Cross-Scale Spatiotemporal Modeling Using an Integrated Data Framework

这一研究项目将在一个单一、统一的分析框架内解决与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，州一级的社会经济变量的空间分析可能掩盖了在县或社区一级发生的地方过程。从历史上看，空间和时间分析要么单独进行，要么以松散耦合的研究设计进行。该项目将为地理空间数据的可视化和分析开发和扩展一个多尺度框架。该框架将解决数据分析中的尺度处理的基本问题，促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栖息地和夏威夷降雨模式的分析。该项目将支持研究生的参与，他们的参与将促进他们自己的专业发展。夏威夷大学和科罗拉多大学博尔德分校的合作将增加地理多样性，并在计算机科学、地球科学和空间数据科学领域增加女性和未被充分代表的少数民族的存在。该研究项目将为多尺度数据表示、建模和分析开发一个理论框架。时空数据的多尺度分析是许多学科中分析系统长期关注的问题。处理规模的问题集中体现在众所周知的可修改面积单位问题及其时间等价物上。这些问题的部分原因是传统上认为时间是线性序列，空间是扁平层的观点。该项目将对时间的二维表示--三角模型(TM)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效用，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整合到用于多尺度时空分析的5D分析框架中。将在模型和框架上开发拓扑、统计和机器学习方法，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框架中现象的时空变化、模式和关系，可以获得哪些额外的知识？2)时空数据的多尺度表示如何促进人-环境相互作用的建模？该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.