Meet the Team

Tracing Patterns in Capital Punishment

Our project identifies specific biases in the application of capital punishment in the United States by analysing execution and death sentence data from 1976 to 2016. We explore how factors such as the race of the defendant and the race of the victim influence who is prosecuted and sentenced to death, drawing on research showing that racial disparities persist at multiple stages of capital cases. Our work combines data analysis, visualisations, and historical context to highlight systemic inequities in American capital punishment and contribute to a deeper understanding of how law and society intersect in life-and-death decisions.

Our Team

Our Awesome Team

Emmanuel

Hello! My name is Emmanuel Martinez and I am currently a 4th year Applied Mathematics major at UCLA. My current role for the project is content developer. During my free time I enjoy going to the gym, running, playing soccer, and losing in Clash Royale.

Amaras

Hi! I’m Amaras, a 4th year Computer Science major. I’m the Project Manager for our team project. I enjoy fishing, hanging out with friends, and cars.

Junnan

Hi! Everyone, there is Junnan Lyu. My major is stats and data science, a senior at UCLA. I am the Data Visualisation Specialist for this project! I am making sure that each chart is clear and understandable!

Diana

Hello! My name is Diana Aboul-Hosn! I am a 3rd year at UCLA double major in English and Labor Studies with a minor in Digital Humanities! My role for this project is the Editor! I enjoy reading, trying out new places with my friends, and just gaining insight on random social trends! 

Cedric

Hi! My name is Cedric Sun and I am currently a 2nd year major in Maths/Econ and Statistics and Data Science. My role is Website Designer. 

Jason

Hi! My name is Jason Vu and I am 4th year Comp Sci major. I am the data specialist. I like to gym and eat and collect Pokemon cards!

Project Process

Sources

Our project Death Penalty Trends in US Capital Punishment investigates patterns of racial disparities in U.S. executions from the years of 1976 to 2016. We utilized a combination of about two quantitative datasets and an abundance of academic resources to show an understanding and clear linkage of how race and geography interact when it comes to the death penalty. 

Our core dataset and primary data is Kaggle’s “Executions in the United States, 1976–2016,” which provides demographic details for every individual executed since the death penalty was reinstated. Of course, there are some areas to consider which is that some victim’s information may be missing, it may also be difficult to accurately record very early executions in previous years, and how that may affect more recent ones. Each case represents an executed individual and includes variables such as their age, race, sex, execution date, victim characteristics (race and gender), execution method, state, and legal status. In addition, we have datasets from the Death Penalty Information Center (DPIC) and the NAACP Legal Defense Fund’s Death Row USA reports in order to cross check and verify the data we have received from Kaggle. To fully analyze these numbers, we also analyzed scholarly articles and studies on the “defendant-race effect,” which suggests executions may be driven more by the race of the defendant and victim than the base-level severity of the crime.

Process

Identifying the datasets we intend to use was a significant struggle in our data process. There were many directions that we could have pursued and we wanted to accurately explore concepts of racism and how rooted it may be in our legal system. After discussing as a group, we decided to hone in on victim’s race, region, defendant’s race and gender, when assessing their probability of receiving the death penalty. When we decided on our main research points we set our marks on obtaining Kaggle’s nationwide dataset. After our initial overview of that, we found that Texas was home to about 30% of all national executions in the United States. It was clear to us that in order to understand the United States’ execution patterns, we have to take a deep dive into Texas as well.

After collecting the datasets, we cleaned the information and structured it to make it suitable for analysis and visualization. The raw data was first reviewed using spreadsheet software (namely Google Sheets so that the entire group can access it) and statistical tools to identify inconsistencies, missing values, and formatting issues (mainly via Tableau). During this stage, key variables such as race, execution method, state, and gender were standardized so that categories were consistent across the entire dataset. Time variables, including execution dates, were also reformatted into a uniform way to allow comparisons across different years and time periods. This data preparation process was lengthy and oftentimes revisited, but it ensured that the dataset could be reliably analyzed across geographic regions and historical trends.

Once the dataset was cleaned, it was used to generate several visualizations designed to address our research questions. These visualizations examine patterns such as the number of executions by state, demographic trends related to race, age, and gender, and the relationship between victim race and sentencing outcomes. By transforming the raw numerical data into charts and maps, these visualizations make large datasets easier to interpret and allow viewers to quickly identify patterns and disparities that may not be immediately visible in spreadsheets. Within digital humanities research, visualization tools such as Tableau was helpful and significant to our representation of our data. Additionally, JS Timeline was a wonderful site that allowed us to compile all of our images and information regarding each yearly event in a condense and readable format.

Presentation

To present our findings, our team was given access by UCLA to a HumSpace domain and used WordPress for website design. We decided on having a deeper and dark theme to compliment the weight of our research topic. We then developed data visualizations to answer our research questions with Tableau. Following our research and acquiring reliable secondary sources, our website prioritizes clarity and accessibility to ensure viewers can identify patterns without being overwhelmed by data complexity. The main focus in our About page and narrative sections is to provide historical and legal evidence, while also awarding our group members for each of their respective contributions to this project. All in all with Tableau visualizations alongside our analysis of cases, and lengthy JS Timeline, we make sure that our digital projects function is not another, over explained database but a representation of how different factors such as race, geography shape the practice of capital punishment in the United States.

Acknowledgements

We would like to thank the following individuals for their invaluable support and guidance throughout this project:

Professor Dr. Nicholas Sabo
Teaching Assistant Pietro Santachiara

Their instruction and feedback helped shape both the analytical framework and the digital presentation of this research.

Professor Dr. Nicholas Sabo

Nicholas Sabo is a lecturer in Digital Humanities at University of California, Los Angeles who teaches data analysis, user experience design, and digital storytelling. He earned a Ph.D. in English Literature from University of Mississippi (2024) and holds a dual degree in Computer Science and English from Rice University. His research focuses on radio, AI-assisted transcription, and digital storytelling technologies.

Pietro Santachiara

Pietro Santachiara is a PhD candidate in Information Studies at University of California, Los Angeles whose research examines knowledge representation and cultural data in AI systems. He holds a Master of Library & Information Science and a Graduate Certificate in Digital Humanities from UCLA, and a Master of Science in Technology-Enhanced Communication for Cultural Heritage from University of Lugano in Switzerland.