In her article, Addington takes a more realistic perspective of NIBRS data by exposing both its advantages and limitations. While she gives NIBRS credit for being far more detailed than the Summary UCR system, Addington points out that the more variables present, the more likely the data is to be incomplete. In many cases, missing data prevents much of the meaning from coming through in the recorded information. One example Addington uses is how victim demographics, such as race or ethnicity, are often left out, stemming from law enforcement not updating files beyond the information they get immediately at the incident. The key takeaway from this article is that the missing data is not random but systematic. In certain cases, mainly those that are more complicated, incomplete information is much more likely to be present, inevitably distorting any conclusions drawn from the data. This distinction is important to our project because any changes in crime severity by victim race/ethnicity might only reflect those individuals/cases who got fully documented, rather than those who are actually being victimized more.

Ajide examined the relationship between Nigeria’s economic status and crime rates. Focusing on unemployment, income level, and economic instability influences criminal activity. He used economic modeling. The result suggested that adverse economic conditions are significantly associated with higher crime rates, especially during economic downturns.

This article estimates the state government spending and its impact on crime rates. Akpom and Doss used data from 1990,2000 and 2010. Using multiple regression analysis to examine the relationships between the variables such as unemployment, poverty, income, and government spending on education, welfare, and protection across different crime categories. Their research suggests that economic(especially poverty) and government spending patterns have a significant impact on the time period and the types of crime.

This journal article explores the relationship between zoning, infrastructure, and crime across Los Angeles. It gives insight into how urban planning decisions shape public safety. Its literature review concludes that zoning changes are associated with reduced crime, suggesting that combining residential zoning with commercial blocks reduces crime. The study chose 8 blocks across Los Angeles to observe: Boyle Heights, Highland Park, Hollywood, San Pedro, South Los Angeles, Southeast Los Angeles, West Adams, and Westlake. For each block, socioeconomic data were collected. For results, the analysis drew conclusions about the associations between crime, zoning classifications, zoning homogeneity, and infrastructure.

This research study hypothesized that gang violence in Los Angeles increased following COVID-19, however, the results showed that it stayed stable instead. It used a time-series forecasting approach to detect changes in crime volume, fitting an ARIMA model in R. Overall, shelter-in-place orders during COVID-19 did not affect gangs across various jurisdictions in Los Angeles.

This article examines whether COVID-19 containment policies produced immediate changes in reported crime in Los Angeles by modeling daily counts from 1 Jan. 2017 through late March 2020 using Bayesian structural time-series methods. They compare “mild” and “strict” intervention windows in March 2020 and find significant short-term decreases in overall reported crime, especially robbery, shoplifting, theft, and battery, while other categories (including homicide and vehicle theft) show no significant immediate change. This source is useful for my project because it provides a rigorous, clearly defined early-pandemic baseline and a method for isolating policy impacts over time. It also supports a digital humanities framing by reminding readers that “reported crime” can reflect shifts in routine activities, enforcement, and reporting behavior, not just changes in offending. One concern here is that it covers a time period before the dataset we are using.

In this report, Gerald Chaleff conducts an independent review of the Los Angeles Police Department’s response to the protests that occurred in May and June 2020 after the death of George Floyd. The report analyzes how LAPD handled the demonstrations and identifies several major problems in the department’s response, including inadequate planning, confusion in command structure, insufficient training for public order policing, and issues with mass arrests and detention procedures. For example, Chaleff explains that the department initially assumed protests in Los Angeles would remain peaceful and did not fully prepare for widespread demonstrations, which made the police response more reactive rather than preventative when violence and looting began to occur. The report also discusses the use of less lethal weapons and notes that some individuals who were not involved in criminal activity were injured during crowd control efforts. Overall, Chaleff concludes that LAPD needs better training, clearer command structures, and improved planning for large-scale protests. This source is useful for our project because it provides a detailed evaluation of police tactics and decision-making during the 2020 protests and offers concrete reports on factors that may have influenced the types of crimes occurring after this period and the public’s overall sentiment toward law enforcement agencies.

This report, authored by criminologists Richard Rosenfeld, Ernesto Lopez, and Thomas Abt for the Council on Criminal Justice’s National Commission on COVID-19 and Criminal Justice, examines changes in crime rates across 34 American cities through March 2021. The study analyzes monthly data for ten categories of violent, property, and drug offenses, finding that aggravated assault rates increased by 7% and gun assault rates rose by 22% compared to the prior year. Notably, the report found that domestic violence did not increase in the first quarter of 2021 over the first quarter of 2020, though this result was based on only 11 of the 32 cities studied. This source is relevant to our project because it provides empirical evidence of how the pandemic reshaped the distribution of crime types in major U.S. cities, supporting our argument that the COVID-19 period compressed crime into specific categories and locations rather than uniformly increasing or decreasing criminal activity. The Council on Criminal Justice is a nonpartisan organization co-chaired by former U.S. Attorneys General, lending institutional credibility to its findings.

636 neighborhoods in Los Angeles were analyzed to evaluate patterns among property crime rates, arrest rates, job density, and demographic data. Both a statistical and an economic model were used to compare these rates across the neighborhoods. The study concluded that crime is high in downtown and inner-city neighborhoods of Los Angeles. Crimes lower in places further from the central city, including West LA, West Valley, Foothill, and Devonshire. Lower average household income is correlated with higher crime rates. However, there seems to be no clear correlation between crime rate and job density. 

Fisher, Cullen, and Turner’s government report analyzes national survey data to show how demographic factors, particularly age and gender, affect the likelihood of sexual victimization among college women. Funded by the U.S. Department of Justice and based on rigorous methodology, the study is highly credible, although it focuses narrowly on one crime category and population. This source supports my research question by illustrating that specific demographic groups are more vulnerable to certain forms of crime, reinforcing the idea that demographic variables in the LA dataset may correlate with distinct crime patterns.

Hou and colleagues compare reported crime incident counts before vs. during COVID-19 across four U.S. cities—Washington, DC; Chicago; New York City; and Los Angeles—focusing on theft, fraud, assault, robbery, and burglary. They test whether COVID-19 case counts “Granger-cause” changes in crime time series and then evaluate whether adding case counts improves short-term crime prediction using LSTM models. For Los Angeles specifically, they report significant Granger relationships for fraud and robbery and show that many crime categories decreased during pandemic periods, consistent with Routine Activity and Opportunity theories. This article is useful for my project because it provides a multi-city comparative baseline that includes Los Angeles and links pandemic conditions to both temporal trends and modeling choices, helping me justify how and why crime patterns might shift during disruptions.

This study examines crime patterns in Los Angeles County before and after the COVID-19 pandemic using a data science–driven methodology. Drawing on LAPD crime datasets and LA County COVID-19 case data, the authors employ geospatial mapping, time-series analysis, Marimekko charts, and regression modeling to compare crime rates, crime types, and demographic variables between pre-pandemic (2018 to 2019) and pandemic/post-pandemic (2020 to present) periods. The paper investigates multiple analytical dimensions, including geographic crime concentration, shifts in crime types, juvenile crime trends, weapon usage, and the predictive influence of demographic factors such as age and sex. While the authors frame the project as a single study, the analysis effectively addresses multiple distinct research questions oriented toward prediction and operational use, particularly for law enforcement resource allocation. The project emphasizes quantitative correlation and regression modeling, as well as practical applications, rather than interpretive synthesis. As such, this work contrasts with a digital humanities approach, which would foreground questions of institutional framing, data silences, narrative construction, and the social meaning of crime data rather than predictive accuracy. This article is useful as a comparative example of how COVID-19 crime research has largely been conducted through applied analytics, highlighting the methodological gap that a humanities-driven analysis can address.

‌This article investigates how victims’ experiences of reporting crime actually relate to the type of crime, the reporting process, and subsequent police responses. They use data from a nationwide survey consisting of over 25,000 crime victims. The authors find that police actions significantly influence victims’ satisfaction. These relationships vary by offense and sociodemographic factors. The research showcases the importance of considering how reporting experiences vary across demographics and crime, supporting my analysis of reporting and resolution patterns between 2021 and 2025.

In this article, Hipp and Kubrin examine how changes in economic inequality in surrounding neighborhoods can influence crime levels in nearby areas. Using neighborhood-level data from Southern California, the authors analyze how both local inequality and inequality in adjacent communities affect crime patterns over time. Their findings suggest that increases in inequality in nearby neighborhoods can lead to higher crime rates in a given area, even if inequality within that specific neighborhood does not change significantly. The authors argue that crime is influenced not only by conditions within a single neighborhood but also by broader spatial dynamics and economic differences between neighboring areas. By highlighting how crime can spread or intensify due to inequality in surrounding communities, the study demonstrates the importance of considering regional patterns rather than focusing only on isolated neighborhoods. This source is useful for our project because it provides empirical evidence linking economic inequality to crime and helps explain how social and economic conditions across neighboring communities can shape crime patterns.

Lauritsen and Quinet’s article reviews how demographic factors such as age, race, and gender shape patterns of victimization and offending, arguing that social inequality and environmental exposure strongly influence risk. As established criminologists publishing in a peer-reviewed journal, they are credible despite the older publication date because their work summarizes foundational theories still widely used in the field. This source is valuable to my research because it provides a theoretical background showing that demographic categories are closely correlated with specific types of crime, which helps me interpret demographic differences within the LA crime dataset.

Loureiro, Moreira, and Ellery examine how political parties can influence crime behavior. They explain the difference between left- and right-leaning parties in their approaches to crime control, enforcement intensity, and public attitudes toward crime behavior.

Peguero’s study examines victimization among racial and ethnic minority youth and finds that these groups experience certain crime types disproportionately due to discrimination, environmental inequality, and school climate. The article, published in a respected scholarly journal and based on large national datasets, provides reliable, contemporary evidence of demographic disparities in crime exposure. This research is directly relevant to my project because it demonstrates how race and ethnicity predict victimization patterns, supporting my analysis of correlations between demographic groups and crime types in Los Angeles.

In this article, Rantala takes a case-study approach to examine what actually happens to crime data when agencies switch from the Summary UCR system to NIBRS, specifically by examining agencies that reported both systems in the same years in the 1990s. By doing this, she can separate changes caused by the reporting method from real changes in crime itself. One key concept she focuses on is the hierarchy rule, which, under the previous Summary UCR, meant that only the most serious offense in an incident was counted. Overall, she finds that total crime rates usually changed only slightly, but certain offenses (specifically larceny and motor vehicle theft) showed noticeable increases under NIBRS because they were often hidden in multiple-charge incidents under the former system. This helps explain why crime can look more frequent or severe after switching systems, even when underlying behavior hasn’t changed. For our project, this article provides quantifiable proof towards the argument that the new counting rules may reflect changes in crime severity rather than actual changes in crime behavior. 

‌This article explores the validity and interpretation of  “clearance rates” as measures of case resolution across various crime types and demographics. Slepicka critiques an over-reliance on clearance percentages alone, showing that arrest likelihood may differ by crime severity. This suggests that changes in clearance statistics over time reflect changes in institutional behavior rather than underlying trends. This study supports my question that case resolution patterns between 2021 and 2025 may be influenced by law enforcement priorities.

This article examines how NIBRS not only fundamentally changes the amount of crime data available but also how crime statistics should be understood more broadly. The previous Summary UCR system, dating back to the typewriter era, only recorded the most serious crimes. This meant that many crimes were completely ignored, including the specifics about them, such as the victim, additional charges, and the circumstances surrounding the incident, seemingly vital information. However, with the shift to NIBRS, every offence occurring in a given incident is now recorded, meaning that charges that were “invisible” in older crime data are now counted.  This is important because crime can now appear more severe or more frequent, even though people’s behavior has not actually changed much. This article is directly relevant to my question, as it explains why differences in crime severity across victim race/ethnicity in Los Angeles from 2021-2025 (when the LAPD adopted this recording methodology) may reflect changes in how crimes are recorded, rather than real changes in crime behavior.

‌This article analyzes how victim reporting to police has changed over time, showing the declines in reporting and factors that may have shaped those trends. Xie explores how many different factors influence whether crimes are reported, and their trust in law enforcement. She finds that reporting varies by the type of offense and the victim’s background. Additionally, declining trust has contributed to lower reporting rates for specific crimes. This study supports my research by showing that differences in crime reporting across crime types and demographic groups are shaped by underlying factors, helping explain the patterns observed between 2021 and 2025.