Bayesian estimation and prediction for inhomogenous spatio-temporal log-Gaussian Cox processes using low-rank models, with application to criminal surveillance

Rodrigues, R. and Diggle, P. (2012) Bayesian estimation and prediction for inhomogenous spatio-temporal log-Gaussian Cox processes using low-rank models, with application to criminal surveillance. Journal of the American Statistical Association, n/a (n/a). ISSN 1537-274X (In Press)

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Official URL: http://amstat.tandfonline.com/doi/full/10.1080/016...

Abstract

In this paper we propose a method for conducting likelihood-based inference for a class of non-stationary spatio-temporal log-Gaussian Cox processes. The method uses convolution-based models to capture spatio-temporal correlation structure, is computationally feasible even for large datasets and does not require knowledge of the underlying spatial intensity of the process. We describe an application to a surveillance system for detecting emergent spatio-temporal clusters of homicides in Belo Horizonte, Brazil, and discuss the advantages and drawbacks of our model-based approach by comparison with other spatio-temporal surveillance methods that have been proposed in the literature.

Item Type:Article
Uncontrolled Keywords:convolution-based model, likelihood-based inference, spatio-temporal process, surveillance system
Subjects:5. Quantitative Data Handling and Data Analysis > 5.9 Spatial Data Analysis
ID Code:2279
Deposited By: L-W-S user
Deposited On:25 Apr 2012 10:05
Last Modified:25 Apr 2012 10:05

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