CRII: III: Discovering Complex Mixture Patterns in Spatial Data to Advance Resilience of Communities
CRII:III:发现空间数据中的复杂混合模式以提高社区的弹性
基本信息
- 批准号:2105133
- 负责人:
- 金额:$ 17.5万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-08-01 至 2024-07-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
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.
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大流行以及干旱、飓风和火灾等因气候变化而加剧的自然灾害。为了提高社区的复原力,一项关键挑战是了解现有的弱点在哪里,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,利用新兴的空间和时空大数据,如地球观测数据(如卫星作物图)、城市兴趣点、生物多样性数据库、地理标记的社交媒体流等,识别这些漏洞。具体来说,该项目将探索混合模式的检测——一种新的模式族,专注于空间和时间中数据点类型的组合——这对于揭示许多漏洞是必要的。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区容易受到单一部门供需变化的影响。如果取得成功,其成果将提高各个领域的抵御能力,包括农业(例如,降低粮食短缺风险)、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)等。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用于告知更好的架构设计或训练策略。建议的技术将通过GitHub上的开源软件包传播,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性研究。这项研究还将促进马里兰大学新本科课程的发展,并帮助来自STEM中代表性不足群体的学生。该项目预计将导致多个数据科学和计算创新。首先,它将探索混合模式的新颖统计鲁棒公式(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高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不规则形状的混合模式,以捕获具有复杂足迹的真实混合过程。一个独特的挑战是局部和模式级混合签名可能不同,这违反了聚类技术中广泛使用的基于局部标准的搜索范式的假设。将探索新的算法设计来弥合这一差距。最后,本文将研究新的时空公式,以捕获跨空间尺度和时间序列的混合模式的非平稳性。如果成功,结果将通过新的模式族扩展数据科学知识,并有可能通过打开数据分析的新镜头来改变相关领域的科学研究。该奖项反映了美国国家科学基金会的法定使命,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。
项目成果
期刊论文数量(15)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
A Statistically-Guided Deep Network Transformation and Moderation Framework for Data with Spatial Heterogeneity
针对空间异质性数据的统计引导深度网络转换和审核框架
- DOI:10.1109/icdm51629.2021.00088
- 发表时间:2022
- 期刊:
- 影响因子:0
- 作者:Xie, Yiqun;He, Erhu;Jia, Xiaowei;Bao, Han;Zhou, Xun;Ghosh, Rahul;Ravirathinam, Praveen
- 通讯作者:Ravirathinam, Praveen
Task-Adaptive Meta-Learning Framework for Advancing Spatial Generalizability
- DOI:10.48550/arxiv.2212.06864
- 发表时间:2022-12
- 期刊:
- 影响因子:0
- 作者:Zhexiong Liu;Licheng Liu;Yiqun Xie;Zhenong Jin;X. Jia
- 通讯作者:Zhexiong Liu;Licheng Liu;Yiqun Xie;Zhenong Jin;X. Jia
Statistically-Robust Clustering Techniques for Mapping Spatial Hotspots: A Survey
- DOI:10.1145/3487893
- 发表时间:2023-03-01
- 期刊:
- 影响因子:16.6
- 作者:Xie,Yiqun;Shekhar,Shashi;Li,Yan
- 通讯作者:Li,Yan
Physics-guided Graph Diffusion Network for Combining Heterogeneous Simulated Data: An Application in Predicting Stream Water Temperature
用于组合异构模拟数据的物理引导图扩散网络:在预测溪流水温中的应用
- DOI:
- 发表时间:2023
- 期刊:
- 影响因子:0
- 作者:Jia, Xiaowei;Chen, Shengyu;Zheng, Can;Xie, Yiqun;Jiang, Zhe;Kalanat, Nasrin
- 通讯作者:Kalanat, Nasrin
Spatial-Net: A Self-Adaptive and Model-Agnostic Deep Learning Framework for Spatially Heterogeneous Datasets
Spatial-Net:用于空间异构数据集的自适应且与模型无关的深度学习框架
- DOI:10.1145/3474717.3483970
- 发表时间:2021
- 期刊:
- 影响因子:0
- 作者:Xie, Yiqun;Jia, Xiaowei;Bao, Han;Zhou, Xun;Yu, Jia;Ghosh, Rahul;Ravirathinam, Praveen
- 通讯作者:Ravirathinam, Praveen
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Yiqun Xie其他文献
User Preferences for Accessing Publically Available Turfgrass Cultivar Performance Data
访问公开草坪草品种性能数据的用户偏好
- DOI:
- 发表时间:
2019 - 期刊:
- 影响因子:1
- 作者:
Chengyan Yue;Jingjing Wang;E. Watkins;Yiqun Xie;S. Shekhar;S. Bonos;A. Patton;Kevin Morris;Kristine M. Moncada - 通讯作者:
Kristine M. Moncada
The High Photoresponse of Stress-Tuned MoTe2 Optoelectronic Devices in the Telecommunication Band
应力调谐 MoTe2 光电器件在电信频段的高光响应
- DOI:
10.1002/pssr.202200276 - 发表时间:
2022 - 期刊:
- 影响因子:0
- 作者:
GuangPing Ye;Wen Xiong;Yiqun Xie;LeLe Gong - 通讯作者:
LeLe Gong
Perfect in-plane CrI3 spin-valve driven by photogalvanic effect
- DOI:
https://doi.org/10.1103/PhysRevMaterials.5.054004 - 发表时间:
2021 - 期刊:
- 影响因子:3.4
- 作者:
Yongzhi Luo;Yiqun Xie;Juan Zhao;Yibin Hu;Xiang Ye;Sanhuang Ke - 通讯作者:
Sanhuang Ke
Referee-Meta-Learning for Fast Adaptation of Locational Fairness
用于快速适应位置公平性的裁判元学习
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Weiye Chen;Yiqun Xie;Xiaowei Jia;Erhu He;Han Bao;Bang An;Xun Zhou - 通讯作者:
Xun Zhou
Largely Enhanced Photogalvanic Effects in a Phosphorene Photodetector by Strain-Increased Device Asymmetry
通过增加应变的器件不对称性大大增强磷烯光电探测器中的光电效应
- DOI:
10.1103/physrevapplied.14.064003 - 发表时间:
2020-08 - 期刊:
- 影响因子:0
- 作者:
Juan Zhao;Yinbin Hu;Yiqun Xie;Lei Zhang;Yin Wang - 通讯作者:
Yin Wang
Yiqun Xie的其他文献
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{{ truncateString('Yiqun Xie', 18)}}的其他基金
Collaborative Research: EarthCube Capabilities: ICESpark: An Open-Source Big Data Platform for Science Discoveries in the New Arctic and Beyond
协作研究:EarthCube 功能:ICESpark:新北极及其他地区科学发现的开源大数据平台
- 批准号:
2126474 - 财政年份:2021
- 资助金额:
$ 17.5万 - 项目类别:
Standard Grant
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