Join host Collin Paschall for a discussion with Claire Kelling on publicly available policing data and how communities can better understand how they are being policed.

Publicly available policing data presents numerous opportunities for communities to better understand how they are being policed. However, limited data quality and complexities in policing data present opportunities and challenges for research in spatial statistics. This talk will focus on one particular challenge within the area of spatial analysis of policing data. Point process models rely on the availability of the precise location—for example, latitude/longitude coordinate—associated with each observed event. Uncertainty in point-level data sets is introduced for many reasons, such as privacy-preserving methods, geocoding algorithms, and data-gathering mechanisms. We introduce a new constrained jittering method and discuss implications for statistical utility and disclosure risk.

Claire Kelling is an Assistant Professor of Statistics at Carleton College in Northfield, Minnesota. She received her dual PhD in statistics and social data analytics from Penn State. Kelling’s research engages statistics, sociology, and data science in order to study and develop statistical methods to inform evidence-based policy.

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