Objectives We illustrate how a machine learning algorithm, Random Forests, can provide
accurate long-term predictions of crime at micro places relative to other popular techniques …

This accessible, alphabetical guide provides concise insights into a variety of digital
research methods, incorporating introductory knowledge with practical application and …

Machine learning is useful for grid-based crime prediction. Many previous studies have
examined factors including time, space, and type of crime, but the geographic characteristics …

Research identifies various place features (eg, bars, schools, public transportation stops)
that generate or attract crime. What is less clear is how the spatial influence of these place …

Sporting events attract high volumes of people, which in turn leads to increased use of social
media. In addition, research shows that sporting events may trigger violent behavior that can …

Near repeat analysis has been increasingly used to measure the spatiotemporal clustering
of crime in contemporary criminology. Despite its predictive capacity, the typically short time …

Geographic forecasting of crime can be done by considering prior crime or by considering
spatial risk factors, eg, using risk terrain modeling (RTM). The present paper tests both …

Purpose The current study investigates the application of risk terrain modeling (RTM) to
forecast gang violence. RTM is routinely utilized to predict future criminal events in micro …

This research focused on the effect of the built environment on Bogotá's violent crime by
using the Risk Terrain Modeling (RTM) technique. The current study used 17 ecological …

The law of crime concentration at place has become a criminological axiom and the
foundation for one of the strongest evidence-based policing strategies to date. Using …