Re-Arranging Space, Time and Scales in GIS: Alternative Models for Multi-Scale Spatio-Temporal Modeling and Analyses
Re-Arranging Space, Time and Scales in GIS: Alternative Models for Multi-Scale Spatio-Temporal Modeling and Analyses
复制标题
重新安排 GIS 中的空间、时间和尺度：多尺度时空建模和分析的替代模型
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
N. Weghe
中科院分区：
文献类型：
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作者：
Y. Qiang;N. Weghe
空间和时间的表示是地理信息科学的基本问题。在流行的GIS和分析系统中，时间被建模为真实的数字的线性流，空间被表示为具有时间戳的平面层。尽管它们在GIS和信息可视化中占据主导地位，但这些表示对于具有复杂时空范围的数据以及多个时空尺度上的数据变化的可视化是低效的。本文介绍了将比例维度纳入时间和空间的替代表示法。本文首先回顾了三角模型（TM）这一多尺度时间序列模型的一系列研究工作。然后，介绍了金字塔模型（PM），这是空间数据的TM的扩展，并演示了PM在可视化土地覆盖数据的多尺度空间格局的实用性。最后，讨论了将TM和PM集成到一个统一的多尺度时空建模框架中的潜力。本文系统地论述了时空交替安排的模型及其在分析不同类型数据中的应用。此外，本文旨在激发重新思考的空间，时间和规模的组织在未来的GIS和分析工具的发展，以处理越来越多的时空数据的数量和复杂性。
The representations of space and time are fundamental issues in GIScience. In prevalent GIS and analytical systems, time is modeled as a linear stream of real numbers and space is represented as flat layers with timestamps. Despite their dominance in GIS and information visualization, these representations are inefficient for visualizing data with complex temporal and spatial extents and the variation of data at multiple temporal and spatial scales. This article presents alternative representations that incorporate the scale dimension into time and space. The article first reviews a series of work about the triangular model (TM), which is a multi-scale temporal model. Then, it introduces the pyramid model (PM), which is the extension of the TM for spatial data, and demonstrates the utility of the PM in visualizing multi-scale spatial patterns of land cover data. Finally, it discusses the potential of integrating the TM and the PM into a unified framework for multi-scale spatio-temporal modeling. This article systematically documents the models with alternative arrangements of space and time and their applications in analyzing different types of data. Additionally, this article aims to inspire the re-thinking of organizations of space, time, and scales in the future development of GIS and analytical tools to handle the increasing quantity and complexity of spatio-temporal data.