Internal and Relative Topologies for Multi-Resolution Vector Data

多分辨率矢量数据的内部和相关拓扑

• 批准号: 0451509
• 负责人: Barbara Buttenfield
• 金额: --
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-05-01 至 2009-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0451509&HistoricalAwards=false
• 关键词: Internal; Relative; Topologies; Multi; Resolution

The search for a robust vector-based, multi-scale geospatial data architecture has challenged GIS and cartographic communities for decades. The successful data model must generate representations at multiple resolutions that preserve line length, local coordinate density, and correct topology. All three are prerequisite to robust spatial analysis and modeling. In particular, the solution must preserve internal and relative topologies. Internal topology means that (for example) stream channels must connect at their confluence, at every level of resolution. Relative topology preserves feature registration between layers: the road feature in one layer must register to the river feature in another layer at the bridge feature in a third layer. Preservation of internal and relative topology at multiple levels of resolution confounds many aspects of GIS-based spatial analysis: simply stated, if features are "conflated" (if they do not register spatially) it becomes difficult to assess spatial relationships that underlie most GIS investigations. This project will implement a pyramid-based vector data architecture along with algorithms for retrieval and data management. The first phase of research implements algorithms for simple vectors, compound vectors (e.g., stream tributaries) and multi-layer vector archives (e.g., roads and hydrography) and demonstrates them on a public domain website that delivers example vector files at progressive resolutions. The second phase addresses core intellectual questions about the nature of geographic information whose content and structure vary with resolution, by embedding uncertainty metrics in the data pyramid that can help to automatically determine if vector data representations at two resolutions are in fact different, and if independently compiled vector datasets contain essentially the same content or unique details across a range of resolutions. The research will benefit human and physical geographers, by facilitating access to important vector datasets, as for example historical GIS datasets of US Census boundaries dating back to 1790. It will validate general purpose geospatial data products such as USGS National Map, by reducing costs of compiling and maintaining numerous 'single purpose' vector databases at resolutions tailored to special purpose applications. The website demonstrating progressive vector data delivery will show how the pyramid architecture can reduce delivery times for very large data archives. The research will refine current metadata practice that assumes positional accuracy is uniform throughout a vector archive.Improved understanding of internal and relative topology in scale-dependent data will support creation of robust, multi-scale, topologically correct vector databases. Broader impacts will improve choices about selecting GIS data at appropriate levels of detail, and about integrating geospatial data sets compiled at discrepant resolutions.

几十年来，寻找一种健壮的基于矢量的多尺度地理空间数据架构一直是GIS和制图界面临的挑战。成功的数据模型必须以多种分辨率生成表示，以保持线长度、局部坐标密度和正确的拓扑结构。所有这三个都是鲁棒空间分析和建模的先决条件。特别是，解决方案必须保留内部拓扑和相对拓扑。内部拓扑意味着（例如）流通道必须在它们的汇合处连接，在每个分辨率级别上。相对拓扑保持了层与层之间的特征配准：一层中的道路特征必须在第三层的桥梁特征上与另一层中的河流特征进行配准。在多个分辨率级别上保留内部和相对拓扑结构会混淆基于GIS的空间分析的许多方面：简单地说，如果特征被“合并”（如果它们没有在空间上注册），则很难评估大多数GIS调查的空间关系。该项目将实现一个基于金字塔的矢量数据架构以及用于检索和数据管理的算法。研究的第一阶段实现了简单矢量、复合矢量（例如，河流支流）和多层矢量档案（例如，道路和水文）的算法，并在一个公共领域网站上演示了它们，该网站以渐进分辨率提供示例矢量文件。第二阶段通过在数据金字塔中嵌入不确定性度量来解决有关地理信息的本质的核心知识问题，这些地理信息的内容和结构随分辨率而变化，这有助于自动确定两种分辨率下的矢量数据表示是否实际上不同，以及独立编译的矢量数据集是否在一系列分辨率下包含本质上相同的内容或独特的细节。这项研究将使人文地理学家和自然地理学家受益，因为它有助于获取重要的矢量数据集，例如可追溯到1790年的美国人口普查边界的历史GIS数据集。它将验证通用地理空间数据产品，如美国地质勘探局国家地图，通过降低编制和维护众多“单一用途”矢量数据库的成本，为特殊用途应用量身定制分辨率。演示渐进式矢量数据传递的网站将展示金字塔架构如何减少非常大的数据档案的传递时间。该研究将改进当前的元数据实践，假设在整个矢量存档中位置精度是统一的。提高对尺度相关数据的内部和相对拓扑的理解将有助于创建健壮的、多尺度的、拓扑正确的矢量数据库。更广泛的影响将改善选择适当详细程度的GIS数据，以及整合以不同分辨率编制的地理空间数据集的选择。