Cross-Scale Spatiotemporal Modeling Using an Integrated Data Framework

使用集成数据框架的跨尺度时空建模

• 批准号: 2102019
• 负责人: Yi Qiang
• 金额: $ 28.69万
• 依托单位: University of South Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2102019&HistoricalAwards=false
• 关键词: Cross; Scale; Spatiotemporal; Modeling; Using

This research project will address the challenges associated with managing scales in space and time within a single, unified analytic framework. The choice of scale is an important question in the analysis of geospatial data. For example, the spatial analysis of socioeconomic variables at the state level may mask local processes taking place at the county or community level. Historically, spatial and temporal analysis has proceeded either separately or in a loosely coupled research design. This project will develop and extend a multi-scale framework for the visualization and analysis of geospatial data. The framework will resolve fundamental issues of scale handling in data analytics, advance knowledge about cross-scale spatio-temporal phenomena, and aid scientists in looking more deeply into the interplay among various environmental and social processes. New tools will be developed and made publicly available. The utility of these tools will be tested in case studies, including an analysis of wetland habitats in coastal Louisiana and Hawaiian rainfall patterns. The project will support graduate students whose participation will advance their own professional development. The collaboration between the University of Hawaii and the University of Colorado at Boulder will increase geographic diversity and the presence of women and underrepresented minorities in computer science, earth science, and spatial data science.This research project will develop a theoretical framework for multi-scale data representation, modeling, and analysis. Multi-scale analysis of spatio-temporal data is a longstanding concern for analytic systems in many disciplines. The problem of handling scale is epitomized in the well-known modifiable areal unit problem and its temporal equivalent. These issues are partially due to the traditionally held views of time as a linear sequence and space as a flat layer. This project extends research on the Triangular Model (TM), a 2D representation of time, into higher dimensional models. The project will test the utility of TM in analyzing linear spatial data, refine conceptual and computational aspects of a 3D Pyramid Model (PM) for multi-scale spatial analysis, and integrate the TM and PM into an 5D analytical framework for multi-scale spatio-temporal analyses. Topologies, statistics, and machine learning methods will be developed on the models and framework to support multi-scale queries, visualization, and quantitative modeling. The questions to be answered in the project include: 1) what additional knowledge can be gained by analyzing spatio-temporal variations, patterns, and relationships of phenomena across in the TM and PM frameworks? and 2) in what ways can multi-scale representations of spatio-temporal data facilitate the modeling of human-environment interactions?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.

该研究项目将解决与在一个统一的分析框架内管理空间和时间尺度相关的挑战。比例尺的选择是地理空间数据分析中的一个重要问题。例如，在州一级对社会经济变量进行的空间分析可能掩盖了在县或社区一级发生的地方进程。从历史上看，空间和时间的分析进行了单独或松散耦合的研究设计。该项目将开发和扩展一个多尺度框架，用于地理空间数据的可视化和分析。该框架将解决数据分析中尺度处理的基本问题，推进对跨尺度时空现象的了解，并帮助科学家更深入地研究各种环境和社会过程之间的相互作用。将开发新的工具并向公众提供。这些工具的效用将在案例研究中进行测试，包括对路易斯安那州沿海湿地生境和夏威夷降雨模式的分析。该项目将支持研究生，他们的参与将促进他们自己的专业发展。夏威夷大学和位于博尔德的科罗拉多大学之间的合作将增加地理多样性以及妇女和代表性不足的少数民族在计算机科学，地球科学和空间数据science.This研究项目将开发多尺度数据表示，建模和分析的理论框架。时空数据的多尺度分析是许多学科分析系统长期关注的问题。处理规模的问题集中体现在著名的可修改的面积单位问题及其时间等效。这些问题部分是由于传统上认为时间是线性序列，空间是平面层。该项目将三角模型（TM）（时间的二维表示）的研究扩展到更高维的模型。该项目将测试TM在分析线性空间数据方面的实用性，改进用于多尺度空间分析的3D金字塔模型（PM）的概念和计算方面，并将TM和PM集成到用于多尺度时空分析的5D分析框架中。拓扑，统计和机器学习方法将在模型和框架上开发，以支持多尺度查询，可视化和定量建模。本计画所要回答的问题包括：1）在TM与PM架构中，透过分析现象的时空变异、模式与关系，可以获得哪些额外的知识？以及2）时空数据的多尺度表示在哪些方面可以促进人与环境交互的建模？该奖项反映了NSF的法定使命，并被认为是值得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的支持。

项目成果

Spatial Assessment of Community Resilience from 2012 Hurricane Sandy Using Nighttime Light

• DOI: 10.3390/rs13204128
• 发表时间: 2021-10
• 期刊: Remote. Sens.
• 作者: Jinwen Xu;Y. Qiang

Power outage and environmental justice in Winter Storm Uri: an analytical workflow based on nighttime light remote sensing

冬季风暴乌里的停电与环境正义：基于夜间灯光遥感的分析工作流程

• DOI: 10.1080/17538947.2023.2224087
• 发表时间: 2023
• 期刊: International Journal of Digital Earth
• 影响因子: 5.1
• 作者: Xu, Jinwen;Qiang, Yi;Cai, Heng;Zou, Lei
• 通讯作者: Zou, Lei

Analyzing multi-scale spatial point patterns in a pyramid modeling framework

在金字塔建模框架中分析多尺度空间点模式

• DOI: 10.1080/15230406.2022.2048419
• 发表时间: 2022
• 期刊: Cartography and Geographic Information Science
• 影响因子: 2.5
• 作者: Qiang, Yi;Buttenfield, Barbara;Xu, Jinwen
• 通讯作者: Xu, Jinwen

Analysing Information Diffusion in Natural Hazards using Retweets - a Case Study of 2018 Winter Storm Diego

使用转发分析自然灾害中的信息扩散 - 以 2018 年冬季风暴迭戈为例

• DOI: 10.1080/19475683.2021.1954086
• 发表时间: 2021
• 期刊: Annals of GIS
• 影响因子: 5
• 作者: Xu, Jinwen;Qiang, Yi
• 通讯作者: Qiang, Yi