Identifying possible hotspots of crime in a city is an important issue for urban safety development and can help the authorities take necessary steps to make the city safer for its residents. The effectiveness of such preventive measures depends on the accuracy of the predictions, which are increasingly being made by artificial intelligence (AI)-based models. Most existing models use subjective perceptions of safe locations, socioeconomic status, and still images of crime scenes, and only a few violent crimes are categorized as input data. As a result, there is often a discrepancy between their predictions and reality.
In a new study published in AAAI Conference on Artificial Intelligence, researchers from the Gwangju Institute of Science and Technology (GIST) in South Korea proposed a different strategy based on a large-scale dataset and the concept of “deviance,” which included not only violent crimes but also civil complaints regarding behaviors violating social norms, which is also called “deviant behavior.”
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