III-CTX: Collaborative Research: Spatio-Temporal Data Mining For Global Scale Eco-Climatic Data

遥感数据包括对陆地表面、生物圈、固体地球、大气和海洋的卫星观测，结合历史气候记录和生态系统模型的预测，为了解地球如何变化、确定引起这些变化的因素以及预测未来变化提供了新的机会。反过来，这可以提供一个前所未有的机会，通过管理我们星球的生态和健康来预测和预防未来的生态问题。数据挖掘和知识发现技术可以通过发现捕获海洋温度、气压、地表气象和陆地碳通量之间复杂相互作用的模式来帮助这项工作。这项工作的目标有两个：1)更好地理解生物圈过程的全球尺度模式，特别是全球碳循环和气候系统的模式。更具体地说，提出的数据挖掘研究是由解决以下两个挑战的需要驱动的：(i)了解海洋、大气和陆地过程是如何耦合的；（ii）探测和预测生态系统干扰，如火灾、洪水和飓风。2)支持数据挖掘领域的创新计算机科学（CS）研究。特别是，地球科学数据的时空性质意味着通常不能直接应用标准的CS数据挖掘技术。例如，在地球科学研究中，一个关键步骤是选择地点和时间段，用于调查两种地球科学现象之间的可能关系，例如，厄尔尼诺现象和美国中西部较温和的冬天。目前，这种选择是基于领域知识的，但是自动化这个过程将是非常有益的。提出的工作将开发新的数据挖掘技术，以解决数据的高维、大尺寸和时空性质。更广泛的影响：本项目开发的用于分析大型时空数据集的新算法和技术将提供给地球科学界的其他研究人员。在很大程度上，该项目将这些算法封装在易于使用的可视化工具中，以便用户能够更容易地从地球科学数据集中提取有用的知识。虽然重点将放在地球科学数据上，但所开发的数据挖掘技术将适用于在空间网格上随时间收集数据的各种其他领域。举个具体的例子，时空聚类已被用于跟踪气旋和动物迁徙，并用于模拟移动电话用户和大脑中的神经元活动，新的时空聚类技术也可能被证明对这些应用很有用。

ContextThe remote sensing data that consists of satellite observations of the land surface, biosphere, solid Earth, atmosphere, and oceans, combined with historical climate records and predictions from ecosystem models, offers new opportunities for understanding how the Earth is changing, for determining what factors cause these changes, and for predicting future changes. In turn, this could provide an unprecedented opportunity for predicting and preventing future ecological problems by managing the ecology and health of our planet. Data mining and knowledge discovery techniques can aid this effort by discovering patterns that capture complex interactions among ocean temperature, air pressure, surface meteorology, and terrestrial carbon flux. Intellectual MeritThe goals of this work are twofold: 1) to better understand global scale patterns in biosphere processes, particularly patterns in the global carbon cycle and climate system. More specifically, the proposed data mining research is driven by the need to address the following two challenges: (i) understanding how ocean, atmosphere and land processes are coupled and (ii) detecting and predicting ecosystem disturbances such as fires, floods, and hurricanes. 2) to support innovative Computer Science (CS) research in data mining. In particular, the spatio-temporal nature of Earth Science data means that standard CS data mining techniques often cannot be directly applied. As an example, in Earth Science research, a key step is the selection of the locations and time periods that are to be used to investigate possible relationships between two Earth Science phenomena, e.g., El Nino and milder winters in the Midwestern United States. Currently, this selection is based on domain knowledge, but automating this process would be very beneficial. The proposed work will develop new data mining techniques that address the high dimensionality, large size, and spatio-temporal nature of the data. Broader Impacts: New algorithms and techniques for the analysis of large spatio-temporal data sets developed in this project will be made available to other researchers within the Earth Science community. To a large extent, this project will encapsulate these algorithms within easy to use visual tools so that users will be able to more easily extract useful knowledge from Earth Science data sets. Although the focus will be on Earth Science data, the data mining techniques that are developed will be applicable to a wide variety of other fields that have data collected over time on a spatial grid. To give a specific example, spatio-temporal clustering has been used to track cyclones and animal migrations, and to model mobile phone users and neuronal activities in the brain, and the new spatio-temporal clustering techniques could also prove useful for these applications.