Identifying Spatial Distribution of Urban Vitality Using Self-Organizing Feature Map Neural Network

As a vital component of urban planning, urban vitality profoundly affects the sustainable development and well-being of cities. Existing evaluation methods struggle to effectively explain the spatial distribution between nonlinear indicators while simultaneously considering geographical location and spatial attributes. How do we propose a research framework to address this nonlinear spatial distribution? This question is crucial for the study of urban vitality. To bridge this research gap, this paper proposes an SOFM neural network utilizing multisource geospatial big data to explore the spatial distribution of urban vitality. Our results showed the following: (1) Urban vitality in the five dimensions of concentration, functional diversity, contact opportunity, accessibility, and distance from border vacuums decreased from the core area to the periphery, except for building diversity, which exhibited an opposite trend. (2) The urban vitality of Beijing’s central areas primarily showed a circled spatial structure and extended along the Beijing Central Axis and Chang’an Avenue. Additionally, a 15 km radius serves as a significant threshold, encompassing clusters 0, 1, and 2, which align with an important circle delineated by the Master Plan of Beijing (2016–2035). The findings of our research serve as valuable insights for enhancing urban vitality and urban planning.