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Abstract:
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Newly available point-level datasets allow us to relate police use of force to other events describing police behavior. Current methods for relating two point processes typically rely on the spatial aggregation. However, when we aggregate point-level data we lose valuable information about the distribution of the points over space as well as event-level information, such as the category of crime for a given event. We investigate new methods that build upon shared component models and case-control methods to retain the point-level nature of both point processes while characterizing the relationship between them. We find that the shared component approach is particularly useful in flexibly relating two point processes, allowing researchers to create a detailed analysis of the relationship between multiple policing datasets. In an application to Chicago policing datasets, we find that our shared component approach allows us to characterize a spatial pattern that is common between police stops and use of force. Our framework also simultaneously identifies the potential influence of community socioeconomic factors on the spatial pattern that is unique to both point processes.
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