BRIDGING THE GAP

Our project draws from the Stanford Open Policing Project dataset.

Traffic stops give us valuable insight into the intricacies of policing and whether biases play a role in these interactions. Our research focuses on understanding these patterns in San Francisco, using data from the Stanford Open Policing Project. Overall, we predict that there may be patterns of disproportionate stops and harsher consequences for minority groups in San Francisco. We also hope to understand how changes in police training and regulations over time may have impacted police stop trends. 

Our primary dataset covers over 100 million traffic stops across the U.S. and provides details like the race, gender, and age of people stopped, the reasons for the stop, and what happened next. The Stanford Policy Lab, the Stanford Journalism Lab, and the Knight Foundation supported the formation of this dataset. It is a resource for looking at aspects of stops like searches, contraband discovery, and whether they result in warnings or citations. However, there are some gaps, such as missing details about what kind of contraband was found or whether body cameras were used, which limit the full picture. Further, we must remember that not all aspects of a stop are documented and that standards might have shifted over time. Even so, the dataset paints a compelling portrait of policing in San Francisco’s diverse communities. 

By studying who gets stopped, why, and what happens after, we aim to uncover patterns that might point to biases or inequalities. As the Stanford Open Policing Project notes, “By making this data accessible, we hope to support reforms that lead to fairer and more equitable policing practices.” For instance, early analysis shows that Black drivers are stopped and cited at disproportionately higher rates compared to other groups. From our initial research, we were able to understand that there may also be certain regions of SF that have significantly more stops than others, and the area might be worth exploring further. Ultimately, we hope this research can add to the conversation about making policing fairer and more accountable by using real-world data to highlight areas that need change.

Our literature review highlights the importance of exploring bias in police behavior, but trends in police stops specifically have yet to be explored in depth.

Our literature review highlights continuous inequities in police traffic stops, namely, that there are significant biases in traffic stops’ frequency and outcomes when referring to marginalized groups. For example, some of the pieces highlighted the disproportionate number of Black and Hispanic drivers who get stopped and searched resulting in lower rates of contraband found when compared to white drivers. The lack of justifiable outcomes was acknowledged within multiple pieces, illustrating further the systemic biases in policing. The readings also address the negative perceptions of law enforcement formed by certain groups as a result of the police stop targeting. The numerous studies highlighted heightened prejudice and scrutiny during police stops. Other literature has begun to focus on data to identify discrimination and prejudice-driven police practices. The focus has shifted to understanding why and how biases are reinforced within police practices. 

Amongst the literature, there was a consensus that there are racial and socioeconomic disparities between traffic stops. Contradictions emerged in the justification of the biases and disparities. Some works, especially those looking at the perspective of law enforcement, articulated that it is efficiency-driven. On the other hand, some believe that there are deeper-rooted racial prejudices from an institutional side of things. Alternatively, there were multiple opinions when it came to reform. Our readings agreed that more data and studies are needed to understand how we can work toward meditating inequities.

The readings have left us with multiple unanswered questions. What specific racial groups are unfairly and more consistently stopped?  Who is being targeted with prejudice on account of biology or other unchangeable factors when it comes to traffic stops? How are police making deductions or incorporating these biases when determining whether to stop someone? Another potential gap in the research we have encountered lies in the analysis of time of stop and how that correlates with force, search frequency, and other factors. We believe this small nuance is important in the conversation of traffic stop factors. 

Understanding the potential biases of police stops in San Francisco can help us learn more about societal bias and work toward understanding how to improve the situations of minority citizens.

While this project focuses on San Francisco, the insights we gather from the data can hold applications for the rest of the United States, and perhaps the world. Through our analysis of maps, we can extrapolate patterns on how humans subconsciously use geography and physical space to perpetrate their own biases against certain groups of people, mainly racial demographics. This tendency manifested in both police practices and all other aspects of daily life and how societies are structured. This project aims to uncover similar humanistic messages and consider how they apply differently worldwide. In a sense, we can view San Francisco as a microcosm of the world while also recognizing its features and history that make it unique. 

This viewpoint of both small and big-picture questions allows us to challenge the current status quo or assumptions associated with current policing practices. We want to help others understand how these practices and outcomes have trended throughout the 21st century, and what areas are most notable for further research. We hope to create a clear view of who is treated unfairly by police so that future researchers can explore how to best support advocacy efforts made by these marginalized groups.

for those unfamiliar with traffic stops and police terminology, here is a list of helpful vocabulary that may be found throughout our project: