A Shared Component Point Process Model for Urban Policing

03/01/2023
by   Claire Kelling, et al.
0

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 of one of the two point processes. 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, and we illustrate this flexibility in simulated examples and an application to Chicago policing data.

READ FULL TEXT

page 5

page 6

page 14

page 22

page 23

research
03/23/2020

Approximate Bayesian inference for a spatial point process model exhibiting regularity and random aggregation

In this paper, we propose a doubly stochastic spatial point process mode...
research
08/10/2021

A Two-Stage Cox Process Model with Spatial and Nonspatial Covariates

There are rich new marked point process data that allow researchers to s...
research
02/11/2021

Mutually exciting point process graphs for modelling dynamic networks

A new class of models for dynamic networks is proposed, called mutually ...
research
06/18/2019

A spatial dependence graph model for multivariate spatial hybrid processes

This paper is concerned with the joint analysis of multivariate mixed-ty...
research
06/01/2023

Inference and Sampling of Point Processes from Diffusion Excursions

Point processes often have a natural interpretation with respect to a co...
research
11/27/2017

Characterising Dependency in Computer Networks using Spectral Coherence

The transmission or reception of packets passing between computers can b...
research
03/28/2022

Information Theory and Point Processes

This paper addresses theoretically correct vs. incorrect ways to apply i...

Please sign up or login with your details

Forgot password? Click here to reset