This book presents an innovative spatial framework for understanding and simulating urban residents’ safety perception using street-level imagery and deep learning. Bridging GIScience, environmental criminology, and urban informatics, it explores how micro-scale urban environments shape fear of crime and perceived safety, offering both theoretical insights and practical modeling tools. Leveraging massive Baidu Street View datasets, convolutional neural networks, and spatial regression techniques, this book uncovers how visual features—such as greenery, lighting, cleanliness, and built structure—affect safety perception at fine spatial scales. It further integrates survey data, crime records, and machine learning to simulate perceived safety across neighborhoods. Designed for researchers and professionals in GIS, urban planning, public health, environmental psychology, and smart city development, this book is suitable for advanced students and interdisciplinary scholars seeking new methods in spatial perception modeling.

Dr. Fengrui Jing is an assistant professor in the Department of Public Health and Preventive Medicine, School of Medicine, at Jinan University, China. He holds a Ph.D. in Geographic Information Science (GIS), with research interests spanning urban safety perception, spatial behavior, and geospatial artificial intelligence (GeoAI). His doctoral research focuses on modeling residents’ perceived safety using street view imagery, deep learning, and spatial simulation techniques, with empirical work conducted in Guangzhou. Dr. Jing’s interdisciplinary work integrates spatial analysis, computer vision, environmental criminology, and urban informatics. He has published in leading international journals such as Cities, Health & Place, and. Environment and Planning B: Urban Analytics and City. His research leverages large-scale urban data, including street view images, POIs, mobile phone trajectories, and crime records, to reveal the microenvironmental mechanisms of urban safety and inform evidence-based policy.