空间显式缩小和对中国大陆人口的预测
Wenru Xu1, Yuyu Zhou2, Hannes Taubenböck3
1Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China.
The Science of the total environment
|May 30, 2024
概括
本研究引入了详细,空间显式人口数据的新框架,通过考虑城市化和不同的城市/农村人口来提高准确性. 这增强了对人与自然相互作用的理解,并为可持续的政策决策提供了信息.
科学领域:
- 人口统计学 人口统计学
- 地理空间分析是什么
- 环境科学 环境科学
背景情况:
- 准确的空间显式人口数据对于人与自然的相互作用研究和政策制定至关重要.
- 现有的全球人口数据缺乏空间细节,忽视城市化动态,并且具有粗略的分辨率.
- 这些限制阻碍了有效的环境管理和风险评估.
研究的目的:
- 开发一个大规模,空间显式人口缩小和未来预测的框架.
- 通过结合城市化和独特的城乡动态来解决现有人口数据的局限性.
- 为各种社会环境研究提供高分辨率的人口数据.
主要方法:
- 开发了一个缩小人口普查数据的框架,以获得更好的空间分辨率.
- 城市和农村人口分别缩小规模,占城市扩张.
- 在共享社会经济路径 (SSP) 场景下使用随机森林模型进行历史缩小和基于重力模型进行未来预测.
主要成果:
- 成功缩小历史人口规模,增强空间和时间细节.
- 考虑到明显的城乡变化和城市扩张,预计未来的人口分布.
- 创造了一个新的能力来分析跨时间,空间和场景维度的空间显式人口变化.
结论:
- 拟议的框架显著提高了人口数据的空间明确性和准确性.
- 这种方法支持对长期社会环境相互作用的跨学科研究.
- 这些发现为更明智的可持续发展和政策决策提供了基础.
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