Eyes on the Streets: Leveraging Street-Level Imaging to Model Urban Crime Dynamics
It addresses urban safety for city planners and policymakers, but is incremental as it applies existing methods to a new dataset.
This study tackled the problem of urban safety in New York City by examining how street-level images correlate with crime rates, finding that built environment characteristics can provide insights for crime prevention and urban planning.
This study addresses the challenge of urban safety in New York City by examining the relationship between the built environment and crime rates using machine learning and a comprehensive dataset of street view images. We aim to identify how urban landscapes correlate with crime statistics, focusing on the characteristics of street views and their association with crime rates. The findings offer insights for urban planning and crime prevention, highlighting the potential of environmental design in enhancing public safety.