Urban Safety Perception in the Age of Big Data
Modeling Residents’ Perceived Safety with Street View Imagery, Deep Learning, and Spatial Simulation
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Beschreibung
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.
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.
Provides model urban safety perception using street view imagery and deep learning in the Chinese context Assess perceived safety at both individual and community levels Offers a scalable, AI-enabled workflow for extracting and quantifying visual cues of safety from real-world urban scenes
Autor*in
Fengrui Jing
Themen in »Urban Safety Perception in the Age of Big Data«
Urban safety perception modeling Street view imagery and deep learning Microenvironment and fear of crime Spatial simulation of perceived safety Street-level urban environment analysis Deep learning in urban studies Machine learning for safety mapping Environmental disorder and safety perception Geospatial analysis of public safety CPTED and urban safety analytics