Partially Generative Neural Networks for Gang Crime Classification with Partial Information

By Sungyong Seo, Hau Chan, P. Jeffrey Brantingham, Jorja Leap, Phebe Vayanos, Milind Tambe, Yan Liu. 2018

16 citations recorded by the source corpus.

More than 1 million homicides, robberies, and aggravated assaults occur in the United States each year. These crimes are often further classified into different types based on the circumstances surrounding the crime (e.g., domestic violence, gang-related). Despite recent technological advances in AI and machine learning, these additional classification tasks are still done manually by specially trained police officers. In this paper, we provide the first attempt to develop a more automatic system for classifying crimes. In particular, we study the question of classifying whether a given violent crime is gang-related. We introduce a novel Partially Generative Neural Networks (PGNN) that is able to accurately classify gang-related crimes both when full information is available and when there is only partial information. Our PGNN is the first generative-classification model that enables to work when some features of the test examples are missing. Using a crime event dataset from Los Angeles covering 2014-2016, we experimentally show that our PGNN outperforms all other typically used classifiers for the problem of classifying gang-related violent crimes.

Subfields: Artificial Intelligence, Sociology and Political Science, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ben Hutchinson
2. Vinodkumar Prabhakaran

In Sociology and Political Science

1. Vinodkumar Prabhakaran
2. Ben Hutchinson
3. Nithya Sambasivan

In Natural Language Processing

1. Ben Hutchinson
2. Nithya Sambasivan
3. Vinodkumar Prabhakaran

Citations in the corpus

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• Data Science as Political Action: Grounding Data Science in a Politics of Justice (2018, unknown): “One example of this work used machine learning to predict which police officers will be involved in adverse events such as racial profiling or inappropriate use of force[56].”