一个数据驱动的全球观测站,通过文本挖掘和复杂的数据可视化来应对全球挑战
Joao Costa1,2, M Besher Massri2,3, Marko Grobelnik1,2,3
1Quintelligence, Ljubljana, Slovenia.
Open research Europe
|September 2, 2024
概括
一个新的全球观测方法集成了多样化的数据来源,以全面地了解全球挑战. 这种方法有助于理解诸如流行病情报和气候变化影响等复杂问题.
科学领域:
- 环境科学 环境科学
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 流行病和气候变化等全球性挑战需要综合,大规模监测.
- 现有的数据源往往是孤立的,阻碍了全面的理解.
- 需要采用统一的方法来将当地问题与全球趋势联系起来.
研究的目的:
- 提出一个数据驱动的全球观测系统方法.
- 整合不同的数据视角 (媒体,科学,统计,传感).
- 展示该观察站在流行病情报和水资源管理方面的应用.
主要方法:
- 使用异质数据源和文本挖掘算法.
- 开发一个数据驱动的全球观测机构框架.
- 与市政,公用事业和政府的利益相关者进行接触.
主要成果:
- 该方法有效地整合了全球问题监测的多视角数据.
- 成功应用证明了COVID-19大流行影响和水资源管理.
- 该观察站为当地环境和优先事项提供了有价值的见解.
结论:
- 一个数据驱动的全球观测站为理解复杂,相互关联的全球问题提供了一个强大的框架.
- 这种综合方法增强了流行病情报和气候变化适应战略.
- 利益相关者的参与对于全球解决方案的成功实施和当地相关性至关重要.
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