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单个统一的分析框架内的时空和时间管理量表相关的挑战。量表的选择是分析地理空间数据的重要问题。例如，对州一级的社会经济变量的空间分析可以掩盖在县或社区层面进行的本地流程。从历史上看，空间和时间分析已分别进行或在松散耦合的研究设计中进行。该项目将开发并扩展一个多尺度框架，以可视化和分析地理空间数据。该框架将解决数据分析中规模处理的基本问题，提高有关跨尺度时空现象的知识，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。将开发并公开开发新工具。这些工具的实用性将在案例研究中进行测试，包括对路易斯安那州沿海湿地栖息地和夏威夷降雨模式的分析。该项目将支持参与活动的研究生。夏威夷大学与科罗拉多大学博尔德分校之间的合作将增加地理多样性，以及在计算机科学，地球科学和空间数据科学领域的妇女和代表性不足的少数群体的存在。这项研究项目将开发一个多尺度的理论框架数据表示，建模和分析。时空数据的多尺度分析是许多学科中的分析系统的长期关注。处理量表的问题在众所周知的可修改的面积单位问题及其时间等效物中表现出来。这些问题部分是由于传统上将时间视为线性序列和空间作为平坦层的观点。该项目将三角模型（TM）（TM）（TM）（TM）（2D表示）的研究扩展到更高的维度模型。该项目将测试TM在分析线性空间数据，完善3D金字塔模型（PM）的概念和计算方面的实用性，以进行多尺度空间分析，并将TM和PM整合到5D分析框架中，以用于多尺度空间的分析框架 - 时空分析。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。项目中要回答的问题包括：1）通过分析TM和PM框架中现象之间的时空变化，模式和关系，可以获得哪些其他知识？ 2）时空数据的多尺度表示可以促进人类环境互动的建模？该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点评估来支持的，并具有更广泛的影响。 。

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

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

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

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