Joint Center for Intelligent Spatial Computing, College of Science, George Mason University, Fairfax, VA 22030-4444, USA.
Proc Natl Acad Sci U S A. 2011 Apr 5;108(14):5498-503. doi: 10.1073/pnas.0909315108. Epub 2011 Mar 28.
Contemporary physical science studies rely on the effective analyses of geographically dispersed spatial data and simulations of physical phenomena. Single computers and generic high-end computing are not sufficient to process the data for complex physical science analysis and simulations, which can be successfully supported only through distributed computing, best optimized through the application of spatial principles. Spatial computing, the computing aspect of a spatial cyberinfrastructure, refers to a computing paradigm that utilizes spatial principles to optimize distributed computers to catalyze advancements in the physical sciences. Spatial principles govern the interactions between scientific parameters across space and time by providing the spatial connections and constraints to drive the progression of the phenomena. Therefore, spatial computing studies could better position us to leverage spatial principles in simulating physical phenomena and, by extension, advance the physical sciences. Using geospatial science as an example, this paper illustrates through three research examples how spatial computing could (i) enable data intensive science with efficient data/services search, access, and utilization, (ii) facilitate physical science studies with enabling high-performance computing capabilities, and (iii) empower scientists with multidimensional visualization tools to understand observations and simulations. The research examples demonstrate that spatial computing is of critical importance to design computing methods to catalyze physical science studies with better data access, phenomena simulation, and analytical visualization. We envision that spatial computing will become a core technology that drives fundamental physical science advancements in the 21st century.
当代物理科学研究依赖于对地理上分散的空间数据的有效分析和物理现象的模拟。单台计算机和通用的高端计算不足以处理复杂物理科学分析和模拟所需的数据，只有通过分布式计算才能成功支持，而通过应用空间原则可以对其进行最佳优化。空间计算是空间信息基础设施的计算方面，是指一种利用空间原则来优化分布式计算机以促进物理科学发展的计算范例。空间原则通过提供空间连接和约束来驱动现象的发展，从而控制科学参数在空间和时间上的相互作用。因此，空间计算研究可以使我们更好地利用空间原则来模拟物理现象，并借此推动物理科学的发展。本文以地理空间科学为例，通过三个研究实例说明了空间计算如何（i）通过高效的数据/服务搜索、访问和利用来实现数据密集型科学，（ii）通过提供高性能计算能力来促进物理科学研究，以及（iii）通过多维可视化工具为科学家提供理解观测和模拟的能力。这些研究实例表明，空间计算对于设计计算方法以促进更好的数据访问、现象模拟和分析可视化的物理科学研究至关重要。我们设想空间计算将成为推动 21 世纪基础物理科学发展的核心技术。
