Spatial data integration is the process of making data from different sources compatible so that they can be used appropriately in an analysis framework. Before spatial data can be viewed, queried, combined, or analyzed, the integration process must address the many ways in which they can differ in terms of the following: Content: The type of geographic feature or phenomenon represented and the properties used to describe it Spatial representation: The conceptual model of space (object or field view), spatial data model (vector or raster) and spatial object type (point, line, polygon, cell), or indirect spatial reference (place name or code) used to identify location Spatial reference: The coordinate system (datum, map projection or coordinate system, and units), geographic extent, and spatial resolution of the data or the source scale of any mapped data inputs Attributes: The measurement scales, units, terminology, and classification systems Time frame: The date or data range of acquisition or last update Data/file format: The format used to encode the data, for example, GML, KML, shapefile, ascii grid, and tiff Data quality: Attribute and positional accuracy, logical consistency in the geometric and attribute data, and data completeness Lineage: The means by which the data were acquired, including instruments, calibration, sampling or enumeration strategies, protocols, and processing steps Purpose: Who collected the data, why, and what, if any, are the restrictions on its use The three types of spatial data integration tasks are temporal, horizontal, and vertical integration. [Source: Encyclopedia of Geography; Spatial Data Integration]
geographic information systems
spatial data integration
concepts