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

Fuente: arXiv
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Autores principales: Qi, Zhixuan, Luo, Huaiying, Chi, Chen
Formato: Preprint
Publicado: 2024
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author Qi, Zhixuan
Luo, Huaiying
Chi, Chen
author_facet Qi, Zhixuan
Luo, Huaiying
Chi, Chen
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2404_10147
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Eyes on the Streets: Leveraging Street-Level Imaging to Model Urban Crime Dynamics
Qi, Zhixuan
Luo, Huaiying
Chi, Chen
Computer Vision and Pattern Recognition
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.
title Eyes on the Streets: Leveraging Street-Level Imaging to Model Urban Crime Dynamics
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2404.10147