Explain the importance of hierarchical data models in geospatial information systems
As the geographic reality naturally follows hierarchical structures, the hierarchical data models have been widely adopted in multiple approaches of geospatial information science and systems. These approaches include (a) a rigorous conceptual representation of real-world features to explain the human perception of the geographic space; (b) a series of indexing mechanisms for both raster and vector datasets to either accelerate their algorithmic processing, such as their retrieval from big data repositories, or compress their volume and storage requirements; (c) the modelling and retrieval of geospatial content to implement efficient mapping services over the web; and (d) the development of advanced geospatial reference frameworks to effectively support a seamless integration of heterogeneous and multi-resolution geospatial data for the whole planet. This article provides an overview of how hierarchical data models can support these areas through a series of illustrated examples. Specifically, the article explains the principles of the Tomlin’s conceptual representation model, the index structures of quadtrees and R-trees, the tile maps and related conventions of online map providers, and the discrete global grid systems as a partitioning approach to divide the Earth’s surface into a group of uniform cells at various levels of resolutions.