Modeling and predicting urban crime – How data assimilation helps bridge the gap between stochastic and continuous models

GT-MAP Seminars
Friday, October 20, 2017 - 3:00pm
2 hours
Skiles 006
GT Math
Data assimilation is a powerful tool for combining mathematical models with real-world data to make better predictions and estimate the state and/or parameters of dynamical systems. In this talk I will give an overview of some work on models for predicting urban crime patterns, ranging from stochastic models to differential equations. I will then present some work on data assimilation techniques that have been developed and applied for this problem, so that these models can be joined with real data for purposes of model fitting and crime forecasting.