Investigation of the Effects of Spatial Jittering and Uncertainty on Point Process Inference with an Application in Urban Crime

Point process models rely on the availability of point-level data, or the precise location (ex: latitude/longitude coordinates) 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. Privacy-preserving methods, such as radial perturbation, purposefully move points to allow for protection of the original location. Geocoding, the process of transforming addresses into coordinates, often introduces uncertainty into the geocoded point due to technological limitations. Datasets collected from news articles allow for novel analyses of challenging problems but also can lead to less precise point locations of events. We analyze the impact of uncertainty in point locations and propose measures that analysts can take to address this uncertainty. We focus our discussion on jittered crime data in the city of Cincinnati and simulated cases.

spatial jittering

point process

geographic masking

geocoding

criminology

spatial statistics 

Mid-Level

Knowledge

Women in Statistics and Data Science 2022