In November 2023, the National Institute of Statistical Sciences (NISS) and Carleton College hosted the Ingram Olkin Forum titled "Statistical Challenges in the Analysis of Police Use of Force." This forum was motivated by the urgent issue of excessive use of force by police and difficulties in effectively analyzing data on policing practices. In this panel, we will summarize four topics discussed at the Ingram Olkin Forum relating to police use of force data: causal inference, use of unstructured data, standardization of data, and spatial analysis. We will give an overview of these areas and facilitate engagement from statisticians that are new to this topic by providing discussion of future areas of research.
Panelists
Dr. Elizabeth Brault, Assistant Professor, Criminology and Criminal Justice, Merrimack College
Dr. Jennifer Bourgeois, Postdoctoral Fellow, Center for Justice Research, Texas Southern University
Moderator
See Publications from Police Use of Force forum:
Bourgeois, J. W., Haensch, A., Kher, S., Knox, D., Lanzalotto, G., & Wong, T. A. (2024). How to Use Causal Inference to Study Use of Force. CHANCE, 37(4), 6–10. https://doi.org/10.1080/09332480.2024.2434435
Brault, E. E., Kelling, C., Bourgeois, J. W., Taheri, S. A., Jones, A., Charles, C., … Banks, D. (2024). Toward Standardization of Police Use of Force Data. CHANCE, 37(4), 24–30. https://doi.org/10.1080/09332480.2024.2434438
Kelling, C. (2024). Statisticians Address Analysis of Police Use of Force. CHANCE, 37(4), 4–5. https://doi.org/10.1080/09332480.2024.2434432
Kelling, C., Allen, C., Brault, E. E., & Matos, P. (2024). Issues in the Spatial Analysis of Police Use of Force Data. CHANCE, 37(4), 11–17. https://doi.org/10.1080/09332480.2024.2434436
Shah, T., Allen, C., Ibrahim, A., Kefalas, H., & Stevens, B. (2024). The Use of Unstructured Data to Study Police Use of Force. CHANCE, 37(4), 18–23. https://doi.org/10.1080/09332480.2024.2434437
About the Panelists
About the Moderator
Dr. Claire Kelling is an Assistant Professor of Statistics in the Mathematics and Statistics department at Carleton College, a position she has held since 2022. She earned dual Ph.D. degrees in Statistics and Social Data Analytics from The Pennsylvania State University and holds a Bachelor of Science in Statistics and a Bachelor of Arts in Economics with a minor in Women's and Gender Studies from Virginia Tech. Dr. Kelling's research lies at the intersection of criminology, public health, public policy, spatial statistics, and computing. She has extensive experience applying statistical methods to research problems that promote social good. In her second year, she was a National Science Foundation (NSF) Big Data Social Science IGERT (Integrative Graduate Education and Research Traineeship) Fellow, during which she analyzed and developed new spatio-temporal models of crime while incorporating social proximity through a network-based approach to neighborhoods. This fellowship facilitated collaborations with experts across multiple disciplines, including statistics, political science, sociology, geography, and computer science. At Carleton, Dr. Kelling teaches various courses, including Introduction to Data Science, Applied Regression Analysis, and Introduction to Statistics. She is committed to involving students in research and welcomes inquiries from those interested in her lab, regardless of experience level. See Profile
About the Statistics Serving Society ( S3 ) Ingram Olkin Forum Series
The National Institute of Statistical Sciences (NISS) presents Statistics Serving Society ( S3 ), a series of forums to honor the memory of Professor Ingram Olkin. These forums at NISS provide a platform for statisticians to discuss and address pressing societal issues through statistical insights and solutions.
- Reducing Maternal Mortality Rates Across the USA
- The Opioid Crisis
- Gun Violence - The Statistical Issues
- Police Use of Force
- Statistical Methods for Combatting Human Trafficking
- Advancing Demographic Equity with Privacy Preserving Methodologies
- COVID and the Schools: Modeling Openings, Closings, and Learning Loss
- Algorithmic Fairness and Social Justice
- Unplanned Clinical Trial Disruptions
Event Type
- NISS Hosted
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