Corpus ID: 208527747

Crime in Philadelphia: Bayesian Clustering with Particle Optimization

@article{Balocchi2019CrimeIP,
  title={Crime in Philadelphia: Bayesian Clustering with Particle Optimization},
  author={Cecilia Balocchi and Sameer K. Deshpande and Edward I. George and Shane T. Jensen},
  journal={arXiv: Applications},
  year={2019}
}
Accurate estimation of the change in crime over time is a critical first step towards better understanding of public safety in large urban environments. Bayesian hierarchical modeling is a natural way to study spatial variation in urban crime dynamics at the neighborhood level, since it facilitates principled "sharing of information"S between spatially adjacent neighborhoods. Typically, however, cities contain many physical and social boundaries that may manifest as spatial discontinuities in… Expand

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