使用POI和多源卫星数据集对中国大陆的人口空间化和基于区域异质性的时空变化进行空间化
1College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China.
The Science of the total environment
|December 21, 2023
概括
这项研究为中国开发了一个高分辨率的500m格式人口数据集. 新的数据集使用遥感和兴趣点 (POI) 数据,提高了对现有的全球数据集的准确性.
科学领域:
- 地理空间科学是一个科学领域.
- 遥感是一种远程传感.
- 人口研究是指人口研究.
背景情况:
- 人口空间化依赖于地理空间和遥感数据,但关系因地区而异.
- 对于具有显著地理差异的地区,现有方法需要改进.
- 区域异质性需要量身定制的数据处理和空间化模型.
研究的目的:
- 在500米分辨率下为中国开发精确的网格化人口分布数据集.
- 评估辅助数据 (土地覆盖面,夜间照明,POI) 在不同地理环境中的重要性.
- 为探索人口的时空变化提供可靠的数据集.
主要方法:
- 利用土地覆盖面,夜间照明和兴趣点 (POI) 数据来表示人类活动和强度.
- 根据地理背景将中国大陆分为北方,南方和西部地区.
- 构建随机森林模型以生成网格化人口数据集和评估数据重要性.
主要成果:
- 与街头水平的WorldPop相比,开发的人口数据集的准确性更高 (R2更高,偏差更小).
- 人口密度和定居区在2010-2020年间增加,总体分布模式稳定.
- 辅助数据的重要性因地区而异;POI数据在南部地区的影响最大,在西部地区影响最小.
结论:
- 改进的网格化人口数据集为中国的时空分析提供了重大潜力.
- 地理背景的区域差异显著影响了辅助数据在人口空间化中的有效性.
- 该研究提供了一个有价值的参考,用于预测使用多源卫星和POI数据在不同地理区域的社会经济数据.
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