November 30, 2019

320 words 2 mins read

AI-powered crime prediction

AI-powered crime prediction

What if we could predict when and where crimes will be committed? Or Herman-Saffar and Ran Taig offer an overview of Crimes in Chicago, a publicly published dataset of reported incidents of crime that have occurred in Chicago since 2001. Or and Ran explain how to use this data to explore committed crimes to find interesting trends and make predictions for the future.
Talk Title AI-powered crime prediction
Speakers Or Herman-Saffar (Dell), Ran Taig (Dell EMC)
Conference Strata Data Conference
Conf Tag Big Data Expo
Location San Jose, California
Date March 6-8, 2018
URL Talk Page
Slides Talk Slides
Video

What if we could predict when and where crimes will be committed? Crimes in Chicago, a publicly published dataset of reported incidents of crime that have occurred in Chicago since 2001, contains as many as 6.4 million rows, and each row includes crime type, geographical location, and date and time when the crime occurred. This extensive data source is very valuable and can form the basis for a machine learning model. One direct and immediate motivation for the dataset is making crime counts predictions for specific crimes, which would assist the police in deciding which areas and times to increase their resources, having a concrete impact on citizens’ safety. However, previous work done on this dataset has been mostly descriptive—explorations made at a high level of the current state and counts (i.e., how many crimes have been committed up to a specific point in time)—rather than focused on predictive models. Or Herman-Saffar and Ran Taig offer an overview of Crimes in Chicago and explain how to use this data to explore committed crimes to find interesting trends and make predictions for the future. Or and Ran conclude by exploring the development of a machine learning model that predicts crime counts for specific crime type on a given day in a specific district within Chicago and cover lessons and insights learned.

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