Prediction of crime occurrence from multi-modal data using deep learning.

ABSTRACT: In recent years, various studies have been conducted on the prediction of crime occurrences. This predictive capability is intended to assist in crime prevention by facilitating effective implementation of police patrols. Previous studies have used data from multiple domains such as demographics, economics, and education. Their prediction models treat data from different domains equally. These methods have problems in crime occurrence prediction, such as difficulty in discovering highly nonlinear relationships, redundancies, and dependencies between multiple datasets. In order to enhance crime prediction models, we consider environmental context information, such as broken windows theory and crime prevention through environmental design. In this paper, we propose a feature-level data fusio

SUBMITTER: Kang HW 

PROVIDER: S-EPMC5402948 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature