Welcome! This is the temporary workspace for the Blue Data Lab. We study police violence and surveillance.
Our Github page on Detroit's ShotSpotter data (credit: Eric Frederiksen) which is in the process of being hosted on Shiny (credit: Dawson Kinsman, Hadi Chaaban).
Preliminary study website (credit: Sydni Uhlenberg, Spring '23)
Other resources;
This project studies the the Mapping Police Violence database. Outputs include:
The homological persistence of police violence: analysis and limitations (with Dawson Kinsman) preprint code
We use topological data analysis, namely persistent homology and persistence landscapes, to study trends in fatal police violence in the US between 2016 and 2022. The results of our analysis do not reveal any novel trends or characterizations about our data, but we are able to determine clusters where police violence occurs and de- termine the stability of these clusters using persistence landscapes. In other words, our findings on the homological persistence of police violence confirms general expectations on where police violence is concentrated. We also discuss the inherent methodological limitations of analyzing police data, also called Blue Data, and consequently the need from broader analyses involving other data sets in order to account from the full scale of the policing apparatus and the consideration of critical questions surrounding the function of policing and carcerality at large.
A Mixed Methods Assessment of Off-Duty Police Shootings in a Media-Curated Database (E. Asabor, E. Lett, B. Mosely, C. Boone, S. Sundaresan, T.A. Wong, M. Majumder)
Health Services Research. (2023) journal
The aim of this study was to examine rates of killings perpetrated by off-duty police and news coverage of those killings, by victim race and gender, and to qualitatively evaluate the contexts in which those killings occur. There were threefold higher odds of news reporting of a police-perpetrated killing and the off-duty status of the officer for incidents with Black and Hispanic victims. Qualitative analysis revealed that off-duty officers intervened violently within their own social networks; their presence escalated situations; they intentionally obscured information about their lethal violence; they intervened while impaired; their victims were often in crisis; and their intervention posed harm and potential secondary traumatization to witnesses. We conclude that police perpetrate lethal violence while off duty, compromising public health and safety. Additionally, off-duty police-perpetrated killings are reported differentially by the news media depending on the race of the victim.
Predictive policing: A mathematical primer (with Theo McKenzie and Joseph Johnson)
Notices of the AMS (to appear)
There are many surveys and explainers on predictive policing from various points of view, such as surveillance technology, algorithmic fairness, and criminal justice, but few specifically address its mathematical aspects. The purpose of this article is to present a selective overview of the mathematics underlying the model used by the company PredPol, followed by critiques and further developments of the model. We focus on PredPol as it is a well-known method of predictive policing and the methodology is arguably less oblique than other predictive policing algorithms that rely more heavily on black-boxed machine learning algorithms.
Proactive policing as reinforcement learning (with Dawson Kinsman)
ICLR Tiny Papers (2023) OpenReview
Recent analyses of predictive policing have shown the inherent biases in such systems. We show that the models considered in fact apply to proactive policing in general, which can be also viewed as a reinforcement learning system, and thus may also lead to over-policing.
Predictive Policing