas or during periods with higher rates of motor vehicle theft and joyriding (Potter, 2000).

According to the NIBRS, burglary and breaking and entering are defined as “the unlawful entry into a building or other structure with the intent to commit a felony or a theft” (FBI, 2012, p. 2). While larceny/theft offenses are defined by the NIBRS as “the unlawful taking, carrying, leading, or riding away of property from the possession, or constructive possession, of another person” (FBI, 2012, p. 4). As illustrated in Figure 2.16 and Figure 2.17, both burglary and larceny exhibited comparable trends between the mid-1980s and 2022 (FBI, 2022c).

The analysis of larceny crime rates in the United States from 1985 to 2022, as depicted in Figure 2.17, reveals notable trends. Initially, there was a discernible peak in 1991, indicative of heightened criminal activity during that period (FBI, 2022e). However, subsequent years witnessed a consistent downward trajectory in larceny rates, possibly reflecting effective crime prevention measures or shifting societal dynamics. Despite this overall decline, there was a slight uptick observed in 2022, suggesting potential fluctuations in crime patterns or the efficacy of law enforcement strategies in recent years. However, to fully understand the reasons underpinning these criminal behavior trends, additional qualitative information is needed to complement the quantitative data provided by the figures.

Motor vehicle theft, as depicted in Figure 2.18, peaked in the early 1990s, followed by a decrease until 2019 (FBI, 2022f). However, there has been a small increase in motor vehicle thefts in 2020, 2021, and 2022, prompting questions about the factors contributing to this recent uptick. While advancements in technology and theft-prevention features in cars would typically lead to a decrease in car thefts, the evolution of technology also enhances offenders’ capabilities to commit such crimes. For instance, a popular TikTok trend in 2021 demonstrated how to steal 2010-2021 Kia and Hyundai vehicles using a USB cord and mechanical key (DiLella & Day, 2022). As such, further qualitative research is essential to comprehensively understand these trends and devise effective strategies to combat auto theft in the evolving technological landscape.

NCVS Crime Trends

The following section provides an analysis of NCVS crime data spanning from 1993 to 2022, focusing on four crime types: rape/sexual assault, robbery, motor vehicle theft, and burglary. These specific crime categories were selected to offer insight into a broad spectrum of criminal trends, encompassing both violent and property crimes. It is crucial to consider the data collection method, which involves surveying approximately 240,000 individuals from around 150,000 households (Harrell et al., 2009). While this is a large population for a survey, this approach represents a significantly smaller population within the dataset compared to NIBRS and UCR data. Therefore, the NCVS data will yield more varied results from year to year than the NIBRS and UCR data. This variability could stem from factors such as changes in reporting behavior, shifts in survey demographics, or fluctuations in respondents’ perceptions of crime or interpretations of survey questions.

According to the NCVS data presented in Figure 2.20, there has been a notable decrease in rape/sexual assault victimization from 1993 to 2022 (BJS, 2022d). However, it’s important to note that the year-to-year variance in these rates appears to be more erratic compared to the NIBRS rape data illustrated in Figure 2.10. Despite this variation, the overall trend exhibits a similar pattern of decline.

Figure 2.21 illustrates the trends in robbery victimization between 1993 and 2022 (BJS, 2022e). Overall, there has been a substantial decrease in robberies during this period. The findings suggest that participants in the NCVS were 250% more likely to report being a victim of robbery in 1993 than in 2022. Despite this long-term decline, the survey indicates a slight increase in robberies in 2021 and again in 2022, mirroring a common trend observed in several crime category charts.

Similar to the trends observed in violent crimes reported in the NCVS, property crimes, such as motor vehicle theft (depicted in Figure 2.22), have experienced significant declines over the past 30 years (BJS, 2022c). This pattern is evident in the decrease in reported motor vehicle thefts between 1993 and 2020. However, there was a slight increase in respondents reporting being victims of motor vehicle theft in 2021 and 2022, which corresponds with data concerning other criminal activity patterns during this same timeframe.

Consistent with trends in other property crimes, burglary victimization (see Figure 2.23) has declined among respondents over the past 30 years (BJS, 2022b). When comparing findings concerning burglary trends from the UCR/NIBRS (see Figure 2.16) with their counterpart findings from the NCVS, a consistent pattern emerges.

Ethics in Criminal Justice: Proper Understanding of Crime Research and Data

Data misrepresentation and misinterpretation are significant concerns when utilizing national crime data to support policies, programs, and theories of crime. Misrepresented and misinterpreted data can reinforce stereotypes, leading to biased conclusions and ineffective or even harmful policies (Moore et al., 2022).

For instance, consider a scenario where a police officer believes that drivers of blue cars are more likely to drink and drive. Acting on this bias, the officer pulls over twice as many blue cars as red cars. Predictably, the officer will arrest more individuals in blue cars for drinking and driving. This biased enforcement results in skewed data that reflects a higher incidence of drinking and driving among blue car drivers, but the underlying bias influencing the officer’s behavior is not captured in the data.

As a result, the data appears to validate the officer’s initial bias, reinforcing the misconception that blue car drivers are more prone to such offenses. This faulty conclusion can then influence departmental practices and policies, leading other officers to similarly target blue cars. Over time, this creates a cycle of bias and discrimination against drivers of blue cars, rooted in misrepresented and misinterpreted data.

Such distortions in data can have far-reaching consequences. Policies and programs based on flawed data can misallocate resources, target the wrong populations, and perpetuate unjust practices. For example, if national crime data inaccurately represents certain communities as having higher crime rates due to biased policing practices, these communities may face increased surveillance and law enforcement presence, exacerbating tensions and mistrust.

Moreover, crime data can easily be manipulated to mislead policymakers and the public about crime trends. This is particularly problematic when dealing with percentage increases or decreases in rare or low-base-number crimes. For example, if a small city typically averages one murder per year but then experiences a domestic violence situation where a husband kills his two children and wife, the murder rate in that city will increase 200% in one year. This dramatic increase could lead people to believe that the city has become significantly more dangerous, even though it is based on a rare and isolated incident. Such interpretations can cause issues in law enforcement practices, resource allocations, and policy implementation.

To prevent these issues, it is crucial to ensure that crime data is accurately collected, reported, and analyzed. This involves implementing rigorous training for law enforcement to minimize biases, adopting transparent data collection methodologies, and conducting regular audits to identify and correct any disparities. Additionally, involving community stakeholders in the analysis and interpretation of crime data can provide valuable insights and help create more equitable and effective policies.

Attributions

1. Figure 2.9: UCR and NIBRS Homicide Crime Rates in the U.S. (1985-2022) by Wesley Maier in the Public Domain; U.S. government works created by a federal government employee as part of their official duties are in the public domain.
2. Figure 2.10: UCR and NIBRS Rape Crime Rates in the U.S. (1985-2022) by Wesley Maier in the Public Domain; U.S. government works created by a federal government employee as part of their official duties are in the public domain.
3. Figure 2.11: UCR and NIBRS Robbery Crime Rates in the U.S. (1985-2022) by Wesley Maier in the Public Domain; U.S. government works created by a federal government employee as part of their official duties are in the public domain.
4. Figure 2.12: UCR and NIBRS Aggravated Assault Crime Rate in the U.S. (1985-2022) by Wesley Maier in the Public Domain; U.S. government works created by a federal government employee as part of their official duties are in the public domain.
5. Figure 2.13: Young scientist standing over chalkboard filled with different scientific formulas by Marco Verch is released under CC-BY 2.0
6. Figure 2.14: image released under the Pexels License
7. Figure 2.15: UCR and NIBRS Arson Crime Rates in the U.S. (1985-2022) by Wesley Maier in the Public Domain; U.S. government works created by a federal government employee as part of their official duties are in the public domain.
8. Figure 2.16: UCR and NIBRS Burglary Crime Rates in the U.S. (1985-2022) by Wesley Maier in the Public Domain; U.S. government works created by a federal government employee as part of their official duties are in the public domain.
9. Figure 2.17: UCR and NIBRS Larceny Crime Rates in the U.S. (1985-2022) by Wesley Maier in the Public Domain; U.S. government works created by a federal government employee as part of their official duties are in the public domain.
10. Figure 2.18: UCR and NIBRS motor vehicle theft crime rates in the U.S. (1985-2022) by Wesley Maier in the Public Domain; U.S. government works created by a federal government employee as part of their official duties are in the public domain.
11. Figure 2.19: image released under the Pexels License
12. Figure 2.20: NCVS Rape/Sexual Assault Crime Rates in the U.S. (1993-2022) by Wesley Maier in the Public Domain; U.S. government works created by a federal government employee as part of their official duties are in the public domain.
13. Figure 2.21: NCVS Robbery Crime Rates in the U.S. (1993-2022) by Wesley Maier in the Public Domain; U.S. government works created by a federal government employee as part of their official duties are in the public domain.
14. Figure 2.22: NCVS Motor Vehicle Theft Crime Rates in the U.S. (1993-2022) by Wesley Maier in the Public Domain; U.S. government works created by a federal government employee as part of their official duties are in the public domain.
15. Figure 2.23: NCVS Burglary Crime Rates in the U.S. (1993-2022) by Wesley Maier in the Public Domain; U.S. government works created by a federal government employee as part of their official duties are in the public domain.
16. Figure 2.24: The Various Components of Ethical Research by Kadence C. Maier, for WA Open ProfTech, © SBCTC, CC BY 4.0
17. Figure 2.25: image released under the Pexels License