Name: Outbreak Detection in Terrorism
Start: 2017-12-07T18:30:00Z
End: 2017-12-07T20:00:00Z
Location: Facebook

Speaker: Peter Brennan

Venue Capacity: 60 people

The capacity of the venue is 60 people but because of no-shows, we have a policy of first-come, first-served on the night in the event of the room filling up. Managing no-shows is a problem all Meetups have to deal with and this is the fairest method we can think of. So please arrive early to avoid disappointment on the night.

The project goal is to identify outbreaks or 'unusual time events' associated with time series data pertaining to terrorist events. A detailed investigation of the GTD (Global terrorism database) was undertaken.

The aim of the research was to be able to detect early onset of a rise or out breaks in terrorism. Such information would be useful for governments, businesses (such as re-insurers) and risk management professionals. A number of modelling and data visualization techniques were used to gain an understanding of temporal, spatio-temporal and region-temporal terrorist behavior.

Key methodologies were identified for analysis of interesting ‘time series count events’ of terrorist related metrics (such as count of incidents or deaths due to terrorism). Interesting time series events would encompass mean shifts or gradual shifts, outbreaks and anomalies.

Using a mix of algorithmic techniques from syndromic surveillance methods to time series outlier detection methods, outbreaks of terrorism, mean shifts or gradual shifts in terrorist behaviors are found and correlated with real world events.

Mick Cooney

Dublin Data Science

Technology

Artificial Intelligence

Open Source

Data Management

Business Intelligence

Hadoop

Big Data

Machine Learning

Data Mining

NoSQL

Data Analytics

Data Visualization

Predictive Analytics

Data Science

Applied Statistics

Outbreak Detection in Terrorism

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Dublin Data Science

Outbreak Detection in Terrorism

Dublin Data Science

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