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 im- ages. 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 pre- vention, highlighting
the potential of environmental de- sign in enhancing public safety.

本研究利用机器学习和全面的街景图像数据集，探讨纽约市的城市安全挑战，以识别城市景观与犯罪率之间的关系，并特别关注街景特征与犯罪率的相关性，从而为城市规划和犯罪预防提供见解，凸显环境设计在提升公共安全方面的潜力。

街道之眼：利用街头成像来建模城市犯罪动态

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

Inspired by expert evaluation policy for urban perception, we proposed a
novel inverse reinforcement learning (IRL) based framework for predicting urban
safety and recovering the corresponding reward function. We also presented a
scalable state representation method to model the prediction problem as a
Markov decision process (MDP) and use reinforcement learning (RL) to solve the
problem. Additionally, we built a dataset called SmallCity based on the
crowdsourcing method to conduct the research. As far as we know, this is the
first time the IRL approach has been introduced to the urban safety perception
and planning field to help experts quantitatively analyze perceptual features.
Our results showed that IRL has promising prospects in this field. We will
later open-source the crowdsourcing data collection site and the model proposed
in this paper.

通过借鉴城市感知的专家评估政策，我们提出了一种新颖的基于 IRL 的框架来预测城市安全，并恢复相应的奖励函数，使用强化学习 (MDP) 来解决问题，并建立了一个基于众包的数据集 SmallCity 进行了研究。我们的结果表明，IRL 在这个领域有着广阔的前景。