Emerging Pattern Detection

Model by Booz Allen

This model observes patterns in a spatiotemporal dataset and identifies time periods and locations in the dataset where the pattern changes from usual behavior. The model accepts time series data with geographic coordinates and forms a geographic grid of the observed area. The model analyzes timeframes of the gridded data and identifies timeframes where the distribution of observations in the grid varies from normal. We use a Local Outlier Factor method to determine how well a grid distribution clusters with other distributions. This model can be used to analyze spatiotemporal data in multiple ways, such as identifying days where city traffic was greatly altered from a citywide perspective down to a granular city block perspective.

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