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A Social Approach to Crime Prediction

A/Prof Flora Salim is leading a range of projects using social media data to predict human activity, including crime.

A/Prof Flora Salim is leading a range of projects using social media data to predict human activity, including crime.

By Shakila Khan Rumi

Computers can be trained to analyse location information generated by social media users to predict the likely time and place of specific crimes.

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Imagine a program that could tell you where and when crimes were about to happen. While it may sound far-fetched, a recent study I published in EPJ Data Science (https://bit.ly/2Vlei3L) with Dr Ke Deng and A/Prof Flora Salim at RMIT University has used location and activity data from Foursquare social media app users in New York City and Brisbane to predict specific types of crime better than any other existing method.

The Foursquare app, which peaked in popularity 5–6 years ago with over 45 million users, allowed people to share their location and activity by checking-in at various places. For this study we gathered data from over 20,000 check-ins by users in Brisbane, and nearly 230,000 check-ins by users in New York City.

The large majority of people in the cities we studied were not using the app, and those who were committing crimes were likely not posting on the app about it, so obviously it’s not as simple as waiting for people to check in at a murder scene or post their intentions to commit crimes in their status update. What we actually do is use the location data from the mobile phones to understand the flow of people and types of activity around a city at any given moment, as this correlates with the likelihood of crimes being committed. This same type of location data are captured...

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