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

大数据市场目前价值1400亿美元，到2025年，预计将拥有约175 zttabytes （1zb = 1e12gb）的数据，这些数据需要定期处理和分析以提取信息。本文提出的研究解决了分析大数据（更具体地说是丰富数据）的新挑战，这些数据是原始大数据的清洁、处理和精炼形式。它专注于开发新的多元分析方法，可以在丰富的数据中分析精确的位置和时间信息，提供关于我们周围任何时间点正在或将要发生的事件的知识。例如，在时空（ST）维度上分析丰富的犯罪数据可以帮助检测社区中不同犯罪类型的每小时、每天或每周的进展。因此，可以有针对性地监控犯罪并充分利用我们有限的资源。该项目将创新多结果跨时空联合建模（JMMOST），利用单一模型从丰富的ST数据中分析多个复杂结果（或事件），生成隐藏在丰富数据中的新信息。然而，将数据的空间和时间维度整合到单一模型中可能非常具有挑战性，因为它们具有对比性，而大量丰富的ST数据使其进一步复杂化。这些分析约束将通过贝叶斯时空建模在小区域水平（例如，社区）的应用来解决。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空间和时间数据方面取代了传统的方法。该项目有短期目标，将帮助我们实现完善JMMOST丰富数据分析的长期目标，并在加拿大建立一个支持贝叶斯空间和ST分析的丰富数据空间分析研究中心。短期目标是开发新的JMMOST方法，用于在空间（如定制网格）和时间（如小时）的精细尺度上分析丰富数据，从而更好地捕获丰富数据中的ST变量。长期目标是在稳健性和灵活性方面完善新方法，从而允许实时分析监视系统中具有空间和时间成分的丰富数据。这一点很重要，因为如果没有可靠的方法来分析丰富的数据，加拿大自然科学与工程（NSE）研究的进展可能会因为未能利用不断增长的丰富的ST数据源而停滞不前。该项目将在全球范围内使地理信息科学等NSE领域以及犯罪学和经济学等其他领域受益，使他们能够分析丰富的数据以获得新的信息。通过我目前的NSERC拨款，我已经建立了一个研究团队来完成拟议的研究。在拨款续期后，将培训更多hqp利用加拿大丰富的数据在不同领域开发和应用JMMOST方法。

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.