Spatial data models Raster –exhaustive regular or irregular partitioning of space –associated with the field view –location-based Vector –points, lines, areas –associated with the object view –object-based

Spatial data models

Vector data models An object (as opposed to field or location) viewpoint of space represent geographic entities as discrete objects composed of points, lines, and polygons

Vector data models Spaghetti vector data model –each point, line, or polygon that represents a geographic entity is stored as a record in a file that consists of that entity’s ID and a list of coordinates that define it’s location (or the coordinate space that it occupies)

Vector data models Spaghetti vector data model –relationships among objects are implied

Vector data models Spaghetti vector data model –advantages simple efficient for display and plotting –disadvantages inefficient for most types of spatial analysis

Vector data models Vector topologic data model –composed of points, lines, and polygons –node: a point at the intersection of two or more lines –in addition to coordinate locations, the topologic relationships between geometric features are explicitly recorded

Vector data models Vector topologic data model

Vector data models Vector topologic data model

Vector data models Vector topologic data model: GBF/DIME

Vector data models Vector topologic data model: POLYVRT (hierarchical data structure)

Vector data models Freeman - Hoffman chain-code for compacting vector storage

Vector data model TIN: Triangulated Irregular Network

Vector data model TIN: Triangulated Irregular Network

Vector data model TIN: Triangulated Irregular Network

Vector data model Hybrid vs. integrated systems for vector data models –A hybrid system stores spatial data in one type of data model and the attribute data in another data model that is often an existing commercial non-spatial database –An integrated system manages both spatial and attribute data using the same data model

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