Joint modeling of multiple outcomes over space and time (JMMOST): A Bayesian approach

空间和时间上多种结果的联合建模 (JMMOST)：贝叶斯方法

• 批准号: RGPIN-2022-03740
• 负责人: Law, Jane
• 金额: $ 2.62万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=763528
• 关键词: Joint; modeling; multiple; outcomes; over

The Big Data market is currently valued at 140 billion USD and, by 2025, is expected to hold about 175 Zettabytes (1 ZB = 1e12 GB) of data that will require regular processing and analysis for information extraction. The proposed research addresses this emerging challenge of analyzing big data, more specifically rich data, which are cleaned, processed, and refined forms of raw-big data. It focuses on developing novel multivariate analytical methods that can analyze the precise location and time information in rich data, providing knowledge about events that are or will be happening around us at any point in time. For example, analyzing rich crime data at space-time (ST) dimensions can help detect the hourly, daily, or weekly progression of different crime types in neighborhoods. Thus, allowing targeted crime monitoring and the best use of our finite resources. The project will innovate the joint modeling of multiple outcomes over space and time (JMMOST) to analyze multiple complex outcomes (or events) from rich ST data using a single model for generating new information hidden in rich data. However, integrating space and time dimensions of data in a single model can be highly challenging due to their contrasting nature, which is further complicated by the large volume of rich ST data. These analytical constraints will be addressed through the application of Bayesian spatiotemporal modeling at a small-area level (e.g., neighborhoods). The selection of the Bayesian framework is based on past research evidence that Bayesian techniques supersede conventional approaches in analyzing space and time data. The project has short-term goals that will help us achieve our long-term goals in perfecting JMMOST for rich data analysis and establishing a Rich Data Spatial Analysis Research Centre in Canada that supports Bayesian spatial and ST analysis. The short-term goals aim to develop novel JMMOST methods for analyzing rich data at a fine--scale of space (e.g., customized grids) and time (e.g., hours), which can better capture the ST variabilities in rich data. The long-term goals aim to perfect the novel methods in terms of robustness and flexibility and thus, allow real-time analysis of rich data with spatial and temporal components for surveillance systems. This is important because, without the availability of reliable methodologies for analyzing rich data, the progress in natural science and engineering (NSE) research in Canada could stall due to the failure to exploit the ever-growing rich ST data sources. The project will globally benefit NSE fields like geographic information science and other fields like criminology and economics, enabling them to analyze their rich data to obtain new information. Through my current NSERC grant, I have established a research team to complete the proposed research. Upon renewal of the grant, more HQPs will be trained to develop and apply JMMOST methods in different fields using Canadian rich data.

目前，大数据市场的价值为1400亿美元，到2025年，预计将持有约175个Zettabytes（1 ZB = 1E12 GB）的数据，这些数据将需要定期处理和分析以进行信息提取。拟议的研究解决了分析大数据的新出现的挑战，这些挑战更具体地是富裕的数据，这些数据是清洁，加工和精制形式的原生数据的挑战。它着重于开发新型的多元分析方法，这些方法可以分析丰富数据中的精确位置和时间信息，提供有关在任何时间点我们周围发生或将要发生的事件的知识。例如，分析时空犯罪数据（ST）维度可以帮助检测社区中不同犯罪类型的每小时，每天或每周进展。因此，允许有针对性的犯罪监测和我们有限资源的最佳利用。 该项目将使用单个模型来分析来自丰富的ST数据的多个复杂结果（或事件），从而在空间和时间上（最多）创新多个结果的联合建模，以生成隐藏在丰富数据中的新信息。但是，单个模型中数据的空间和时间维度的整合由于其对比性质可能会极具挑战性，这与大量丰富的ST数据更加复杂。这些分析限制将通过在小区域（例如社区）上应用贝叶斯时空建模来解决。贝叶斯框架的选择是基于过去的研究证据，表明贝叶斯技术在分析时空数据中取代了常规方法。 该项目具有短期目标，可以帮助我们实现长期目标，以完善JM最丰富的数据分析并在加拿大建立丰富的数据空间分析研究中心，以支持贝叶斯的空间和ST分析。短期目标旨在开发新型的JM大多数方法，用于分析空间（例如，定制的网格）和时间（例如小时），以更好地捕获丰富数据中的ST变异性。长期目标旨在从鲁棒性和灵活性方面完善新的方法，从而允许使用空间和时间成分的监视系统实时分析丰富的数据。这很重要，因为如果没有可靠的方法来分析丰富的数据，加拿大自然科学和工程研究（NSE）研究的进展可能会因为未能利用不断增长的丰富的ST数据源而停滞。该项目将在全球范围内受益于NSE领域，例如地理信息科学和其他领域，例如犯罪学和经济学，使他们能够分析其丰富的数据以获取新信息。 通过当前的NSERC赠款，我建立了一个研究团队来完成拟议的研究。续签赠款后，将对使用加拿大富裕数据在不同领域中开发和应用JM大多数方法进行培训。