Predictive Spatiotemporal Modeling

Document Type

Presentation Abstract

Presentation Date

4-24-2017

Abstract

Statistical modeling is often used for one of two distinct paradigms: explanatory or predictive inference. Data science or predictive analytics based applications are often concerned with prediction. With an emphasis on methodology for predictive inference in spatiotemporal settings, this talk will provide an overview of a multiscale spatiotemporal framework developed to predict outbreaks of social unrest in Central and South America. Civil unrest is a complicated, multifaceted social phenomenon that is difficult to forecast. Relevant data for predicting future protests consist of a massive set of heterogenous data sources, primarily from social media. A modular approach to extract pertinent information from disparate data sources is implemented to develop a Bayesian multiscale framework to fuse prediction from algorithms mining social media.

Additional Details

Monday, April 24, 2017 at 3:00 p.m. in Math 103
Refreshments at 4:00 p.m. in Math Lounge 109

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