Activity Number:
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538
- SPEED: Predictive Analytics with Social/Behavioral Science Applications: Spatial Modeling, Education Assessment, Population Behavior, and the Use of Multiple Data Sources
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2018 : 10:30 AM to 11:15 AM
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Sponsor:
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Social Statistics Section
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Abstract #332619
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Title:
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A Spatially Correlated Auto-Regressive Model for Count Data with Applications for Modeling Crime
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Author(s):
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Nicholas Clark* and Philip M Dixon
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Companies:
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Iowa State University and Iowa State University
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Keywords:
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Burglaries;
Space-Time Correlation;
Bayesian;
Self-Exciting;
Markov
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Abstract:
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The statistical modeling of multivariate count data observed on a space-time lattice has generally focused on using a hierarchical modeling approach where space-time correlation structure is placed on a continuous, unobservable, process. The count distribution is then assumed to be conditionally independent given the latent process. However, in many real-world applications, especially in the modeling of criminal or terrorism data, the conditional independence between the count distributions is inappropriate. We propose a class of models that extends the INGARCH process to account for small scale spatial variation, which we refer to as a SPINGARCH process. The resulting model allows both data model dependence as well as dependence in a latent structure. We demonstrate how second-order properties can be used to differentiate between models in this class. We apply Bayesian inference for the SPINGARCH process demonstrating its use in modeling the spatio-temporal structure of burglaries in Chicago from 2010-2015 and demonstrate how accounting for spatial correlation changes the conclusion on the existence of repeat victimization.
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Authors who are presenting talks have a * after their name.
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