On Spatiotemporal Patterns in Urban Violent Crime

We're sorry. Some content is restricted until Monday 1st of June, 2026. (See file details below.)
Description
"Urban crime, as understood in modern criminology is a sociological, and further a sociogenic phenomenon governed by complex, dynamic processes. Characteristics of the civic, social, and urban environment influence where and when crime events oc- cur; however, past studies often analyse cross-sectional data for one spatial scale and do not account for the processes and place-based policies that influence crime across multiple scales. This thesis builds upon a curated dataset for studying reported crime incidents in context over the last 10 years in the City of Chicago, and (i) applies a Bayesian cross-classified multilevel modelling approach to examine the spatiotemporal patterning of violent crime in the city; and (ii) motivates directions at the intersection of Bayesian computation and data assimilation for supplementing theoretical work in mathematical criminology with observed. We utilise a grid-based and subsequently a stochastic partial differential equations (SPDE) approach to the modelling of violent crime incidents as Gaussian spatial processes and elucidate the computational infras- tructure that enables sparse approximations to solution Gaussian fields of the said SPDE such that Bayesian inference is computationally feasible."

In collections

File details
ID Label Size Mimetype Created
OBJ OBJ datastream * 5.79 MiB application/pdf 2021-06-01
TN TN 3.18 KiB image/jpeg 2021-06-01
* We're sorry. Some content is restricted until Monday 1st of June, 2026.
Contact Archives and Special Collections with questions at [email protected]