CRII: III: Discovering Complex Mixture Patterns in Spatial Data to Advance Resilience of Communities

近年来，我们的社区继续面临前所未有的挑战，包括粮食短缺、COVID-19大流行以及因气候变化而加剧的自然灾害，如干旱、飓风和火灾。为了提高社区的复原力，一个关键的挑战是了解现有弱点"在哪里"，以便及时进行政策干预和改变规划。该项目的目标是研究新的数据科学和计算技术，以利用新兴的空间和时空大数据（如地球观测数据）来识别此类脆弱性（例如，具体而言，该项目将探索混合模式的检测-一种新的模式族，侧重于数据点类型在空间和时间上的构成-这对于揭示许多脆弱性是必要的。例如，作物多样化程度低的地区容易受到单一疾病的破坏性影响，经济多样化程度低的地方容易受到单一部门供求变化的影响。如果成功，其结果将导致提高各个领域的复原力，包括农业（例如，降低粮食短缺的风险），经济（例如，减少COVID-19等干扰的影响），生态系统（例如，生物多样性，还有更多。更广泛地说，混合模式还可以帮助改进其他数据科学技术。例如，在机器学习中，错误类型的混合模式可以用于通知更好的架构设计或训练策略。提出的技术将通过GitHub上的开源包传播，并与流行的软件/工具结合，以增强研究基础设施并促进可重复研究。这项研究还将促进马里兰州大学新本科课程的开发，并帮助吸引STEM中代表性不足的群体的学生。该项目预计将导致多种数据科学和计算创新。首先，它将探索混合模式的新的抗干扰鲁棒配方（例如，新的测试统计和点过程），以允许明确控制虚假结果的比率，这在资源有限和社会影响大的现实世界应用中至关重要。其次，它将设计可扩展的计算框架来识别具有不规则形状的混合模式，以捕获具有复杂足迹的真实世界混合过程。一个独特的挑战是，本地和模式级的混合签名可以是不同的，这违反了假设的本地标准为基础的搜索范式广泛使用的聚类类型的技术。将探索新的算法设计来弥合这一差距。最后，它将研究新的时空公式，以捕捉跨空间尺度和时间序列的混合模式的非平稳性。如果成功的话，这些成果将通过新的模式家族扩展数据科学知识，并有可能通过为数据分析打开新的视角来改变相关领域的科学研究。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Our communities have continued to face unprecedented challenges in recent years, including food-shortages, the COVID-19 pandemic, and natural disasters exacerbated by climate change such as droughts, hurricanes, and fires. To improve the resilience of communities, a key challenge is to understand “where” existing weaknesses are in order to make timely policy intervention and changes in planning. The goal of this project is to investigate novel data science and computational techniques to identify such vulnerabilities using emerging spatial and spatiotemporal big data such as Earth observation data (e.g., satellite-based crop maps), urban points-of-interest, biodiversity databases, geo-tagged social media streams, etc. Specifically, this project will explore the detection of mixture patterns – a new pattern family that focuses on compositions of the types of data points in space and time – which is necessary in revealing many of the vulnerabilities. For example, regions with low crop diversification are subject to devastating impact of a single disease, and places with low economic diversification are vulnerable to changes in supply or demand for a single sector. If successful, the results will lead to improved resilience in various domains, including agriculture (e.g., lowering risks of food shortages), economy (e.g., reducing impact of disturbances such as COVID-19), ecosystems (e.g., biodiversity) and many more. More broadly, mixture patterns may also help improve other data science techniques. For example, in machine learning, mixture patterns of error types may be used to inform better architecture designs or training strategies. Proposed techniques will be disseminated via open-source packages on GitHub as well as incorporation with popular software/tools to enhance research infrastructure and promote reproducible research. This research will also facilitate development of new undergraduate courses at the University of Maryland and help engage students from underrepresented groups in STEM.This project is expected to result in multiple data science and computing innovations. First, it will explore novel statistically-robust formulations of mixture patterns (e.g., new test statistics and point processes) to allow explicit control of the rate of spurious results, which is critical in real-world applications with limited resources and high societal impact. Second, it will design scalable computational frameworks to identify mixtures patterns with irregular shapes to capture real-world mixture processes with complex footprints. A unique challenge is that local- and pattern-level mixture signatures can be different, which violates the assumption of local-criteria-based search paradigms widely used in clustering-type of techniques. New algorithmic designs will be explored to bridge this gap. Finally, it will investigate new spatiotemporal formulations to capture the non-stationarity of mixture patterns across spatial scales and time-series. If successful, the results will expand data science knowledge with new pattern families, and have the potential to transform related domain science research by opening new lenses for data analytics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.