Planning and layout of tourism and leisure facilities based on POI big data and machine learning

A machine learning approach identifies optimal locations for tourism and leisure facilities along Beijing's Central Axis, achieving 83.5% accuracy in predicting suitable sites based on existing POI data. The study reveals that accommodation availability, shopping, and transportation infrastructure are key factors influencing facility placement, with a notable centralization pattern aligned to the urban core. Predictive analysis suggests new developments should prioritize densely populated areas currently lacking such amenities, offering a data-driven strategy to enhance urban planning while reducing subjectivity in decision-making.

Wu, S., Wang, J., Jia, Y., Yang, J., & Li, J. (2025). Planning and layout of tourism and leisure facilities based on POI big data and machine learning. Plos One, 20(3), e0298056. https://doi.org/10.1371/journal.pone.0298056