Carleton College

Leaps-mps: Development of Models in Spatial Statistics for Complex Policing and Social Science Applications -pressing Research Concerns in the Social Sciences Often Require the Analysis of Spatial Data, Which Has Become Increasingly Complex. This Research Aims to Better Understand and Model Some of These Complexities With Innovative Statistical Methodology That Will Allow the Pi, Other Researchers, and Community Members to Better Answer Research Questions Relevant to Public Policy. the Pi Also Will Host a Forum at Carleton College in Fall 2023 on Statistical Challenges in the Analysis of Police Use of Force. This Work Will Have Immediate Impacts on the Undergraduates at Carleton College Who Participate in This Research Process, the Students Outside Carleton Who Attend the Statistical Forum on Policing, and the Local Minneapolis Community Through Intentional Partnership With Community Organizations. the Proposed Work Will Help Those in Local Communities Answer Specific Questions About the Policing of Their Neighborhoods, Allowing Them to Add Additional Context From Data to Their Lived Experiences. This Project Will Develop New Statistical Methodology With Three Specific Aims: Methods to Address and Account for Spatial Uncertainty, Methods to Incorporate Bounded or Constrained Actors Into Spatial Models, and Spatio-temporal Extensions of Preliminary Spatial Models. the First Aim Will Develop Software to Implement a Constrained Spatial Privacy Method That Protects the Original Locations While Preserving the Statistical Utility of the Original Dataset. New Methodology Will Be Proposed to Analyze Spatial Uncertainty Due to Both Structured (privacy) and Unstructured (geocoding Error or Imprecise Data Collection) Processes. the Second Aim Will Develop Research Methods to Incorporate Actors That Are Spatially Constrained Both Due to Structured (e.g., Police Jurisdictions) and Inferred (regional Unions) Boundaries. the Third Aim Will Extend a Newly Developed Spatial Model, Which Is a Shared Component Model for Characterizing the Relationship Between Two Point Processes, to a Spatio-temporal Model, Incorporating Temporal Dynamics as Well. This Award Reflects NSF'S Statutory Mission and Has Been Deemed Worthy of Support Through Evaluation Using the Foundation's Intellectual Merit and Broader Impacts Review Criteria.