Latent-Gaussian Spatio-temporal models for complex problems

复杂问题的潜在高斯时空模型

• 批准号: RGPIN-2017-06856
• 负责人: Brown, Patrick
• 金额: $ 1.75万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=635869
• 关键词: Latent; Gaussian; Spatio; temporal; models

One feature of the `big data’ era is that increasingly large amounts of spatial information are routinely collected and stored in administrative databases. This has enabled researchers to answer questions at a finer spatial scale than was previously possible, for instance examining variation in cancer risk within a city as opposed to between cities. These new data sources and the research questions which accompany them have required advancements to be made in the area of Spatial Statistics, as methods which were well suited to models for 50 health regions often work poorly when applied to data from 10,000 census regions.

“大数据”时代的一个特点是，越来越多的空间信息被例行地收集并存储在行政数据库中。这使得研究人员能够在比以前更精细的空间尺度上回答问题，例如检查城市内癌症风险的差异，而不是城市之间的差异。这些新的数据来源和随之而来的研究问题要求在空间统计领域取得进展，因为适用于50个健康区域模型的方法在应用于10 000个人口普查区域的数据时往往效果不佳。