Eyes on the Streets: Leveraging Street-Level Imaging to Model Urban Crime Dynamics

TL;DR

This paper explores how street-level images can be used with machine learning to understand and predict urban crime patterns in New York City, providing insights for urban planning and safety improvements.

Contribution

It introduces a novel approach combining street view imagery and machine learning to analyze the relationship between urban environment features and crime rates.

Findings

Certain urban landscape features correlate strongly with crime rates.

Street view imagery can effectively predict areas with higher crime risk.

Insights support environmental design strategies for crime prevention.

Abstract

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.