填充空白:与高斯过程和社交媒体数据的空间插值
Salvatore Giorgi1, Johannes C Eichstaedt2, Daniel Preoţiuc-Pietro3
1Department of Computer and Information Science, University of Pennsylvania, United States of America.
概括
研究人员现在可以通过将调查数据与社交媒体语言相结合,在更多的美国县估计生活满意度. 这种使用高斯过程 (GP) 的方法提高了空间覆盖率,并保持了心理变量的准确性.
科学领域:
- 计算社会科学 计算社会科学
- 心理测量 心理测量
- 地理空间分析是什么
背景情况:
- 调查数据往往缺乏对心理变量进行细致的地理覆盖.
- 像盖洛普-医疗健康健康福利指数这样的大规模调查提供了有限的县级估计.
- 对调查数据的高响应值产生了偏见和减少的人口样本.
研究的目的:
- 开发用于插曲心理变量空间估计的原则方法.
- 评估地理标记的社交媒体数据在提高插值精度方面的实用性.
- 将生活满意度估计的空间覆盖范围扩展到更多的美国县.
主要方法:
- 利用高斯过程 (GP),贝叶斯建模方法,用于空间插值.
- 结合有限的调查数据与大规模的地理标记社交媒体 (Twitter) 语言数据.
- 评估了跨地理,社会经济和基于语言的空间的插值性能.
主要成果:
- 高斯过程 (GP) 有效地插入了对相当多的县的生活满意度估计.
- 与传统的社会人口和地理措施相比,地理标记的Twitter语言使用提高了插值准确性.
- 增强的方法保持了与外部标准的融合有效性,将可靠的估计扩展到2,954个县.
结论:
- 贝叶斯技术,特别是高斯过程 (GP),可以可靠地扩展心理变量的空间覆盖范围.
- 地理标记的社交媒体语言为文化相似性提供了有价值的见解,增强了传统数据.
- 开源工具可用于促进研究人员采用这些先进的插值方法.
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