基于POI大数据和机器学习的旅游和休设施的规划和布局
Shifeng Wu1,2,3, Jiangyun Wang1, Yinuo Jia1
1School of Tourism, Hebei University of Economics and Business, Shijiazhuang, China.
PloS one
|March 4, 2025
概括
机器学习优化了北京的旅游设施布置,识别了合适的新站点. 该方法实现了83.5%的适配度,指导智能城市规划和城市发展.
科学领域:
- 城市规划 城市规划
- 智能旅游是一个智能旅游.
- 地理信息系统 (GIS) 是一个地理信息系统.
背景情况:
- 旅游和休设施的战略位置对于智能旅游城市的发展至关重要.
- 兴趣点 (POI) 数据为城市规划和空间布局优化提供了宝贵的见解.
研究的目的:
- 通过机器学习评估北京城市是否适合新的旅游和休设施.
- 基于POI和人口统计数据,对设施的最佳位置进行定量模拟.
主要方法:
- 机器学习方法应用于POI和人口统计数据.
- 用于决策模型培训的CART算法,用于识别定位影响.
- 对最佳设施位置的定量模拟.
主要成果:
- 机器学习模型实现了83.5%的匹配度,表明了高可行性.
- 住宿,购物和运输对设施的位置有很大的影响.
- 设施展示了一个集中式布局,在人口密集地区预测的新发展和确定服务不足的地点.
结论:
- 机器学习为城市规划中的设施定位提供了一种可行和客观的方法.
- 确定服务不足的地区为旅游和休设施的战略发展提供了机会.
- 该研究提供了提高城市布局和减轻规划主观性的见解.
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