Neighborhood Level

Neighborhood-level analysis focuses on understanding the characteristics and interactions within geographic areas, leveraging diverse data sources and computational methods to achieve this. Current research employs various machine learning models, including graph neural networks, multilayer perceptrons, and diffusion models, to analyze spatial data, predict neighborhood attributes (e.g., crime rates, mental health risks), and optimize resource allocation. These studies are significant for informing urban planning, public health interventions, and the development of more efficient and privacy-preserving technologies, particularly in areas like autonomous driving and personalized services.