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

Urban vitality in Beijing's central areas follows a distinct spatial pattern, decreasing from core to periphery in most dimensions except building diversity. A 15 km radius emerges as a critical threshold, aligning with urban planning circles outlined in the Master Plan of Beijing (2016–2035). This finding was derived using a Self-Organizing Feature Map (SOFM) neural network, which analyzed multisource geospatial big data to reveal nonlinear spatial distributions. The study highlights how urban vitality extends along key axes like the Beijing Central Axis and Chang’an Avenue, offering valuable insights for enhancing urban planning and sustainability.

Cai, X., Wen, C., Wang, H., & Chen, W. (2025). Identifying Spatial Distribution of Urban Vitality Using Self-Organizing Feature Map Neural Network. ISPRS International Journal of Geo-Information, 14(2), 62. https://doi.org/10.3390/ijgi14020062