Internal and Relative Topologies for Multi-Resolution Vector Data

几十年来，对强大的基于矢量的、多尺度的地理空间数据架构的探索一直对地理信息系统和制图界提出了挑战。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。所有这三项都是进行稳健的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和相对拓扑。内部拓扑意味着(例如)流通道必须在每个分辨率级别的汇合处连接。相对拓扑保留了层之间的要素配准：一个层中的道路要素必须注册到第三层中桥要素处的另一个层中的河流要素。在多个分辨率级别上保存内部和相对拓扑结构会混淆基于地理信息系统的空间分析的许多方面：简单地说，如果要素被“合并”(如果它们没有在空间上配准)，则很难评估作为大多数地理信息系统调查基础的空间关系。该项目将实施基于金字塔的矢量数据体系结构以及检索和数据管理算法。研究的第一阶段实现了简单向量、复合向量(例如河流支流)和多层向量档案(例如道路和水文)的算法，并在一个公共领域网站上进行了演示，该网站以渐进的分辨率提供了示例向量文件。第二阶段通过在数据金字塔中嵌入不确定性度量，帮助自动确定在两种分辨率下的矢量数据表示是否实际上不同，以及独立汇编的矢量数据集在一系列分辨率上是否包含基本上相同的内容或独特的细节，来解决关于其内容和结构随分辨率而变化的地理信息的性质的核心智力问题。这项研究将通过促进访问重要的矢量数据集，例如可追溯到1790年的美国人口普查边界的历史地理信息系统数据集，使人类和自然地理学家受益。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，通过减少编辑和维护许多“单一用途”矢量数据库的成本，为特殊用途的应用量身定做。展示渐进式矢量数据交付的网站将展示金字塔体系结构如何减少超大型数据档案的交付时间。这项研究将改进当前的元数据实践，这些实践假设位置精度在整个矢量归档中是一致的。改进对比例相关数据中的内部和相对拓扑的理解将支持创建健壮的、多尺度的、拓扑正确的矢量数据库。更广泛的影响将改善在选择适当详细程度的地理信息系统数据以及整合以不同分辨率汇编的地理空间数据集方面的选择。

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.