在基于模拟数据和德国COVID-19感染预测的地理空间研究中,为XGBoost进行全球或本地建模
1Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, 10587, Berlin, Germany. ximeng.cheng@hhi.fraunhofer.de.
Scientific reports
|March 15, 2025
概括
对于使用XGBoost进行的地理空间研究,全球建模通常更好,空间变化较低. 当局建模在空间变化较高时,为并行计算提供了更多的稳定性和潜力.
科学领域:
- 地理空间分析是什么?
- 人工智能的人工智能是人工智能.
- 机器学习是机器学习.
背景情况:
- 由于空间异质性,地理空间研究传统上建模了局部同质区域.
- 传统的方法难以有效地挖掘大型数据集.
- 包括机器学习在内的人工智能 (AI) 模型可以处理大量的数据.
研究的目的:
- 调查单个全球或多个本地XGBoost模型是否优于地理空间研究.
- 为了比较全球与本地XGBoost建模方法的性能.
- 评估空间变化的对模型选择的影响.
主要方法:
- 使用了XGBoost机器学习算法.
- 对比全球和地方建模策略.
- 在模拟数据和来自德国的真实世界COVID-19感染数据上评估模型.
主要成果:
- 当数据关系和值范围具有较低的空间变化时,全球XGBoost建模是可取的.
- 局部XGBoost建模显示出更高的稳定性和更高的性能,具有较高的空间变化,特别是对于二次数据.
- 局部建模允许并行计算,但需要仔细的空间分区.
结论:
- 在地理空间分析中,全球和本地XGBoost模型之间的选择取决于数据空间变化的程度.
- 局部建模为复杂的空间异质性提供了更强大的解决方案.
- 优化空间分区对于有效的局部建模至关重要.
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