Eyes on the Streets: Leveraging Street-Level Imaging to Model Urban
  Crime Dynamics
Zhixuan Qi Huaiying Luo Chen Chi
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.